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*!Version 3.0.0 5Aug09 (By Jonah B. Gelbach)
*!Version 3.0.0 5Aug09 (By Jonah B. Gelbach)
*!Version 2.3.1 4Aug09 (By Jonah B. Gelbach)
*!Version 2.3.1 4Aug09 (By Jonah B. Gelbach)
*!Version 2.3.0 5Feb07 (By Jonah B. Gelbach)
*!Version 2.3.0 5Feb07 (By Jonah B. Gelbach)
*!Version 2.2.0 24Jan07 (By Jonah B. Gelbach)
*!Version 2.2.0 24Jan07 (By Jonah B. Gelbach)
*!Version 2.1.0 19Sep06 (By Jonah B. Gelbach)
*!Version 2.1.0 19Sep06 (By Jonah B. Gelbach)
*!Version 2.0.1 (By Douglas L. Miller)
*!Version 2.0.1 (By Douglas L. Miller)
*!Version 2.0.0 22May06 (By Jonah B. Gelbach)
*!Version 2.0.0 22May06 (By Jonah B. Gelbach)
*!Version 1.0.1 22May06 (By Jonah B. Gelbach)
*!Version 1.0.1 22May06 (By Jonah B. Gelbach)
*!Version 1.0.0 28Mar06 (By Jonah B. Gelbach)
*!Version 1.0.0 28Mar06 (By Jonah B. Gelbach)
*************
*************
* CHANGELOG *
* CHANGELOG *
*************
*************
*
*
* 3.0.0:
* 3.0.0:
*
*
* ## Fixed minor issue with weights in ereturn post line, added e(wtype) local macro
* ## Fixed minor issue with weights in ereturn post line, added e(wtype) local macro
* ## Added eigenvalue fix for non-psd cases
* ## Added eigenvalue fix for non-psd cases
* ## Fixed ereturn behavior for scalars & macros
* ## Fixed ereturn behavior for scalars & macros
*
*
* 2.3.1: small edit by JBG to
* 2.3.1: small edit by JBG to
*
*
* ## ensure that observations with missing cluster values are dropped
* ## ensure that observations with missing cluster values are dropped
* (this matters b/c cgmreg runs Stata's -regress- without clustering,
* (this matters b/c cgmreg runs Stata's -regress- without clustering,
* so previous behavior was to include obs with missing cluster values
* so previous behavior was to include obs with missing cluster values
* and then treat "missing" as a cluster in its own right)
* and then treat "missing" as a cluster in its own right)
*
*
* 2.3.0: medium edit by JBG to
* 2.3.0: medium edit by JBG to
*
*
* ## add treatment of if & in conditions
* ## add treatment of if & in conditions
* ## add treatment of weights
* ## add treatment of weights
*
*
* (required edit of syntax of to sub_robust subroutine, as well as adding some code on main regress line)
* (required edit of syntax of to sub_robust subroutine, as well as adding some code on main regress line)
*
*
*
*
* 2.2.0: medium edit by JBG to make sure that "robust" option doesn't get passed to regress for line where we obtain (X'X)^(-1) using mse1 option.
* 2.2.0: medium edit by JBG to make sure that "robust" option doesn't get passed to regress for line where we obtain (X'X)^(-1) using mse1 option.
* (comment: this seems like a stata bug to me -- why should stata allow you to use the robust option when the whole point is to get (X'X)^(-1)????
* (comment: this seems like a stata bug to me -- why should stata allow you to use the robust option when the whole point is to get (X'X)^(-1)????
*
*
* 2.1.0: medium edit by JBG to move from use of -tab- to -unique- (I just dumped in the text of unique.ado to address this locally)
* 2.1.0: medium edit by JBG to move from use of -tab- to -unique- (I just dumped in the text of unique.ado to address this locally)
*
*
* 2.0.1: minor edit by Doug to unabbreviate "pred"
* 2.0.1: minor edit by Doug to unabbreviate "pred"
*
*
* 2.0.0: major addition: command now handles arbitrary number of grouping vars
* 2.0.0: major addition: command now handles arbitrary number of grouping vars
* also, we now use cgmreg to calculate manually when only one clustering variable is used.
* also, we now use cgmreg to calculate manually when only one clustering variable is used.
