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;