|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
|
|
|
|
|
|
| program define cgmreg, eclass byable(onecall) sortpreserve
|
|
|
| syntax varlist(fv) [if] [in] [aweight fweight iweight pweight /], Cluster(varlist) [*]
|
|
|
| tempname bT e_chi2 e_chi2_p e_df_m e_df_r e_r2 e_rmse e_mss e_rss e_r2_a e_ll e_ll_0 e_S e_NC n rows cols
|
| xxinv running_sum Bigmat b elmat numsubs included grouplist plusminus e_predict nrows ncols c oV optmax tV
|
| b2 v2 noomit
|
| tempvar regvar clusvar resid groupvar
|
|
|
| capture which unique
|
| if _rc != 0 {
|
| di as error `"You need the unique command. You can obtain it by typing "findit unique" in Stata."'
|
| exit
|
| }
|
|
|
| marksample touse
|
| markout `touse' `cluster', strok
|
|
|
| scalar `e_NC'=wordcount("`cluster'")
|
|
|
| while ( regexm("`options'","robust")==1 ) {
|
|
|
| di " -> Removing string 'robust' from your options line: it's unnecessary as an option,"
|
| di " but it can cause problems if we leave it in."
|
| di " If some variable in your options list contains the string 'robust', you will"
|
| di " have to rename it."
|
| di
|
| local options = regexr("`options'", "robust", "")
|
|
|
| }
|
|
|
|
|
| while ( regexm("`options'","max")==1 ) {
|
|
|
| local `optmax'="max"
|
| local options = regexr("`options'", "max", "")
|
|
|
| }
|
|
|
|
|
| if "`weight'"~="" {
|
| local weight "[`weight'=`exp']"
|
| }
|
| else {
|
| local weight ""
|
| }
|
|
|
|
|
| qui regress `varlist' if `touse' `weight', `options' mse1
|
| di in green "Note: +/- means the corresponding matrix is added/subtracted"
|
| di
|
|
|
|
|
|
|
|
|
|
|
| mat `b' = e(b)
|
| local depname=e(depvar)
|
|
|
|
|
|
|
| qui predict double `resid' if `touse'==1, residual
|
| scalar `n' = e(N)
|
| local `e_predict' = e(predict)
|
| scalar `e_df_m' = e(df_m)
|
|
|
| scalar `e_df_r' = e(N)-e(df_m)-(regexm("`options'","noconstant") == 0)
|
|
|
|
|
| mat `xxinv' = e(V)
|
| mat `rows' = rowsof(e(V))
|
| scalar `nrows' = `rows'[1,1]
|
| scalar `ncols' = `nrows'
|
|
|
|
|
| mat `running_sum' = J(`nrows',`ncols',0)
|
|
|
|
|
| mat `Bigmat' = J(1,1,1)
|
|
|
|
|
| forvalues a=2/`=`e_NC'' {
|
|
|
| mat `Bigmat' = J(1,`a',0) \ ( J(2^(`a'-1)-1,1,1) , `Bigmat' ) \ (J(2^(`a'-1)-1,1,0) , `Bigmat' )
|
| mat `Bigmat'[1,1] = 1
|
|
|
| }
|
|
|
| mat colnames `Bigmat' = `cluster'
|
|
|
| scalar `numsubs' = 2^`=`e_NC'' - 1
|
| scalar `e_S' = `numsubs'
|
|
|
| forvalues s=1/`=`e_S'' {
|
|
|
| {
|
| scalar `included'=0
|
| local `grouplist'
|
| }
|
|
|
| foreach clusvar in `cluster' {
|
|
|
| mat `elmat' = `Bigmat'[`s',"`clusvar'"]
|
|
|
| if `elmat'[1,1] == 1 {
|
|
|
| scalar `included'= `included' + 1
|
| local `grouplist' "``grouplist'' `clusvar'"
|
|
|
| }
|
| }
|
|
|
|
|
|
|