* this feature helps show that the sub_robust routine is correct
* this feature helps show that the sub_robust routine is correct
* 1.0.1: corrected error in 1.0.0:
* 1.0.1: corrected error in 1.0.0:
* I forgot to subtract out the estimate with cluster(bothvars) when `numcvars'==2
* I forgot to subtract out the estimate with cluster(bothvars) when `numcvars'==2
*********************
*********************
* SCHEMATIC OF CODE *
* SCHEMATIC OF CODE *
*********************
*********************
/*
/*
1. Run the regression, with mse1 option (this option sets robust off and variance=1, so that resulting "cov" matrix is (X'X)^-1)
1. Run the regression, with mse1 option (this option sets robust off and variance=1, so that resulting "cov" matrix is (X'X)^-1)
2. Save some matrices, scalars, and macros from the ereturned regress output
2. Save some matrices, scalars, and macros from the ereturned regress output
3. Generate predicted residuals
3. Generate predicted residuals
4. Set up a giant matrix that has
4. Set up a giant matrix that has
* one column for every clustering variable
* one column for every clustering variable
* [(2^K) - 1] rows, where K is the number of clustering variables
* [(2^K) - 1] rows, where K is the number of clustering variables
* elements equal to either 0 or 1
* elements equal to either 0 or 1
Each row of this matrix corresponds to one subcase, so that it provides a list of clustering vars for which we will calculate the middle matrix.
Each row of this matrix corresponds to one subcase, so that it provides a list of clustering vars for which we will calculate the middle matrix.
We then add or subtract the middle matrices according to the inclusion/exclusion rule:
We then add or subtract the middle matrices according to the inclusion/exclusion rule:
* when the number of included clustering vars is odd, we add
* when the number of included clustering vars is odd, we add
* when the number of included clustering vars is even, we subtract
* when the number of included clustering vars is even, we subtract
5. We then iterate over the rows of this matrix, using egen, group() and the list of included clustering variables
5. We then iterate over the rows of this matrix, using egen, group() and the list of included clustering variables
to create a grouped var indicating an observation's membership category according to this group
to create a grouped var indicating an observation's membership category according to this group
6. We then use the sub_robust subroutine (which uses _robust) to calculate the appropriate part of the covariance matrix
6. We then use the sub_robust subroutine (which uses _robust) to calculate the appropriate part of the covariance matrix
7. The resulting part of the covariance matrix is added/subtracted to the matrix `running_mat', as given by the inc/exc rule
7. The resulting part of the covariance matrix is added/subtracted to the matrix `running_mat', as given by the inc/exc rule
8. The header in the stata output tells us
8. The header in the stata output tells us
Number of obs [Total number of included observations]
Number of obs [Total number of included observations]
Num clusvars [Number of clustering vars, i.e., dimensions]
Num clusvars [Number of clustering vars, i.e., dimensions]
Num combinations [Total number of possible combinations of the clusvars, i.e., 2^K-1]
Num combinations [Total number of possible combinations of the clusvars, i.e., 2^K-1]
Followed by a list of the number of distinct categories in each clustering variable.
Followed by a list of the number of distinct categories in each clustering variable.
9. Then the regression output appears, and we are done.
9. Then the regression output appears, and we are done.
*/
*/
program define cgmreg, eclass byable(onecall) sortpreserve
pr fixed_cgmreg, eclass byable(onecall) sortpreserve
syntax anything [if] [in] [aweight fweight iweight pweight /], /*
syntax anything [if] [in] [aweight fweight iweight pweight /], /*
*/ Cluster(string) [NOEIGenfix *]
*/ Cluster(string) [NOEIGenfix *]
*NOTE: use "NOEIGenfix" rather than "noEIGenfix" b/c we define a separate macro eigenfix below
*NOTE: use "NOEIGenfix" rather than "noEIGenfix" b/c we define a separate macro eigenfix below
*marksample code added in version 2.3.1, replacing homemade mark that happened after regress
*marksample code added in version 2.3.1, replacing homemade mark that happened after regress
marksample touse
marksample touse
markout `touse' `cluster', strok
markout `touse' `cluster', strok
local numcvars : word count `cluster'
local numcvars : word count `cluster'
di
di
while ( regexm("`options'","robust")==1 ) {
while ( regexm("`options'","robust")==1 ) {
di " -> Removing string 'robust' from your options line: it's unnecessary as an option,"
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 " 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 " If some variable in your options list contains the string 'robust', you will"
di " have to rename it."
di " have to rename it."
