| {smcl} | |
| {* *! version 1.2 09-OCT-2022}{...} | |
| {viewerjumpto "Syntax" "binslogit##syntax"}{...} | |
| {viewerjumpto "Description" "binslogit##description"}{...} | |
| {viewerjumpto "Options" "binslogit##options"}{...} | |
| {viewerjumpto "Examples" "binslogit##examples"}{...} | |
| {viewerjumpto "Stored results" "binslogit##stored_results"}{...} | |
| {viewerjumpto "References" "binslogit##references"}{...} | |
| {viewerjumpto "Authors" "binslogit##authors"}{...} | |
| {cmd:help binslogit} | |
| {hline} | |
| {title:Title} | |
| {p 4 8}{hi:binslogit} {hline 2} Data-Driven Binscatter Logit Estimation with Robust Inference Procedures and Plots.{p_end} | |
| {marker syntax}{...} | |
| {title:Syntax} | |
| {p 4 14} {cmdab:binslogit} {depvar} {it:indvar} [{it:othercovs}] {ifin} {weight} [ {cmd:,} {opt deriv(v)} {opt at(position)} {opt nolink}{p_end} | |
| {p 14 14} {opt dots(dotsopt)} {opt dotsgrid(dotsgridoption)} {opt dotsplotopt(dotsoption)}{p_end} | |
| {p 14 14} {opt line(lineopt)} {opt linegrid(#)} {opt lineplotopt(lineoption)}{p_end} | |
| {p 14 14} {opt ci(ciopt)} {opt cigrid(cigridoption)} {opt ciplotopt(rcapoption)}{p_end} | |
| {p 14 14} {opt cb(cbopt)} {opt cbgrid(#)} {opt cbplotopt(rareaoption)}{p_end} | |
| {p 14 14} {opt polyreg(p)} {opt polyreggrid(#)} {opt polyregcigrid(#)} {opt polyregplotopt(lineoption)}{p_end} | |
| {p 14 14} {opth by(varname)} {cmd:bycolors(}{it:{help colorstyle}list}{cmd:)} {cmd:bysymbols(}{it:{help symbolstyle}list}{cmd:)} {cmd:bylpatterns(}{it:{help linepatternstyle}list}{cmd:)}{p_end} | |
| {p 14 14} {opt nbins(nbinsopt)} {opt binspos(position)} {opt binsmethod(method)} {opt nbinsrot(#)} {opt samebinsby} {opt randcut(#)}{p_end} | |
| {p 14 14} {cmd:pselect(}{it:{help numlist}}{cmd:)} {cmd:sselect(}{it:{help numlist}}{cmd:)}{p_end} | |
| {p 14 14} {opt nsims(#)} {opt simsgrid(#)} {opt simsseed(seed)}{p_end} | |
| {p 14 14} {opt dfcheck(n1 n2)} {opt masspoints(masspointsoption)}{p_end} | |
| {p 14 14} {cmd:vce(}{it:{help vcetype}}{cmd:)} {opt asyvar(on/off)}{p_end} | |
| {p 14 14} {opt level(level)} {opt logitopt(logit_option)} {opt usegtools(on/off)} {opt noplot} {opt savedata(filename)} {opt replace}{p_end} | |
| {p 14 14} {opt plotxrange(min max)} {opt plotyrange(min max)} {it:{help twoway_options}} ]{p_end} | |
| {p 4 8} where {depvar} is the dependent variable, {it:indvar} is the independent variable for binning, and {it:othercovs} are other covariates to be controlled for.{p_end} | |
| {p 4 8} The degree of the piecewise polynomial p, the number of smoothness constraints s, and the derivative order v are integers | |
| satisfying 0 <= s,v <= p, which can take different values in each case.{p_end} | |
| {p 4 8} {opt fweight}s and {opt pweight}s are allowed; see {help weight}.{p_end} | |
| {marker description}{...} | |
| {title:Description} | |
| {p 4 8} {cmd:binslogit} implements binscatter logit estimation with robust inference procedures and plots, following the results in | |
| {browse "https://nppackages.github.io/references/Cattaneo-Crump-Farrell-Feng_2022_Binscatter.pdf":Cattaneo, Crump, Farrell and Feng (2022a)}. | |
| Binscatter provides a flexible way of describing the mean relationship between two variables, after possibly adjusting for other covariates, based on partitioning/binning of the independent variable of interest. | |
| The main purpose of this command is to generate binned scatter plots with curve estimation with robust pointwise confidence intervals and uniform confidence band. | |
| If the binning scheme is not set by the user, the companion command {help binsregselect:binsregselect} is used to implement binscatter | |
| in a data-driven way. | |
