When using panel data, it is common to have observations from the same area over time. The questioner mentioned analyzing client data clustered within therapist. It seems intuitive to cluster the standard errors, but I am not sure how to decide on clustering on the country level versus the regional level. All regions are part of a country (~12 countries).

Clustered standard errors are often justified by possible correlation in modeling residuals within each cluster; while recent work suggests that this is not the precise justification behind clustering, it may be pedagogically useful. In general, the bootstrap is used in statistics as a resampling method to estimate the standard error of a statistical model.

The example below shows the bootstrap for the standard errors of the difference between the coefficients for age and education. The vce(bootstrap) option is used to bootstrap the standard errors of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls command provides an estimate of rho in the output. Rho is the intraclass correlation coefficient, which tells you the percent of variance in the dependent variable that is at the higher level of the data hierarchy.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data. Therefore, it affects the hypothesis testing.

In Stata, Newey{West standard errors for panel datasets are obtained by using the newey command. The newey command provides a convenient way to estimate the standard errors of the coefficients.

The vce(cluster clustvar) option is used to specify the variable that defines the clusters. The clusters are the groups of observations that are not independent.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The vce(cluster clustvar) option is used to specify the variable that defines the clusters. The clusters are the groups of observations that are not independent.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are correct if and only if 2. is the standard errors are so important: they are crucial in determining how many stars your table gets.

The temporary variable newid would assign a different ID Number to each resampled panel. The newid variable is used to identify the clusters in the data.

The cluster option is used to specify the clusters in the data. The clusters are the groups of observations that are not independent.

The vce(bootstrap) option is used to estimate the standard error of the coefficients. The bootstrap command can be used to estimate the standard error of the coefficients.

The xtgls estimates are more efficient and so would be preferred. The xtgls command provides an estimate of rho in the output.

The standard errors determine how accurate is your estimation. The stars matter a lot, when you are writing a paper, especially considering panel data.

The xtscc command provides a convenient way to estimate the standard errors of the coefficients. The xtscc command is used to estimate the standard errors of the coefficients for panel data.

The standard errors are so important: they are crucial in determining how many stars your table gets. The stars matter a lot, when you are writing a paper, especially considering panel data.

The


**Problem 1: Picnic Tables**
Ms. Davis's class is having a picnic with 27 students. Each picnic table can seat 6 people. How many picnic tables will the class need to seat all students and Ms. Davis?

**Problem 2: Ski Rental Discount**
Betty wants to rent penguin skis for no more than 150 fish dollars. The original rental fee is 225 fish dollars, but there's a 45% discount and an 8% tax after the discount. What is the percent difference between the original price and the special offer?

**Problem 3: Apple Purchase**
Haley buys 9 pounds of apples for $3. How many pounds of apples can she buy for $1?

**Problem 4: Maximum Value**
Find the maximum value of p = 4x + 5y subject to given constraints.

**Note:** The constraints for Problem 4 are not provided.

Multiple choice questions and answers are not available for these problems.


Research on the COVID-19 epidemic has revealed an approximately linear growth of total cases worldwide and a slow linear decrease of daily new cases and deaths in countries like the USA and Italy. This phenomenon occurs after a lockdown, where a large cluster, such as a nation, is divided into subclusters that evolve independently. The interaction between subclusters is modeled by the random outbreak time of the epidemic in each subcluster, with a probability density that varies slowly over time.

The total number of cases at a given time is the sum of many random variables, representing the number of cases in each subcluster. Assuming the outbreak times and parameters describing the epidemic in each subcluster are independent and identically distributed, the expectation of the total number of cases is proportional to the distribution function of the outbreak times. If the probability density of the outbreak times is a slowly changing function, the expectation of the total number of cases can be approximated by a linear function, with a small correction term.