di
di
local options = regexr("`options'", "robust", "")
local options = regexr("`options'", "robust", "")
}
}
/* deal with weights */
/* deal with weights */
if "`weight'"~="" {
if "`weight'"~="" {
local wtype "`weight'"
local wtype "`weight'"
local weight "[`weight'=`exp']"
local weight "[`weight'=`exp']"
}
}
else {
else {
local weight ""
local weight ""
}
}
/* main regression */
/* main regression */
qui regress `anything' if `touse' `weight', `options' mse1
qui regress `anything' if `touse' `weight', `options' mse1
di "Note: +/- means the corresponding matrix is added/subtracted"
di "Note: +/- means the corresponding matrix is added/subtracted"
di
di
/*commented for version 2.3.1 fix of missing cluster values issue (we make `touse' with marksample, above)
/*commented for version 2.3.1 fix of missing cluster values issue (we make `touse' with marksample, above)
/* copy some information that regress provides */
/* copy some information that regress provides */
tempvar touse
tempvar touse
qui gen `touse' = e(sample) /* note that this will take care of zero-weight cases */
qui gen `touse' = e(sample) /* note that this will take care of zero-weight cases */
end commented for version 2.3.1 fix of missing cluster values issue
end commented for version 2.3.1 fix of missing cluster values issue
*/
*/
tempname b
tempname b
mat `b' = e(b)
mat `b' = e(b)
local depname = e(depvar)
local depname = e(depvar)
tempname N_clust N df_m F r2 mss rss r2_a ll ll_0
tempname N_clust N df_m F r2 mss rss r2_a ll ll_0
* scalar N_clust = e(N_clust)
* scalar N_clust = e(N_clust)
scalar `N' = e(N)
scalar `N' = e(N)
scalar `df_m' = e(df_m)
scalar `df_m' = e(df_m)
* scalar `df_r' = e(df_r)
* scalar `df_r' = e(df_r)
* scalar `F' = e(F)
* scalar `F' = e(F)
scalar `r2' = e(r2)
scalar `r2' = e(r2)
* scalar `rmse' = e(rmse)
* scalar `rmse' = e(rmse)
scalar `mss' = e(mss)
scalar `mss' = e(mss)
scalar `rss' = e(rss)
scalar `rss' = e(rss)
scalar `r2_a' = e(r2_a)
scalar `r2_a' = e(r2_a)
* scalar `ll' = e(ll)
* scalar `ll' = e(ll)
* scalar `ll_0' = e(ll_0)
* scalar `ll_0' = e(ll_0)
local title = e(title)
local title = e(title)
local depvar = `depname'
local depvar = `depname'
local cmd = "cgmreg"
local cmd = "cgmreg"
local properties = e(properties)
local properties = e(properties)
local predict = e(predict)
local predict = e(predict)
local model = e(model)
local model = e(model)
local estat_cmd = e(estat_cmd)
local estat_cmd = e(estat_cmd)
local vcetype = e(vcetype)
local vcetype = e(vcetype)
local clustvar = "`cluster'"
local clustvar = "`cluster'"
local clusvar = "`cluster'"
local clusvar = "`cluster'"
/* generate the residuals */
/* generate the residuals */
tempvar resid
tempvar resid
qui predict double `resid' if `touse'==1, residual
qui predict double `resid' if `touse'==1, residual
local n = e(N)
local n = e(N)
*save (x'x)^-1
*save (x'x)^-1
tempname xxinv rows
tempname xxinv rows
mat `xxinv' = e(V)
mat `xxinv' = e(V)
mat `rows' = rowsof(e(V))
mat `rows' = rowsof(e(V))
local rows = `rows'[1,1]
local rows = `rows'[1,1]
local cols = `rows' /* avoid confusion */
local cols = `rows' /* avoid confusion */
local k = e(df_m) + 1 // plus constant
di as error "[cgmreg] rows=`rows' k=`k'"
/* matrix that holds the running sum of covariance matrices as we go through clustering subsets */
/* matrix that holds the running sum of covariance matrices as we go through clustering subsets */
tempname running_sum
tempname running_sum
mat `running_sum' = J(`rows',`cols',0)
mat `running_sum' = J(`rows',`cols',0)
/* we will use a_cluster for matrix naming below as our trick to enumerate all clustering combinations */
/* we will use a_cluster for matrix naming below as our trick to enumerate all clustering combinations */