| Hypothesis testing for parametric specifications of and shape restrictions on the regression function can be conducted via the | |
| companion command {help binstest:binstest}. Hypothesis testing for pairwise group comparisons can be conducted via the | |
| companion command {help binspwc: binspwc}. Binscatter estimation based on the least squares method can be conducted via the command {help binsreg: binsreg}. | |
| {p_end} | |
| {p 4 8} A detailed introduction to this command is given in | |
| {browse "https://nppackages.github.io/references/Cattaneo-Crump-Farrell-Feng_2022_Stata.pdf":Cattaneo, Crump, Farrell and Feng (2022b)}. | |
| Companion R and Python packages with the same capabilities are available (see website below). | |
| {p_end} | |
| {p 4 8} Companion commands: {help binstest:binstest} for hypothesis testing of parametric specifications and shape restrictions, | |
| {help binspwc:binspwc} for hypothesis testing for pairwise group comparisons, | |
| and {help binsregselect:binsregselect} for data-driven binning selection. | |
| {p_end} | |
| {p 4 8} Related Stata, R and Python packages are available in the following website:{p_end} | |
| {p 8 8} {browse "https://nppackages.github.io/":https://nppackages.github.io/}{p_end} | |
| {marker options}{...} | |
| {title:Options} | |
| {dlgtab:Estimand} | |
| {p 4 8} {opt deriv(v)} specifies the derivative order of the regression function for estimation and plotting. | |
| The default is {cmd:deriv(0)}, which corresponds to the function itself. | |
| {p_end} | |
| {p 4 8} {opt at(position)} specifies the values of {it:othercovs} at which the estimated function is evaluated for plotting. | |
| The default is {cmd:at(mean)}, which corresponds to the mean of {it:othercovs}. Other options are: {cmd:at(median)} for the median of {it:othercovs}, | |
| {cmd:at(0)} for zeros, and {cmd:at(filename)} for particular values of {it:othercovs} saved in another file. | |
| {p_end} | |
| {p 4 8} Note: When {cmd:at(mean)} or {cmd:at(median)} is specified, all factor variables in {it:othercovs} (if specified) are excluded from the evaluation (set as zero). | |
| {p_end} | |
| {p 4 8}{opt nolink} specifies that the function within the inverse link (logistic) function be reported instead of the conditional probability function. | |
| {p_end} | |
| {dlgtab:Dots} | |
| {p 4 8} {opt dots(dotsopt)} sets the degree of polynomial and the number of smoothness for point estimation and plotting as "dots". | |
| If {cmd:dots(p s)} is specified, a piecewise polynomial of degree {it:p} with {it:s} smoothness constraints is used. | |
| The default is {cmd:dots(0 0)}, which corresponds to piecewise constant (canonical binscatter). | |
| If {cmd:dots(T)} is specified, the default {cmd:dots(0 0)} is used unless the degree {it:p} and smoothness {it:s} selection | |
| is requested via the option {cmd:pselect()} (see more details in the explanation of {cmd:pselect()}). | |
| If {cmd:dots(F)} is specified, the dots are not included in the plot. | |
| {p_end} | |
| {p 4 8} {opt dotsgrid(dotsgridoption)} specifies the number and location of dots within each bin to be plotted. | |
| Two options are available: {it:mean} and a {it:numeric} non-negative integer. | |
| The option {opt dotsgrid(mean)} adds the sample average of {it:indvar} within each bin to the grid of evaluation points. | |
| The option {opt dotsgrid(#)} adds {it:#} number of evenly-spaced points to the grid of evaluation points for each bin. | |
| Both options can be used simultaneously: for example, {opt dotsgrid(mean 5)} generates six evaluation points | |
| within each bin containing the sample mean of {it:indvar} within each bin and five evenly-spaced points. | |