The model consists of a large number of disconnected subclusters, each with its own population and parameters describing the epidemic. The number of cases in each subcluster is a random variable, and the total number of cases is the sum of these variables. The limit of the density of cases as the number of subclusters increases is the relevant dynamical variable. The model assumes that the outbreak times and parameters are independent and identically distributed, and that the correlation between subclusters decays sufficiently fast with distance.

The expected density of cases is given by a theorem, which states that for any positive time, the expected density of cases is approximately equal to the distribution function of the outbreak times, with a small correction term. The correction term represents the contribution of subclusters where the epidemic started between a certain time and the current time. The theorem is proved using the dominated convergence theorem and the weak law of large numbers.

The model also predicts that the linear behavior should occur while the epidemic is passing its peak phase, and that the behavior of the total number of cases is independent of the specific epidemic model in each cluster. The origin of the probability distribution of the outbreak times can be understood through models that take into account the spatial spreading of the epidemic.

The research used data from websites such as worldometers.info and coronavirus.jhu.edu, and the authors had discussions with other experts in the field. The authors declare that they have no competing interests and that the research was not funded by any specific grant. The article is licensed under a Creative Commons Attribution 4.0 International License.

The linear behavior of the COVID-19 epidemic can be explained by the lockdown, which divides a large cluster into subclusters that evolve independently. The interaction between subclusters is modeled by the random outbreak time of the epidemic in each subcluster, with a probability density that varies slowly over time. The model predicts that the linear behavior should occur while the epidemic is passing its peak phase, and that the behavior of the total number of cases is independent of the specific epidemic model in each cluster.

The expected density of cases is given by a theorem, which states that for any positive time, the expected density of cases is approximately equal to the distribution function of the outbreak times, with a small correction term. The correction term represents the contribution of subclusters where the epidemic started between a certain time and the current time. The theorem is proved using the dominated convergence theorem and the weak law of large numbers.

The model consists of a large number of disconnected subclusters, each with its own population and parameters describing the epidemic. The number of cases in each subcluster is a random variable, and the total number of cases is the sum of these variables. The limit of the density of cases as the number of subclusters increases is the relevant dynamical variable. The model assumes that the outbreak times and parameters are independent and identically distributed, and that the correlation between subclusters decays sufficiently fast with distance.

The research used data from websites such as worldometers.info and coronavirus.jhu.edu, and the authors had discussions with other experts in the field. The authors declare that they have no competing interests and that the research was not funded by any specific grant. The article is licensed under a Creative Commons Attribution 4.0 International License.

Key findings:
- The COVID-19 epidemic exhibits approximately linear growth of total cases worldwide and a slow linear decrease of daily new cases and deaths in countries like the USA and Italy.
- The linear behavior occurs after a lockdown, where a large cluster is divided into subclusters that evolve independently.
- The interaction between subclusters is modeled by the random outbreak time of the epidemic in each subcluster, with a probability density that varies slowly over time.
- The expected density of cases is approximately equal to the distribution function of the outbreak times, with a small correction term.
- The model predicts that the linear behavior should occur while the epidemic is passing its peak phase, and that the behavior of the total number of cases is independent of the specific epidemic model in each cluster.

Key equations:
- The expected density of cases is given by the distribution function of the outbreak times, with a small correction term.
- The correction term represents the contribution of subclusters where the epidemic started between a certain time and the current time.
- The theorem is proved using the dominated convergence theorem and the weak law of large numbers.

Key parameters:
- The probability density of the outbreak times, which varies slowly over time.
- The distribution function of the outbreak times, which represents the expected density of cases.
- The correction term, which represents the contribution of subclusters where the epidemic started between a certain time and the current time.
- The number of subclusters, which increases to infinity in the limit.
- The population and parameters describing the epidemic in each subcluster, which are random variables.

Key assumptions:
- The outbreak times and parameters are independent and identically distributed.
- The correlation between subclusters decays sufficiently fast with distance.
- The probability density of the outbreak times varies slowly over time.
- The number of subclusters increases to infinity in the limit.