tempname Bigmat
tempname Bigmat
mat `Bigmat' = J(1,1,1)
mat `Bigmat' = J(1,1,1)
*taking inductive approach
*taking inductive approach
forvalues a=2/`numcvars' { /* inductive loop for Bigmat */
forvalues a=2/`numcvars' { /* inductive loop for Bigmat */
mat `Bigmat' = J(1,`a',0) \ ( J(2^(`a'-1)-1,1,1) , `Bigmat' ) \ (J(2^(`a'-1)-1,1,0) , `Bigmat' )
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 `Bigmat'[1,1] = 1
} /* end inductive loop for Bigmat */
} /* end inductive loop for Bigmat */
mat colnames `Bigmat' = `cluster'
mat colnames `Bigmat' = `cluster'
local numsubs = 2^`numcvars' - 1
local numsubs = 2^`numcvars' - 1
local S = `numsubs' /* for convenience below */
local S = `numsubs' /* for convenience below */
forvalues s=1/`S' { /* loop over rows of `Bigmat' */
forvalues s=1/`S' { /* loop over rows of `Bigmat' */
{ /* initializing */
{ /* initializing */
local included=0
local included=0
local grouplist
local grouplist
} /* done initializing */
} /* done initializing */
foreach clusvar in `cluster' { /* checking whether each `clusvar' is included in row `s' of `Bigmat' */
foreach clusvar in `cluster' { /* checking whether each `clusvar' is included in row `s' of `Bigmat' */
tempname element
tempname element
mat `element' = `Bigmat'[`s',"`clusvar'"]
mat `element' = `Bigmat'[`s',"`clusvar'"]
local element = `element'[1,1]
local element = `element'[1,1]
if `element' == 1 { /* add `clusvar' to grouplist if it's included in row `s' of `Bigmat' */
if `element' == 1 { /* add `clusvar' to grouplist if it's included in row `s' of `Bigmat' */
local included= `included' + 1
local included= `included' + 1
local grouplist "`grouplist' `clusvar'"
local grouplist "`grouplist' `clusvar'"
} /* end add `clusvar' to grouplist if it's included in row `s' of `Bigmat' */
} /* end add `clusvar' to grouplist if it's included in row `s' of `Bigmat' */
} /* checking whether each `clusvar' is included in row `s' of `Bigmat' */
} /* checking whether each `clusvar' is included in row `s' of `Bigmat' */
*now we use egen to create the var that groups observations by the clusvars in `grouplist'
*now we use egen to create the var that groups observations by the clusvars in `grouplist'
tempname groupvar
tempname groupvar
qui egen `groupvar' = group(`grouplist') if `touse'
qui egen `groupvar' = group(`grouplist') if `touse'
*now we get the robust estimate
*now we get the robust estimate
local plusminus "+"
local plusminus "+"
if mod(`included',2)==0 { /* even number */
if mod(`included',2)==0 { /* even number */
local plusminus "-"
local plusminus "-"
} /* end even number */
} /* end even number */
sub_robust `if' `in' `weight', groupvar(`groupvar') xxinv(`xxinv') plusminus(`plusminus') resid(`resid') running_sum(`running_sum') touse(`touse')
sub_robust `if' `in' `weight', groupvar(`groupvar') xxinv(`xxinv') plusminus(`plusminus') resid(`resid') running_sum(`running_sum') touse(`touse') k(`k')
di "Calculating cov part for variables: `grouplist' (`plusminus')"
di "Calculating cov part for variables: `grouplist' (`plusminus')"
} /* end loop over rows of `Bigmat' */
} /* end loop over rows of `Bigmat' */
*checking/fixing non-psd variance estimate
*checking/fixing non-psd variance estimate
tempname eigenvalues eigenvectors
tempname eigenvalues eigenvectors
*use mata to get eigenvalues after ensuring that variance matrix is (numerically) symmetric
*use mata to get eigenvalues after ensuring that variance matrix is (numerically) symmetric
mata {
mata {
B = st_matrix("`running_sum'")
B = st_matrix("`running_sum'")
A = makesymmetric(B)
A = makesymmetric(B)
symeigensystem(A, C=., lamda=.)
symeigensystem(A, C=., lamda=.)