| qui egen `groupvar' = group(``grouplist'') if `touse'
|
|
|
|
|
| local `plusminus' "+"
|
| if mod(`included',2)==0 {
|
| local `plusminus' "-"
|
| }
|
|
|
| sub_robust if `touse' `weight', groupvar(`groupvar') xxinv(`xxinv') plusminus(``plusminus'') resid(`resid') running_sum(`running_sum') touse(`touse')
|
| di in green "Calculating cov part for variables: ``grouplist'' (``plusminus'')"
|
| qui drop `groupvar'
|
|
|
| }
|
|
|
|
|
|
|
|
|
| if "``optmax''"=="max" {
|
| qui reg `varlist' if `touse' `weight', `options'
|
| matrix `oV'=e(V)
|
|
|
| qui reg `varlist' if `touse' `weight', `options' robust
|
| matrix `tV'=e(V)
|
|
|
| forvalues k=1(1)`=`nrows'' {
|
| if `tV'[`k',`k']>`oV'[`k',`k'] {
|
| matrix `oV'[`k',`k']=`tV'[`k',`k']
|
| }
|
| }
|
|
|
| foreach clusvar in `cluster' {
|
| qui reg `varlist' if `touse' `weight', `options' cluster(`clusvar')
|
| matrix `tV'=e(V)
|
| forvalues k=1(1)`=`nrows'' {
|
| if `tV'[`k',`k']>`oV'[`k',`k'] {
|
| matrix `oV'[`k',`k']=`tV'[`k',`k']
|
| }
|
| }
|
| }
|
| forvalues k=1(1)`=`nrows'' {
|
| if `oV'[`k',`k']>`running_sum'[`k',`k'] {
|
| matrix `running_sum'[`k',`k']=`oV'[`k',`k']
|
| }
|
| }
|
|
|
| }
|
|
|
|
|
| if wordcount("`varlist'")==1 {
|
| matrix `e_chi2'=[0]
|
| scalar `e_chi2_p' = .
|
| }
|
| else {
|
|
|
| _ms_omit_info `b'
|
| matrix `noomit' = J(1,colsof(`b'),1)-r(omit)
|
|
|
|
|
| mata: newV = select(st_matrix(st_local("running_sum")),(st_matrix(st_local("noomit"))))
|
| mata: newV = select(newV,(st_matrix(st_local("noomit")))')
|
| mata: st_matrix(st_local("v2"),newV)
|
|
|
|
|
| mata: b2 = select(st_matrix(st_local("b")),(st_matrix(st_local("noomit"))))
|
| mata: st_matrix(st_local("b2"),b2)
|
|
|
| if regexm("`options'","noconstant") == 0 mat `bT'=[I(colsof(`b2')-1),J(colsof(`b2')-1,1,0)]
|
| else mat `bT'=[I(colsof(`b2'))]
|
|
|
| matrix `e_chi2'=`b2'*`bT''*inv(`bT'*`v2'*`bT'')*`bT'*`b2''
|
| scalar `e_chi2_p' = chi2tail(`e_df_m',`e_chi2'[1,1])
|
| }
|
|
|
| scalar `e_r2' = e(r2)
|
| scalar `e_rmse' = e(rmse)
|
| scalar `e_mss' = e(mss)
|
| scalar `e_rss' = e(rss)
|
| scalar `e_r2_a' = 1 - (1-e(r2))*(e(N)-(regexm("`options'","noconstant") == 0))/(e(N)-e(df_m)-(regexm("`options'","noconstant") == 0))
|
| scalar `e_ll' = e(ll)
|
| scalar `e_ll_0' = e(ll_0)
|
|
|
|
|
| di
|
|
|
| dis " "
|
| dis in green "Regress with clustered SEs"
|
| _column (50) "Number of obs" _column(69) "=" _column(71) %8.0f in yellow `n'
|
| dis in green _column(50) "Wald chi2(" in yellow `e_df_m' in green ")" _column(69) "=" _column(71) %8.2f in yellow `e_chi2'[1,1]
|
| dis in green "Number of clustvars" _column(20) "=" _column(21) %5.0f in yellow `=`e_NC''
|
| in green _column(50) "Prob > chi2" _column(69) "=" _column(71) %8.4f in yellow `e_chi2_p'
|