| Given this choice, the dots are point estimates evaluated over the selected grid within each bin. | |
| The default is {opt dotsgrid(mean)}, which corresponds to one dot per bin evaluated at the sample average of {it:indvar} within each bin (canonical binscatter). | |
| {p_end} | |
| {p 4 8} {opt dotsplotopt(dotsoption)} standard graphs options to be passed on to the {help twoway:twoway} command to modify the appearance of the plotted dots. | |
| {p_end} | |
| {dlgtab:Line} | |
| {p 4 8} {opt line(lineopt)} sets the degree of polynomial and the number of smoothness constraints | |
| for plotting as a "line". If {cmd:line(p s)} is specified, a piecewise polynomial of | |
| degree {it:p} with {it:s} smoothness constraints is used. | |
| If {cmd:line(T)} is specified, {cmd:line(0 0)} is used unless the degree {it:p} and smoothness {it:s} selection | |
| is requested via the option {cmd:pselect()} (see more details in the explanation of {cmd:pselect()}). | |
| If {cmd:line(F)} or {cmd:line()} is specified, the line is not included in the plot. | |
| The default is {cmd:line()}. | |
| {p_end} | |
| {p 4 8} {opt linegrid(#)} specifies the number of evaluation points of an evenly-spaced grid within | |
| each bin used for evaluation of the point estimate set by the {cmd:line(p s)} option. | |
| The default is {cmd:linegrid(20)}, which corresponds to 20 evenly-spaced evaluation points within each bin for fitting/plotting the line. | |
| {p_end} | |
| {p 4 8} {opt lineplotopt(lineoption)} standard graphs options to be passed on to | |
| the {help twoway:twoway} command to modify the appearance of the plotted line. | |
| {p_end} | |
| {dlgtab:Confidence Intervals} | |
| {p 4 8} {opt ci(ciopt)} specifies the degree of polynomial and the number of smoothness constraints | |
| for constructing confidence intervals. If {cmd:ci(p s)} is specified, a piecewise polynomial of | |
| degree {it:p} with {it:s} smoothness constraints is used. | |
| If {cmd:ci(T)} is specified, {cmd:ci(1 1)} is used unless the degree {it:p} and smoothness {it:s} selection | |
| is requested via the option {cmd:pselect()} (see more details in the explanation of {cmd:pselect()}). | |
| If {cmd:ci(F)} or {cmd:ci()} is specified, the confidence intervals are not included in the plot. | |
| The default is {cmd:ci()}. | |
| {p_end} | |
| {p 4 8} {opt cigrid(cigridoption)} specifies the number and location of evaluation points in the grid | |
| used to construct the confidence intervals set by the {opt ci(p s)} option. | |
| Two options are available: {it:mean} and a {it:numeric} non-negative integer. | |
| The option {opt cigrid(mean)} adds the sample average of {it:indvar} within each bin to the grid of evaluation points. | |
| The option {opt cigrid(#)} adds {it:#} number of evenly-spaced points to the grid of evaluation points for each bin. | |
| Both options can be used simultaneously: for example, {opt cigrid(mean 5)} generates six evaluation points within each bin containing the sample mean of {it:indvar} within each bin and five evenly-spaced points. | |
| The default is {opt cigrid(mean)}, which corresponds to one evaluation point set at the sample average of {it:indvar} within each bin for confidence interval construction. | |
| {p_end} | |
| {p 4 8} {opt ciplotopt(rcapoption)} standard graphs options to be passed on to the {help twoway:twoway} command to modify the appearance of the confidence intervals. | |
| {p_end} | |
| {dlgtab:Confidence Band} | |
| {p 4 8} {opt cb(cbopt)} specifies the degree of polynomial and the number of smoothness constraints | |
| for constructing the confidence band. If {cmd:cb(p s)} is specified, a piecewise polynomial of | |
| degree {it:p} with {it:s} smoothness constraints is used. | |