st_matrix("`eigenvalues'", lamda)
st_matrix("`eigenvalues'", lamda)
st_matrix("`eigenvectors'", C)
st_matrix("`eigenvectors'", C)
}
}
local rnames : rownames `running_sum'
local rnames : rownames `running_sum'
local numcols = colsof(`running_sum')
local numcols = colsof(`running_sum')
local eigenfix "no"
local eigenfix "no"
forvalues col=1/`numcols' { /* column number loop */
forvalues col=1/`numcols' { /* column number loop */
if (`eigenvalues'[1,`col']<0) {
if (`eigenvalues'[1,`col']<0) {
if "`noeigenfix'"=="noeigenfix" {
if "`noeigenfix'"=="noeigenfix" {
di
di
di " -> NOTE: Raw estimated variance matrix was non positive semi-definite."
di " -> NOTE: Raw estimated variance matrix was non positive semi-definite."
di
di
di " Because you used the -noeigenfix- option, -cgmreg- must end."
di " Because you used the -noeigenfix- option, -cgmreg- must end."
di
di
di " See Cameron, Gelbach & Miller, "
di " See Cameron, Gelbach & Miller, "
di " 'Robust Inference with Multi-Way Clustering'."
di " 'Robust Inference with Multi-Way Clustering'."
di
di
di "Program terminated."
di "Program terminated."
di
di
exit
exit
}
}
mat `eigenvalues'[1,`col']=0
mat `eigenvalues'[1,`col']=0
local eigenfix "yes"
local eigenfix "yes"
}
}
} /* end column number loop */
} /* end column number loop */
*now reconstruct variance matrix using spectral decomposition formula (e.g., Def A.16 in Greene, 6th)
*now reconstruct variance matrix using spectral decomposition formula (e.g., Def A.16 in Greene, 6th)
tempname raw_running_sum
tempname raw_running_sum
mat `raw_running_sum' = `running_sum' /* pre eigen-fix variance matrix */
mat `raw_running_sum' = `running_sum' /* pre eigen-fix variance matrix */
mat `running_sum' = `eigenvectors'*diag(`eigenvalues')*`eigenvectors''
mat `running_sum' = `eigenvectors'*diag(`eigenvalues')*`eigenvectors''
mat rownames `running_sum' = `rnames'
mat rownames `running_sum' = `rnames'
mat colnames `running_sum' = `rnames'
mat colnames `running_sum' = `rnames'
/* end checking/fixing non-psd variance estimate */
/* end checking/fixing non-psd variance estimate */
/* final cleanup and post */
/* final cleanup and post */
di
di
di _column(50) "Number of obs = `n'"
di _column(50) "Number of obs = `n'"
di _column(50) "Num clusvars = `numcvars'"
di _column(50) "Num clusvars = `numcvars'"
di _column(50) "Num combinations = `S'"
di _column(50) "Num combinations = `S'"
if "`if'"~="" di _column(50) "If condition = `if'"
if "`if'"~="" di _column(50) "If condition = `if'"
if "`in'"~="" di _column(50) "In condition = `in'"
if "`in'"~="" di _column(50) "In condition = `in'"
if "`weight'"~="" di _column(50) "Weights are = `weight'"
if "`weight'"~="" di _column(50) "Weights are = `weight'"
di
di
local c 0
local c 0
foreach clusvar in `cluster' { /* getting num clusters by cluster var */
foreach clusvar in `cluster' { /* getting num clusters by cluster var */
local c = `c' + 1
local c = `c' + 1
qui unique `clusvar' if `touse'
qui unique `clusvar' if `touse'
di _column(50) "G(`clusvar')" _column(68) "= " _result(18)
di _column(50) "G(`clusvar')" _column(68) "= " _result(18)
local Gclusvar`c' = _result(18)
local Gclusvar`c' = _result(18)
} /* end getting num obs by cluster var */
} /* end getting num obs by cluster var */
di
di
ereturn post `b' `running_sum' , e(`touse') depname(`depname')
ereturn post `b' `running_sum' , e(`touse') depname(`depname')
ereturn display
ereturn display
*scalars
*scalars
ereturn scalar N = `N'
ereturn scalar N = `N'
ereturn scalar df_m = `df_m'
ereturn scalar df_m = `df_m'
* ereturn scalar df_r = `df_r'
* ereturn scalar df_r = `df_r'
ereturn scalar r2 = `r2'
ereturn scalar r2 = `r2'
* ereturn scalar rmse = `rmse'
* ereturn scalar rmse = `rmse'
ereturn scalar mss = `mss'