Key limitations:
- The model assumes that the outbreak times and parameters are independent and identically distributed, which may not be realistic in practice.
- The model assumes that the correlation between subclusters decays sufficiently fast with distance, which may not be realistic in practice.
- The model does not take into account other factors that may affect the spread of the epidemic, such as human behavior and interventions.

Key future directions:
- To develop more realistic models that take into account the complexities of the real world.
- To estimate the parameters of the model from data and to validate the model using real-world data.
- To use the model to predict the future course of the epidemic and to evaluate the effectiveness of different interventions.
- To develop models that take into account the spatial spreading of the epidemic and the origin of the probability distribution of the outbreak times.


A student is struggling to understand how to convert decimals into mixed numbers after missing a math class and having a teacher who explains lessons poorly. The student has a test coming up and is seeking help to understand the concept.

Another user, oc_rana, responded by asking for more details about the student's trouble with converting decimals into mixed numbers. oc_rana recommended using Algebrator, a program that can solve math problems and explain each step in an easy-to-follow manner.

DoniilT also recommended Algebrator, stating that it has been helpful with various algebra topics, including function composition, least common measure, and factoring expressions. The program provides step-by-step solutions to algebra problems.

T0ng expressed interest in trying Algebrator and asked where to find the program. Outafnymintjo responded by providing a link to the software and mentioning that it comes with a satisfaction guarantee and a money-back guarantee.

The key concept discussed is converting decimals into mixed numbers, and the recommended tool for understanding this concept is Algebrator. The program is described as easy to use and effective in providing step-by-step solutions to algebra problems.

To convert decimals into mixed numbers, it is suggested that using a program like Algebrator can be helpful. The program can provide a clear and easy-to-follow explanation of the steps involved in the conversion process.

If you are having trouble with converting decimals into mixed numbers, consider trying Algebrator. The program is available for download and comes with a satisfaction guarantee.

What is the best way to convert decimals into mixed numbers?
A) Using a calculator
B) Using a program like Algebrator
C) Reading a textbook
D) Asking a teacher

The correct answer is B) Using a program like Algebrator, as it provides step-by-step solutions and explanations.

Where can you find Algebrator?
A) At a bookstore
B) At a library
C) Online at the provided software link
D) At a school

The correct answer is C) Online at the provided software link.

What guarantee comes with Algebrator?
A) A 30-day guarantee
B) A satisfaction guarantee and a money-back guarantee
C) A 1-year guarantee
D) No guarantee

The correct answer is B) A satisfaction guarantee and a money-back guarantee.


**Chapter 9 Homework Problems**

**Problem 9:**
Given data for a private closed economy:
- Real Domestic Output = DI + AE
- Exports = $20
- Imports = $30
- Private Closed Economy:
  - DI: $200, $240, $250, $280, $300, $320, $350, $360, $400, $400, $450, $440, $500, $480, $550, $520
  - AE: $250, $280, $300, $320, $350, $360, $400, $400, $450, $440, $500, $480, $550, $520
- Net Exports = Exports - Imports

**Problem 10:**
Given data for a private open economy:
- GDP: $100, $120, $200, $200, $300, $280, $400, $360, $400, $500, $440, $300, $600, $520, $200, $700, $700
- Consumption (C): $100, $120, $200, $200, $300, $280, $400, $360, $400, $500, $440, $300, $600, $520, $200, $700, $700
- Graph the consumption schedule and determine the MPC.
- Assume a lump-sum tax of $10 billion at all levels of GDP. Graph the resulting consumption schedule and compare the MPC and the multiplier with those of the pretax consumption schedule.
- Consumption after Tax:
  - GDP: $100, $200, $300, $400, $500, $600, $700
  - C: $112, $192, $272, $352, $432, $512, $592
- Equilibrium GDP is reduced by $40, consumption by $8, and the multiplier is 5. The new equilibrium GDP is $160.