| If the option {cmd:cb(T)} is specified, {cmd:cb(1 1)} is used unless the degree {it:p} and smoothness {it:s} selection | |
| is requested via the option {cmd:pselect()} (see more details in the explanation of {cmd:pselect()}). | |
| If {cmd:cb(F)} or {cmd:cb()} is specified, the confidence band is not included in the plot. | |
| The default is {cmd:cb()}. | |
| {p_end} | |
| {p 4 8} {opt cbgrid(#)} specifies the number of evaluation points of an evenly-spaced grid within each bin | |
| used for evaluation of the point estimate set by the {cmd:cb(p s)} option. | |
| The default is {cmd:cbgrid(20)}, which corresponds to 20 evenly-spaced evaluation points within each bin for confidence band construction. | |
| {p_end} | |
| {p 4 8} {opt cbplotopt(rareaoption)} standard graphs options to be passed on to | |
| the {help twoway:twoway} command to modify the appearance of the confidence band. | |
| {p_end} | |
| {dlgtab:Global Polynomial Regression} | |
| {p 4 8} {opt polyreg(p)} sets the degree {it:p} of a global polynomial regression model for plotting. | |
| By default, this fit is not included in the plot unless explicitly specified. | |
| Recommended specification is {cmd:polyreg(3)}, which adds a cubic polynomial fit of the regression function of interest to the binned scatter plot. | |
| {p_end} | |
| {p 4 8} {opt polyreggrid(#)} specifies the number of evaluation points of an evenly-spaced grid | |
| within each bin used for evaluation of the point estimate set by the {cmd:polyreg(p)} option. | |
| The default is {cmd:polyreggrid(20)}, which corresponds to 20 evenly-spaced evaluation points within each bin for confidence interval construction. | |
| {p_end} | |
| {p 4 8} {opt polyregcigrid(#)} specifies the number of evaluation points of an evenly-spaced grid within each bin used for constructing confidence intervals based on polynomial regression set by the {cmd:polyreg(p)} option. | |
| The default is {cmd:polyregcigrid(0)}, which corresponds to not plotting confidence intervals for the global polynomial regression approximation. | |
| {p_end} | |
| {p 4 8} {opt polyregplotopt(lineoption)} standard graphs options to be passed on to the {help twoway:twoway} command to modify the appearance of the global polynomial regression fit. | |
| {p_end} | |
| {dlgtab:Subgroup Analysis} | |
| {p 4 8} {opt by(varname)} specifies the variable containing the group indicator to perform subgroup analysis; | |
| both numeric and string variables are supported. | |
| When {opt by(varname)} is specified, {cmdab:binslogit} implements estimation and inference for each subgroup separately, | |
| but produces a common binned scatter plot. | |
| By default, the binning structure is selected for each subgroup separately, | |
| but see the option {cmd:samebinsby} below for imposing a common binning structure across subgroups. | |
| {p_end} | |
| {p 4 8} {cmd:bycolors(}{it:{help colorstyle}list}{cmd:)} specifies an ordered list of colors | |
| for plotting each subgroup series defined by the option {opt by()}. | |
| {p_end} | |
| {p 4 8} {cmd:bysymbols(}{it:{help symbolstyle}list}{cmd:)} specifies an ordered list of symbols | |
| for plotting each subgroup series defined by the option {opt by()}. | |
| {p_end} | |
| {p 4 8} {cmd:bylpatterns(}{it:{help linepatternstyle}list}{cmd:)} specifies an ordered list of line patterns | |
| for plotting each subgroup series defined by the option {opt by()}. | |
| {p_end} | |
| {dlgtab:Binning/Degree/Smoothness Selection} | |
| {p 4 8} {opt nbins(nbinsopt)} sets the number of bins for partitioning/binning of {it:indvar}. | |
| If {cmd:nbins(T)} or {cmd:nbins()} (default) is specified, the number of bins is selected via the companion command {help binsregselect:binsregselect} | |
