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雷达卡
我现在要做zero-inflated poisson的回归，stata命令是 zip varlist1, inflate (varlist2)
然后在回归结果中，我想展示firm-year two dimension cluster 的T值。
因为没有找到现成的ado, 我想在ols回归的firm-year two dimension cluster 的ado文件，-cluster2- 的基础上编写ado，
我觉得我应该修改的部分就是红色的部分，但是现在有个问题就是 不知道 inflate 应该如何写入其中，请高手帮忙看一下，谢谢啦！
-cluster2- 的 ado ,如下
* cluster2.ado ---- written by Mitchell Petersen -- March 2006
* Program calculates clustered standard errors in both a firm and time dimension
* as described by Thompson in "A Simple Formula for Standard Errors that Cluster by Both Firm and Time" and
* and Cameron, Gelbach, and Miller, 2006, "Robust Inference with Multi-way Clustering"
* Additions and edits
* Compliant with outreg -- June, 2007 (Jingling Guan) [eclass]
* Checks for multiple observations per fcluster-tcluster -- June, 2007 (Jingling Guan)
* this is test of update of file date
#delimit ;
program define cluster2, eclass sortpreserve byable(recall);
syntax [varlist] [in] [if], fcluster(varname) tcluster(varname);
tokenize `varlist';
marksample touse;
local depv `"`1'"';
* ---------------------------------------------------------------- ;
* ----- Regression Clustering by First Variable (e.g. Firm) ------ ;
* ---------------------------------------------------------------- ;
quietly reg `varlist' if `touse', robust cluster(`fcluster');
matrix vcf = e(V);
local nfcluster=e(N_clust);
* ---------------------------------------------------------------- ;
* ----- Regression Clustering by Second Variable (e.g. Time) ----- ;
* ---------------------------------------------------------------- ;
quietly reg `varlist' if `touse', robust cluster(`tcluster');
matrix vct = e(V);
local ntcluster=e(N_clust);
* ---------------------------------------------------------------- ;
* --------------- Regression with "No Clustering" -------------- ;
* ---------------------------------------------------------------- ;
capture confirm string variable `fcluster';
if !_rc {;
gen bc1 = `fcluster'; /* string variable */
};
else {;
gen bc1 = string(`fcluster'); /* numeric */
};
capture confirm string variable `tcluster';
if !_rc {;
gen bc2 = `tcluster'; /* string variable */
};
else {;
gen bc2 = string(`tcluster'); /* numeric */
};
gen bc3 = bc1 + "_" + bc2;
* --------------------------------------------------------- ;
* Check for multiple observations per fcluster-tcluster ;
* --------------------------------------------------------- ;
bysort bc3: gen unique_obs = _n==1; * =1 if only one obs per fcluster-tcluster;
qui sum unique_obs;
if r(mean)==1 {;
quietly reg `varlist' if `touse', robust;
local mcluster=0;
};
else {;
quietly reg `varlist' if `touse', robust cluster(bc3);
local mcluster =1 ;
};
drop bc1 bc2 bc3 unique_obs;
local nparm = e(df_m)+1;
matrix coef = e(b);
matrix vc = vcf+vct-e(V);
* ---------------------------------------------------------------- ;
* ----------------- Print out Regression Results ----------------- ;
* ---------------------------------------------------------------- ;
tokenize `varlist'; /* this puts varlist in to the macros `1' `2' etc */
macro shift; /* drops first arguement (dep var) and shifts the rest up one */
dis " ";
dis in green "Linear regression with 2D clustered SEs"
_column (56) "Number of obs = " %7.0f in yellow e(N);
dis in green _column(56) "F(" %3.0f e(df_m) "," %6.0f e(df_r) ") =" %8.2f in yellow e(F);
dis in green _column(56) "Prob > F =" %8.4f in yellow 1-F(e(df_m),e(df_r),e(F));
dis in green "Number of clusters (`fcluster') = " _column(31) %5.0f in yellow $_nfcluster
in green _column(56) "R-squared =" %8.4f in yellow e(r2);
dis in green "Number of clusters (`tcluster') = " _column(31) %5.0f in yellow $_ntcluster
in green _column(56) "Root MSE =" %8.4f in yellow e(rmse);
* ---------------------------------------------------------------- ;
* ----------------- upload Regression Results into e()------------ ;
* ---------------------------------------------------------------- ;
* save statistics from the last regression (clustered by fcluster+tcluster);
* scalars;
scalar e_N=e(N);
scalar e_df_m = e(df_m);
scalar e_df_r = e(df_r);
scalar e_F = e(F);
scalar e_r2 = e(r2);
scalar e_rmse = e(rmse);
scalar e_mss = e(mss);
scalar e_rss = e(rss);
scalar e_r2_a = e(r2_a);
scalar e_ll = e(ll);
scalar e_ll_0 = e(ll_0);
* prepare matrices to upload into e();
ereturn clear;
tempname b V;
matrix `b' = coef;
matrix `V' = vc;
* post the resuls in e();
ereturn post `b' `V';
ereturn scalar N = e_N;
ereturn scalar df_m = e_df_m;
ereturn scalar df_r = e_df_r;
ereturn scalar F= e_F;
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 local title "Linear regression with clustered SEs";
ereturn local method "2-dimension clustered SEs";
ereturn local depvar "`depv'";
ereturn local cmd "cluster2";
* end of uploading;
* ==================================================================;
* display coefficients and se;
ereturn display;
dis " ";
if $_mcluster==1 {;
dis " SE clustered by " "`fcluster'" " and " "`tcluster'" " (multiple obs per " "`fcluster'" "-" "`tcluster'" ")";
};
else {;
dis " SE clustered by " "`fcluster'" " and " "`tcluster'";
};
dis " ";
scalar drop e_N e_df_m e_df_r e_F e_r2 e_rmse e_mss e_rss e_r2_a e_ll e_ll_0;
matrix drop coef vc vcf vct;
end;
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