**Problem 12:**
Given data for real domestic output:
- Real Domestic Output = DI + AE
- Private Closed Economy:
  - DI: $200, $250, $300, $350, $400, $450, $500, $550
  - AE: $240, $280, $320, $360, $400, $440, $480, $520
  - Exports: $20
  - Imports: $30
  - Net Exports: -$10
  - Government Expenditure: $20
  - Taxes: $20
- Open Economy:
  - DI: $200, $250, $300, $350, $400, $450, $500, $550
  - AE: $240, $280, $320, $360, $400, $440, $480, $520
- An increase in government spending of $20 increases equilibrium GDP by $100.
- If the government plans to tax and spend $20 at each level of GDP, GDP increases by the amount of the change in spending ($20).

**Problem 13:**
Given data for possible levels of employment:
- Employment (millions): 90, 100, 110, 120, 130
- Real Domestic Output: $500, $550, $600, $650, $700
- AE: $520, $560, $600, $640, $680
- Full employment: 130 million
- There will be a recessionary expenditure gap.
- Employment will be 20 million less than at full employment.
- To eliminate the recessionary AE gap, AE in column 3 must change by $20 at each level of GDP.
- The multiplier is 5.
- If the full-employment level of output is $500 billion, there will be an inflationary expenditure gap.
- The consequence is demand-pull inflation (higher prices, no increase in output).
- To eliminate the gap, AE in column 3 must change by $20 at each level of GDP.
- The multiplier is 5.


##### Finding Derivatives and Differentiability

To find **f'(4)** if **f(x) = 2√(2x)**, we'll apply the chain rule. First, rewrite **f(x)** as **2(2x)^(1/2)**. The derivative of **x^(1/2)** is **(1/2)x^(-1/2)**, so by the chain rule, **f'(x) = 2 * (1/2) * (2x)^(-1/2) * 2** = **2/√(2x)**. Then, **f'(4) = 2/√(2*4) = 2/√8 = 2/2√2 = 1/√2**.

The derivative of **f(x) = x^2** using First Principles is found by applying the limit definition of a derivative: **f'(x) = lim(h → 0) [f(x + h) - f(x)]/h**. Substituting **f(x) = x^2**, we get **f'(x) = lim(h → 0) [(x + h)^2 - x^2]/h** = **lim(h → 0) [x^2 + 2hx + h^2 - x^2]/h** = **lim(h → 0) [2hx + h^2]/h** = **lim(h → 0) 2x + h** = **2x**.

For the function **f(x,y) = ∫cos(-7t^2 - 6t - 1)dt**, finding the partial derivative with respect to **x** or **y** involves recognizing that the integral does not depend on **x** or **y** explicitly in its limits or integrand, implying the partial derivatives with respect to **x** and **y** are **0**, since the function does not vary with **x** or **y**.

The function **f(x) = |x^2 - 9|** is differentiable for all **x** except where **x^2 - 9 = 0**, which gives **x = ±3**. At these points, the absolute value function has a sharp turn, making it non-differentiable.

To find non-differentiable points for a function, look for points where the function's graph has a sharp corner or discontinuity, where the function's formula changes, or where the derivative's limit does not exist. Apply the definition of a derivative as a limit, and check for points where this limit does not exist, indicating non-differentiability.


Lesson: Graphs of Inverses of Functions Mathematics • Second Year of Secondary School
In this lesson, we will learn how to use a graph to find the inverse of a function and analyze the graphs for the inverse of a function.
Lesson Plan
Lesson Presentation
Lesson Video: 16:01
Lesson Explainer
Lesson Playlist: 01:45, 02:43, 02:42


To solve an equation, follow these steps: move all terms containing the variable to one side and all other terms to the opposite side, then simplify. For example, to solve 36x^2 = 100 for 'x', divide each side by 36 to get x^2 = 2.777777778. Take the square root of each side to find x = {-1.666666667, 1.666666667}.