| in a data-driven, optimal way whenever possible. If a {help numlist:numlist} with more than one number is specified, | |
| the number of bins is selected within this list via the companion command {help binsregselect:binsregselect}. | |
| {p_end} | |
| {p 4 8} {opt binspos(position)} specifies the position of binning knots. | |
| The default is {cmd:binspos(qs)}, which corresponds to quantile-spaced binning (canonical binscatter). | |
| Other options are: {cmd:es} for evenly-spaced binning, or a {help numlist} for manual specification of | |
| the positions of inner knots (which must be within the range of {it:indvar}). | |
| {p_end} | |
| {p 4 8} {opt binsmethod(method)} specifies the method for data-driven selection of the number of bins via the companion command {help binsregselect:binsregselect}. | |
| The default is {cmd:binsmethod(dpi)}, which corresponds to the IMSE-optimal direct plug-in rule. | |
| The other option is: {cmd:rot} for rule of thumb implementation. | |
| {p_end} | |
| {p 4 8} {opt nbinsrot(#)} specifies an initial number of bins value used to construct the DPI number of bins selector. | |
| If not specified, the data-driven ROT selector is used instead. | |
| {p_end} | |
| {p 4 8} {opt samebinsby} forces a common partitioning/binning structure across all subgroups specified by the option {cmd:by()}. | |
| The knots positions are selected according to the option {cmd:binspos()} and using the full sample. | |
| If {cmd:nbins()} is not specified, then the number of bins is selected via the companion command | |
| {help binsregselect:binsregselect} and using the full sample. | |
| {p_end} | |
| {p 4 8} {opt randcut(#)} specifies the upper bound on a uniformly distributed variable used to draw a subsample | |
| for bins/degree/smoothness selection. | |
| Observations for which {cmd:runiform()<=#} are used. # must be between 0 and 1. | |
| By default, max(5,000, 0.01n) observations are used if the samples size n>5,000. | |
| {p_end} | |
| {p 4 8} {opt pselect(numlist)} specifies a list of numbers within which the degree of polynomial {it:p} for | |
| point estimation is selected. Piecewise polynomials of the selected optimal degree {it:p} | |
| are used to construct dots or line if {cmd:dots(T)} or {cmd:line(T)} is specified, | |
| whereas piecewise polynomials of degree {it:p+1} are used to construct confidence intervals | |
| or confidence band if {cmd:ci(T)} or {cmd:cb(T)} is specified. | |
| {p_end} | |
| {p 4 8} {opt sselect(numlist)} specifies a list of numbers within which | |
| the number of smoothness constraints {it:s} | |
| for point estimation. Piecewise polynomials with the selected optimal | |
| {it:s} smoothness constraints are used to construct dots or line | |
| if {cmd:dots(T)} or {cmd:line(T)} is specified, | |
| whereas piecewise polynomials with {it:s+1} constraints are used to construct | |
| confidence intervals or confidence band if {cmd:ci(T)} or {cmd:cb(T)} is specified. | |
| If not specified, for each value {it:p} supplied in the | |
| option {cmd:pselect()}, only the piecewise polynomial with the maximum smoothness is considered, i.e., {it:s=p}. | |
| {p_end} | |
| {p 4 8} Note: To implement the degree or smoothness selection, in addition to {cmd:pselect()} | |
| or {cmd:sselect()}, {cmd:nbins(#)} must be specified. | |
| {p_end} | |
| {dlgtab:Simulation} | |
| {p 4 8} {opt nsims(#)} specifies the number of random draws for constructing confidence bands. | |
| The default is {cmd:nsims(500)}, which corresponds to 500 draws from a standard Gaussian random vector of size [(p+1)*J - (J-1)*s]. | |
| A large number of random draws is recommended to obtain the final results. | |