| dis in green "Num cobminations" _column(20) "=" _column(21) %5.0f in yellow `=`e_S''
|
| in green _column(50) "R-squared" _column(69) "=" _column(71) %8.4f in yellow `e_r2'
|
| dis in green _column(50) "Adj R-squared" _column(69) "=" _column(71) %8.4f in yellow `e_r2_a'
|
|
|
| if "`if'"~="" di in green _column(50) "If condition" _column(69) "= `if'"
|
| if "`in'"~="" di in green _column(50) "In condition" _column(69) "= `in'"
|
| if "`weight'"~="" di in green _column(50) "Weights are" _column(69) "= `weight'"
|
|
|
| di
|
| scalar `c'=0
|
| foreach clusvar in `cluster' {
|
|
|
| scalar `c' = `c' + 1
|
| qui unique `clusvar' if `touse'
|
| di _column(50) in green "G(`clusvar')" _column(69) "=" %8.0f in yellow _result(18)
|
| tempname N_`=`c'' NM_`=`c''
|
| local `NM_`=`c''' "`clusvar'"
|
| scalar `N_`=`c''' = _result(18)
|
|
|
| }
|
| di
|
|
|
|
|
| ereturn post `b' `running_sum', e(`touse') depname(`depname')
|
|
|
| ereturn scalar N = `n'
|
| ereturn scalar df_m = `e_df_m'
|
| ereturn scalar df_r = `e_df_r'
|
| ereturn scalar chi2 = `e_chi2'[1,1]
|
| ereturn scalar chi2_p = `e_chi2_p'
|
| ereturn scalar r2 = `e_r2'
|
| ereturn scalar rmse = `e_rmse'
|
| ereturn scalar mss = `e_mss'
|
| ereturn scalar rss = `e_rss'
|
| ereturn scalar r2_a = `e_r2_a'
|
| ereturn scalar ll = `e_ll'
|
| ereturn scalar ll_0 = `e_ll_0'
|
| ereturn scalar S = `e_S'
|
| ereturn scalar NC = `e_NC'
|
| forvalues k=1(1)`=`c'' {
|
| ereturn scalar N_``NM_`k''' = `N_`k''
|
| }
|
|
|
| ereturn local title = e(title)
|
| ereturn local depvar = "`depname'"
|
| ereturn local cmd = "cgmreg"
|
| ereturn local properties = e(properties)
|
| ereturn local predict = "``e_predict''"
|
| ereturn local model = e(model)
|
| ereturn local estat_cmd = e(estat_cmd)
|
| ereturn local vcetype = e(vcetype)
|
| ereturn local clustvar = "`cluster'"
|
| ereturn local clusvar = "`cluster'"
|
|
|
|
|
|
|
| ereturn display
|
|
|
| end
|
|
|
|
|
|
|
| prog define sub_robust
|
|
|
| syntax [if] [in] [aweight fweight iweight pweight /] , groupvar(string) xxinv(string) plusminus(string) resid(string) running_sum(string) touse(string)
|
|
|
| tempname rows nrows m
|
| |
|
|
| local xxinv "`2'"
|
| local plusminus "`3'"
|
| local resid "`4'"
|
| local running_sum "`5'"
|
| local touse "`6'"
|
|
|
|
|
|
|
| if "`weight'"~="" {
|
| local weight "[`weight'=`exp']"
|
| }
|
| else {
|
| local weight ""
|
| }
|
|
|
| mat `rows' = rowsof(`xxinv')
|
| scalar `nrows' = `rows'[1,1]
|
|
|
| cap mat drop `m'
|
| mat `m' = `xxinv'
|
|
|
| if "`if'"=="" local if "if 1"
|
| else local if "`if' & `touse'"
|
|
|
| qui _robust `resid' `if' `in' `weight', v(`m') minus(`=`nrows'') cluster(`groupvar')
|
| mat `running_sum' = `running_sum' `plusminus' `m'
|
|
|
|
|
| end
|
|
|
|
|
|
|