Various equations can be solved using these principles, including:
- Linear equations: 12x = 4(7x + 24), x - 7 = 5x - 21, 3x - 2 = 4x + 3(6x + 8)
- Simple equations: x - 9 = 3 - 5x, 27 + 2x = 7x - 33, 6x - 4(5 - x) = 17
- Equations with fractions: 1.75d = 21, 5a + 9 = 12
- Quadratic equations: x^2 + 10x + 20 = 0, 3x^2 - 13x - 10 = 0
- Equations with multiple variables: -2x + 3y = -6.1, -x + 3y = -4.1, 8x - y = -2

Tools available for solving equations and performing mathematical operations include:
- Equations solver for equations involving one unknown
- Quadratic equations solver
- Percentage Calculator with step-by-step solutions
- Derivative calculator with step-by-step solutions
- Graphs of functions
- Factorization
- Greatest Common Factor and Least Common Multiple calculations
- System of equations solver with step-by-step solutions
- Fractions calculator with step-by-step solutions
- Theory in mathematics
- Roman numerals conversion
- Tip calculator
- Numbers as decimals, fractions, percentages converter
- More or less than questions solver

Example multiple-choice question: What is the value of x in the equation 2x + 7 = 11?
A) x = 2
B) x = 3
C) x = 4
D) x = 5

Solution: Subtract 7 from both sides to get 2x = 4, then divide by 2 to find x = 2. The correct answer is A) x = 2.


**Author** David Graser, Yavapai College, Prescott, AZ (David_Graser@yc.edu)

**Abstract**

This project investigates teacher shortages in public schools using data from the National Center for Education Statistics (NCES). Students analyze national and state data to form student-to-teacher ratios from 2000 to 2006. Initially, they attempt to model the ratio using polynomial models, but these fail to capture asymptotic behavior. Instead, students model students and teachers individually using polynomials or exponential functions and then form a ratio function. Care is taken to choose degrees consistent with expected asymptotic behavior. All students complete a common component on national data and then apply their learning to assigned state data.

**Project Details**

* Content Area: Calculus, Nonlinear Functions, Modeling
* Time Frame: 4-5 weeks with mini lectures
* Published: December 4, 2009
* Keywords: rational functions, limits, modeling

**Project Content**

The project includes:
* **Project Letter** (DOC | PDF)
* **Project Data** (Excel | PDF)
* **Scaffolding Resources**
* Technology assignments:
	+ **Scatter Plot** (DOC | PDF): Students learn to create scatter plots on graphing calculators and in Excel.
	+ **Regression Model** (DOC | PDF): Students use scatter plots to find linear and quadratic models of student-to-teacher ratio data.
	+ **Rational Model** (DOC | PDF): Students model students and teachers separately and combine functions to create a ratio function, accounting for asymptotic behavior.
	+ **Limits at Infinity** (DOC | PDF): Students compute the asymptotic behavior of their ratio function.
* Video resources:
	+ **How to Make a Scatter Plot in Excel**
	+ **Graphing a Function in Excel**
	+ **Adding Axes Titles To Your Excel Graph**
	+ **Adding a Chart Title to your Graph**
	+ **Changing a Graph in Excel**
	+ **Graphing a Function on a TI-83 (or TI-84)**
	+ **How to Make a Scatter Plot on a TI-83 (or TI-84)**
	+ **Quadratic Regression on the TI-83 (or TI-84)**
	+ **Documenting Your Solution to an Equation Using Mathtype**
	+ **Documenting the Limit of a Rational Function in Mathtype**
	+ **Use Your TI-83 (or TI-84) to Generate a Table for a Limit**

**Key Findings**

* Most states have decreasing student-to-teacher ratios, above the NEA-recommended 15:1 ratio.
* By choosing the degree of the polynomial appropriately, students can ensure their function levels off.
* Some models may lead to vertical asymptotes; in such cases, exponential models for students and teachers can be used instead.