| {p_end} | |
| {p 4 8} {opt simsgrid(#)} specifies the number of evaluation points of an evenly-spaced grid | |
| within each bin used for evaluation of the supremum operation needed to construct confidence bands. | |
| The default is {cmd:simsgrid(20)}, which corresponds to 20 evenly-spaced evaluation points within each bin | |
| for approximating the supremum operator. | |
| A large number of evaluation points is recommended to obtain the final results. | |
| {p_end} | |
| {p 4 8} {opt simsseed(#)} sets the seed for simulations. | |
| {p_end} | |
| {dlgtab:Mass Points and Degrees of Freedom} | |
| {p 4 8} {opt dfcheck(n1 n2)} sets cutoff values for minimum effective sample size checks, | |
| which take into account the number of unique values of {it:indvar} (i.e., adjusting for the number of mass points), | |
| number of clusters, and degrees of freedom of the different statistical models considered. | |
| The default is {cmd:dfcheck(20 30)}. See Cattaneo, Crump, Farrell and Feng (2022b) for more details. | |
| {p_end} | |
| {p 4 8} {opt masspoints(masspointsoption)} specifies how mass points in {it:indvar} are handled. | |
| By default, all mass point and degrees of freedom checks are implemented. | |
| Available options: | |
| {p_end} | |
| {p 8 8} {opt masspoints(noadjust)} omits mass point checks and the corresponding effective sample size adjustments.{p_end} | |
| {p 8 8} {opt masspoints(nolocalcheck)} omits within-bin mass point and degrees of freedom checks.{p_end} | |
| {p 8 8} {opt masspoints(off)} sets {opt masspoints(noadjust)} and {opt masspoints(nolocalcheck)} simultaneously.{p_end} | |
| {p 8 8} {opt masspoints(veryfew)} forces the command to proceed as if {it:indvar} has only a few number of mass points (i.e., distinct values). | |
| In other words, forces the command to proceed as if the mass point and degrees of freedom checks were failed.{p_end} | |
| {dlgtab:Standard Error} | |
| {p 4 8} {cmd:vce(}{it:{help vcetype}}{cmd:)} specifies the {it:vcetype} for variance estimation used by | |
| the command {help logit##options:logit}. | |
| The default is {cmd:vce(robust)}. | |
| {p_end} | |
| {p 4 8} {opt asyvar(on/off)} specifies the method used to compute standard errors. | |
| If {cmd:asyvar(on)} is specified, the standard error of the nonparametric component is used and the uncertainty related to other control variables {it:othercovs} is omitted. | |
| Default is {cmd:asyvar(off)}, that is, the uncertainty related to {it:othercovs} is taken into account. | |
| {p_end} | |
| {dlgtab:Other Options} | |
| {p 4 8} {opt level(#)} sets the nominal confidence level for confidence interval and confidence band estimation. Default is {cmd:level(95)}. | |
| {p_end} | |
| {p 4 8} {opt logitopt(logit_option)} options to be passed on to the command {help logit##options:logit}. | |
| For example, options that control for the optimization process can be added here. | |
| {p_end} | |
| {p 4 8}{opt usegtools(on/off)} forces the use of several commands in the community-distributed Stata package {cmd:gtools} to speed the computation up, if {it:on} is specified. | |
| Default is {cmd:usegtools(off)}. | |
| {p_end} | |
| {p 4 8} For more information about the package {cmd:gtools}, please see {browse "https://gtools.readthedocs.io/en/latest/index.html":https://gtools.readthedocs.io/en/latest/index.html}. | |
| {p_end} | |
| {p 4 8} {opt noplot} omits binscatter plotting. | |
| {p_end} | |
| {p 4 8} {opt savedata(filename)} specifies a filename for saving all data underlying the binscatter plot (and more). | |
| {p_end} | |
| {p 4 8} {opt replace} overwrites the existing file when saving the graph data. | |