ereturn scalar mss = `mss'
ereturn scalar rss = `rss'
ereturn scalar rss = `rss'
ereturn scalar r2_a = `r2_a'
ereturn scalar r2_a = `r2_a'
local c 0
local c 0
foreach clusvar in `cluster' { /* getting num clusters by cluster var */
foreach clusvar in `cluster' { /* getting num clusters by cluster var */
local c = `c' + 1
local c = `c' + 1
ereturn scalar N_clus`c' = `Gclusvar`c''
ereturn scalar N_clus`c' = `Gclusvar`c''
ereturn scalar N_clus_`clusvar' = `Gclusvar`c''
ereturn scalar N_clus_`clusvar' = `Gclusvar`c''
}
}
*locals
*locals
ereturn local eigenfix = "`eigenfix'"
ereturn local eigenfix = "`eigenfix'"
ereturn local cmdline = "cgmreg `anything' `if' `in' `weight', cluster(`cluster')"
ereturn local cmdline = "cgmreg `anything' `if' `in' `weight', cluster(`cluster')"
ereturn local title = "Linear regression with CGM covariance estimation"
ereturn local title = "Linear regression with CGM covariance estimation"
ereturn local depvar = "`depname'"
ereturn local depvar = "`depname'"
ereturn local cmd = "cgmreg"
ereturn local cmd = "cgmreg"
ereturn local properties = "`properties'"
ereturn local properties = "`properties'"
ereturn local predict = "regres_p"
ereturn local predict = "regres_p"
ereturn local model = "ols"
ereturn local model = "ols"
ereturn local estat_cmd = "regress_estat"
ereturn local estat_cmd = "regress_estat"
ereturn local vcetype = "cgm_robust"
ereturn local vcetype = "cgm_robust"
ereturn local wtype = "`wtype'"
ereturn local wtype = "`wtype'"
ereturn local clustvar = "`cluster'"
ereturn local clustvar = "`cluster'"
ereturn local clusvar = "`cluster'"
ereturn local clusvar = "`cluster'"
*matrices
*matrices
ereturn matrix rawcovmat = `raw_running_sum'
ereturn matrix rawcovmat = `raw_running_sum'
if "`eigenfix'"=="yes" {
if "`eigenfix'"=="yes" {
di
di
di " -> NOTE: Raw estimated variance matrix was non-positive semi-definite."
di " -> NOTE: Raw estimated variance matrix was non-positive semi-definite."
di " -cgmreg- is replacing any/all negative eigenvalues with 0."
di " -cgmreg- is replacing any/all negative eigenvalues with 0."
di
di
di " See Cameron, Gelbach & Miller, "
di " See Cameron, Gelbach & Miller, "
di " 'Robust Inference with Multi-Way Clustering'."
di " 'Robust Inference with Multi-Way Clustering'."
di
di
di " Raw, non-psd covariance estimate will be available "
di " Raw, non-psd covariance estimate will be available "
di " in e(rawcovmat)."
di " in e(rawcovmat)."
di
di
di " (If you don't want this behavior, use the 'noeigenfix' option,"
di " (If you don't want this behavior, use the 'noeigenfix' option,"
di " in which case -cgmreg- will throw an error)"
di " in which case -cgmreg- will throw an error)"
di
di
di
di
}
}
end
end
prog define sub_robust
prog define sub_robust
syntax [if] [in] [aweight fweight iweight pweight /] , groupvar(string) xxinv(string) plusminus(string) resid(string) running_sum(string) touse(string)
syntax [if] [in] [aweight fweight iweight pweight /] , groupvar(string) xxinv(string) plusminus(string) resid(string) running_sum(string) touse(string) k(integer)
/*
/*
local cvar "`1'" /* cluster var, to be fed to us as argument 1 */
local cvar "`1'" /* cluster var, to be fed to us as argument 1 */
local xxinv "`2'" /* xxinv estimate, to be fed to us as argument 2 */
local xxinv "`2'" /* xxinv estimate, to be fed to us as argument 2 */
local plusminus "`3'" /* whether to add or subtract to `running_sum', argument 3 */
local plusminus "`3'" /* whether to add or subtract to `running_sum', argument 3 */
local resid "`4'" /* name of tempvar with resids in it, arg 4 */
local resid "`4'" /* name of tempvar with resids in it, arg 4 */
local running_sum "`5'" /* running_sum estimate, to be fed to us as argument 5 */