| {p_end} | |
| {p 4 8} {opt plotxrange(min max)} specifies the range of the x-axis for plotting. Observations outside the range are dropped in the plot.{p_end} | |
| {p 4 8} {opt plotyrange(min max)} specifies the range of the y-axis for plotting. Observations outside the range are dropped in the plot.{p_end} | |
| {p 4 8} {it:{help twoway_options}} any unrecognized options are appended to the end of the twoway command generating the binned scatter plot. | |
| {p_end} | |
| {marker examples}{...} | |
| {title:Examples} | |
| {p 4 8} Setup{p_end} | |
| {p 8 8} . {stata sysuse auto}{p_end} | |
| {p 4 8} Run a binscatter logit regression and report the plot{p_end} | |
| {p 8 8} . {stata binslogit foreign weight mpg}{p_end} | |
| {p 4 8} Add confidence intervals and confidence band{p_end} | |
| {p 8 8} . {stata binslogit foreign weight mpg, ci(1 1) nbins(5)}{p_end} | |
| {marker stored_results}{...} | |
| {title:Stored results} | |
| {synoptset 17 tabbed}{...} | |
| {p2col 5 17 21 2: Scalars}{p_end} | |
| {synopt:{cmd:e(N)}}number of observations{p_end} | |
| {synopt:{cmd:e(level)}}confidence level{p_end} | |
| {synopt:{cmd:e(dots_p)}}degree of polynomial for dots{p_end} | |
| {synopt:{cmd:e(dots_s)}}smoothness of polynomial for dots{p_end} | |
| {synopt:{cmd:e(line_p)}}degree of polynomial for line{p_end} | |
| {synopt:{cmd:e(line_s)}}smoothness of polynomial for line{p_end} | |
| {synopt:{cmd:e(ci_p)}}degree of polynomial for confidence interval{p_end} | |
| {synopt:{cmd:e(ci_s)}}smoothness of polynomial for confidence interval{p_end} | |
| {synopt:{cmd:e(cb_p)}}degree of polynomial for confidence band{p_end} | |
| {synopt:{cmd:e(cb_s)}}smoothness of polynomial for confidence band{p_end} | |
| {p2col 5 17 21 2: Matrices}{p_end} | |
| {synopt:{cmd:e(N_by)}}number of observations for each group{p_end} | |
| {synopt:{cmd:e(Ndist_by)}}number of distinct values for each group{p_end} | |
| {synopt:{cmd:e(Nclust_by)}}number of clusters for each group{p_end} | |
| {synopt:{cmd:e(nbins_by)}}number of bins for each group{p_end} | |
| {synopt:{cmd:e(cval_by)}}critical value for each group, used for confidence bands{p_end} | |
| {synopt:{cmd:e(imse_var_rot)}}variance constant in IMSE, ROT selection{p_end} | |
| {synopt:{cmd:e(imse_bsq_rot)}}bias constant in IMSE, ROT selection{p_end} | |
| {synopt:{cmd:e(imse_var_dpi)}}variance constant in IMSE, DPI selection{p_end} | |
| {synopt:{cmd:e(imse_bsq_dpi)}}bias constant in IMSE, DPI selection{p_end} | |
| {marker references}{...} | |
| {title:References} | |
| {p 4 8} Cattaneo, M. D., R. K. Crump, M. H. Farrell, and Y. Feng. 2022a. | |
| {browse "https://nppackages.github.io/references/Cattaneo-Crump-Farrell-Feng_2022_Binscatter.pdf":On Binscatter}. | |
| {it:arXiv:1902.09608}. | |
| {p_end} | |
| {p 4 8} Cattaneo, M. D., R. K. Crump, M. H. Farrell, and Y. Feng. 2022b. | |
| {browse "https://nppackages.github.io/references/Cattaneo-Crump-Farrell-Feng_2022_Stata.pdf":Binscatter Regressions}. | |
| {it:arXiv:1902.09615}. | |
| {p_end} | |
| {marker authors}{...} | |
| {title:Authors} | |
| {p 4 8} Matias D. Cattaneo, Princeton University, Princeton, NJ. | |
| {browse "mailto:cattaneo@princeton.edu":cattaneo@princeton.edu}. | |
| {p_end} | |
| {p 4 8} Richard K. Crump, Federal Reserve Band of New York, New York, NY. | |
| {browse "mailto:richard.crump@ny.frb.org":richard.crump@ny.frb.org}. | |
| {p_end} | |
| {p 4 8} Max H. Farrell, University of Chicago, Chicago, IL. | |
| {browse "mailto:max.farrell@chicagobooth.edu":max.farrell@chicagobooth.edu}. | |
| {p_end} | |
| {p 4 8} Yingjie Feng, Tsinghua University, Beijing, China. | |
| {browse "mailto:fengyingjiepku@gmail.com":fengyingjiepku@gmail.com}. | |
| {p_end} | |