local running_sum "`5'" /* running_sum estimate, to be fed to us as argument 5 */
local touse "`6'"
local touse "`6'"
*/
*/
/* deal with weights */
/* deal with weights */
if "`weight'"~="" {
if "`weight'"~="" {
local weight "[`weight'=`exp']"
local weight "[`weight'=`exp']"
}
}
else {
else {
local weight ""
local weight ""
}
}
tempname rows
tempname rows
mat `rows' = rowsof(`xxinv')
*mat `rows' = rowsof(`xxinv')
local rows = `rows'[1,1]
*local rows = `rows'[1,1]
local rows = `k'
cap mat drop `m'
cap mat drop `m'
tempname m
tempname m
mat `m' = `xxinv'
mat `m' = `xxinv'
if "`if'"=="" local if "if 1"
if "`if'"=="" local if "if 1"
else local if "`if' & `touse'"
else local if "`if' & `touse'"
qui _robust `resid' `if' `in' `weight', v(`m') minus(`rows') cluster(`groupvar')
qui _robust `resid' `if' `in' `weight', v(`m') minus(`rows') cluster(`groupvar')
mat `running_sum' = `running_sum' `plusminus' `m'
mat `running_sum' = `running_sum' `plusminus' `m'
* mat li `running_sum'
* mat li `running_sum'
end
end
*end program sub_robust
*end program sub_robust
*! version 1.1 mh 15/4/98 arb 20/8/98
*! version 1.1 mh 15/4/98 arb 20/8/98
*got this from http://fmwww.bc.edu/repec/bocode/u/unique.ado
*got this from http://fmwww.bc.edu/repec/bocode/u/unique.ado
program define unique
pr unique
local options "BY(string) GENerate(string) Detail"
local options "BY(string) GENerate(string) Detail"
local varlist "req ex min(1)"
local varlist "req ex min(1)"
local if "opt"
local if "opt"
local in "opt"
local in "opt"
parse "`*'"
parse "`*'"
tempvar uniq recnum count touse
tempvar uniq recnum count touse
local sort : sortedby
local sort : sortedby
mark `touse' `if' `in'
mark `touse' `if' `in'
qui gen `recnum' = _n
qui gen long `recnum' = _n
sort `varlist'
sort `varlist'
summ `touse', meanonly
summ `touse', meanonly
local N = _result(18)
local N = _result(18)
sort `varlist' `touse'
sort `varlist' `touse'
qui by `varlist': gen byte `uniq' = (`touse' & _n==_N)
qui by `varlist': gen byte `uniq' = (`touse' & _n==_N)
qui summ `uniq'
qui summ `uniq'
di in gr "Number of unique values of `varlist' is " in ye _result(18)
di in gr "Number of unique values of `varlist' is " in ye _result(18)
di in gr "Number of records is "in ye "`N'"
di in gr "Number of records is "in ye "`N'"
if "`detail'" != "" {
if "`detail'" != "" {
sort `by' `varlist' `touse'
sort `by' `varlist' `touse'
qui by `by' `varlist' `touse': gen int `count' = _N if _n == 1
qui by `by' `varlist' `touse': gen long `count' = _N if _n == 1
label var `count' "Records per `varlist'"
label var `count' "Records per `varlist'"
if "`by'" == "" {
if "`by'" == "" {
summ `count' if `touse', d
summ `count' if `touse', d
}
}
else {
else {
by `by': summ `count' if `touse', d
by `by': summ `count' if `touse', d
}
}
}
}
if "`by'" !="" {
if "`by'" !="" {
if "`generate'"=="" {
if "`generate'"=="" {
cap drop _Unique
cap drop _Unique
local generat _Unique
local generat _Unique
}
}
else {
else {
confirm new var `generate'
confirm new var `generate'
}
}
drop `uniq'
drop `uniq'
sort `by' `varlist' `touse'
sort `by' `varlist' `touse'
qui by `by' `varlist': gen byte `uniq' = (`touse' & _n==_N)
qui by `by' `varlist': gen byte `uniq' = (`touse' & _n==_N)
qui by `by': replace `uniq' = sum(`uniq')
qui by `by': replace `uniq' = sum(`uniq')
qui by `by': gen `generate' = `uniq'[_N] if _n==1
qui by `by': gen long `generate' = `uniq'[_N] if _n==1
di in blu "variable `generate' contains number of unique values of `varlist' by `by'"
di in blu "variable `generate' contains number of unique values of `varlist' by `by'"
list `by' `generate' if `generate'!=., noobs nodisplay
list `by' `generate' if `generate'!=., noobs nodisplay
}
}
sort `sort' `recnum'
sort `sort' `recnum'
end
end