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submitted JASA data/Bryan Graham Stata software/dgp_cross_section_v2_rep100.g

@@ -0,0 +1,192 @@
+/*
+Title: Generating data from a treatment effect model in which 1) coefficient depends on spacial location; 2)The treatment selection depends on covariates. 
+Author: Ke Yang
+date created: 3/1/2017
+last change:  4/13/2018 
+ The selection process for the treatment group. The treatment group is selected based on a funciton of covariates; see Freeman and Berk, 2012  
+Note:This program generates data using the following model: 
+The location variable, X=[x1,x2], both of which follow a positive-half-normal distribution; 
+Y = f0(x) + f1(x).DB 2 + u; with var(u)=1. There are n subjects.
+DB = (a + b.z + V > 0 ) where V is a r.v. following N(0,1). 
+*/
+
+new;
+library pgraph; 
+
+/*Section 1: Defines the parameter values in the DGP & estimation procedure*/
+
+rep=100;
+wind=0.15; /*This is the window size used in gwr_knn() estimation*/
+bandk=2;   /*This is the bandwidth used in K()*/
+
+@mux=0; mean of x@
+/*varx={0.08 0.17};  variance of x*/
+varx={14042 18883};
+nj={600 300}; /* 600 number of individuals, n*/
+var={100}; /*variance of error * 100 */
+cor=0;   /*correlation of errors terms from the same individual*/
+roh=0;   /*spatial dependence*/
+nei= 3; /* number of neighborhoods */
+periods=20;
+
+
+
+/*Section 2: DGP cycle starts here*/
+
+/*cyCount=1;*/
+
+ cyc_nj=1;
+   do while cyc_nj<=cols(nj);
+
+         cyc_var=1;
+         do while cyc_var<=cols(var);
+
+
+
+                 field = 1; 
+                 prec = 0; 
+                 fmat = "%*.*lf";  
+                 index_nj = ftos(nj[cyc_nj],fmat,field,prec); 
+                 index_var= ftos(var[cyc_var],fmat,field,prec);
+                 index_rep= ftos(rep,fmat,field,prec);
+
+                 index=index_nj$+index_var$+index_rep; 
+                 outfile="C:/Users/ke/GoogleDrive/keyang/research/vancouvor_housing/mc/cross_section_data"$+index$+".txt";
+                 output file = ^outfile reset;
+
+                 /*Section 2: Defines the vectors to store the regression results*/
+                 d=zeros(nj[cyc_nj]*rep,8); 
+                 fgwr=zeros(nj[cyc_nj]*rep, 7);
+                 fols=zeros(rep,7);
+                 colname= zeros(5,4*cols(nj)*cols(var)*cols(cor)*cols(roh)); 
+
+                 rep_cyc=1; 
+                 do while rep_cyc<=rep;
+
+                    /*read parameter values in this cycle*/
+                    nc=nj[cyc_nj]; /*sample size = nj*/
+                    varc=var[cyc_var]/100; /*variance of the error term*/
+                    corc=cor/100; /*correlations for observations in the same panel*/
+                    rohc=roh/100; /*spacial AR parameter*/
+
+                    /*x follows a half normal (right) distribution; Then we truncate the data outside of 2 stdev above the mean
+                    x=rndn(nc,2); 
+                    x=abs(x); /
+                    flag1=(x[.,1].>2);
+                    flag2=(x[.,2].>2);
+                    x[.,1]=x[.,1]-flag1*2;
+                    x[.,2]=x[.,2]-flag2*2;
+                    */
+                    /*x=x.*varx; */
+
+                    x=rndu(nc, 2).*2;
+
+                    y=zeros(nc,1); 
+                    nhood=ones(nc/nei,1).*.seqa(1,1,nei); /* a category variable that resembles different neiborhoods */ 
+                    times=ones(nc/periods,1).*.seqa(1,1,periods);/* a category variable that resembles different time periods */
+                    imr=rndn(nc,1)*3;
+                    /*Dtreat=(rndn(nc,1)+nhood.*sin(-x)+imr.*cos(-x)+0.3).gt 0;  treatment dummy: Dtreat = (error + b1.nhood + b2.imr + V > 0) */ 
+                    /*Dtreat=(rndn(nc,1)+nhood.*(0.25*x[.,1].^2-0.5*x[.,2])+imr.*(-0.5*x[.,1]+x[.,2])).gt 0;*/ 
+                    /*Dtreat=(rndn(nc,1)+imr.*(-0.5*x[.,1]+x[.,2])).gt 0;*/
+                    /*Dtreat=sqrt((x[.,1]).^2+(x[.,2]).^2).le 0.78;   Dummy based on distance from the center: 0.78 for 30%; 1.12 for 50%; 1.4 for 70%*/
+                    Dtreat=(x[.,1]-1+0.25*x[.,2]-.25).gt 0;
+                    Dtime=times.>=11;
+
+                    dist=zeros(nc,nc);/*matrix gives distances between units*/
+                     i=1;
+                     do while i<=nc;/*print ((x[i,1]-x[.,1]').^2+(x[i,2]-x[.,2]').^2)'~sqrt((x[i,1]-x[.,1]').^2+(x[i,2]-x[.,2]').^2)';stop;*/
+                       dis=sqrt((x[i,1]-x[.,1]').^2+(x[i,2]-x[.,2]').^2)/(stdc(sqrt((x[i,1]-x[.,1]').^2+(x[i,2]-x[.,2]').^2)'));
+                       dist[i,.]=inv(sqrt(2*pi))*exp(-0.5.*dis.^2/1.5); /*gaussion weight*/
+                       i=i+1;
+                     endo;
+                    /*dist=diagrv(dist,zeros(nc,1)); replace diaganal element with zero*/
+                     
+
+                    /*error process: alpha-unit specific error with spatial dependence*/
+
+                    alpha=rndn(nc,1)*sqrt(corc*varc); 
+                    epsilon=alpha+rndn(nc,1)*sqrt((1-corc)*varc);/*one-way error, grouped by unit*/
+                    alpha=rohc*dist*alpha;/*first order spatial autoregressive process*/
+                     
+                    
+                    /*regression model with difference-in-differnce structure */
+                    /*y=(x^2-2*x)+sin(6*x).*Dtreat +epsilon/8;    */
+                    /*y=(3-x) .*Dtreat + epsilon/8; */
+                    /*trueATE=10*inv(2*pi)*exp(-0.5*(x[.,1].^2+x[.,2].^2)); */
+                    trueATE=1/5*(-x[.,1].^0.3-x[.,2]^0.3);
+                    y=trueATE.*Dtreat.*Dtime + 0.2*imr +epsilon/8;
+                                        
+                    d[nc*(rep_cyc-1)+1:nc*rep_cyc,.] = y~x~Dtreat~Dtime~imr~epsilon/8~trueATE;    /*data matrix to be generated  */
+
+                    /*Estimate all four parameter functions with gwr_knn() procedure GWR-"" OLS-""*/
+                    /*{proj_y, bgwr,weits,Lmat}=gwr_knn(y, Dpost~Dtreat~(Dtreat.*Dpost) ,(x-x'),zeros(njc,njc), wind*njc,wind*njc);*/ /*using zeros(nj,nj) for year_dt to ignore that weight scheme*/
+                    /*{proj_y, bgwr}=gwr(y, dpost~Dtreat~(Dtreat.*Dpost)~nhood~imr, (x-x'), zeros(njc, njc), (maxc(x)-minc(x))/8, (maxc(x)-minc(x))/8);
+                    fgwr[nJc*(rep_cyc-1)+1:nJc*rep_cyc,.]=bgwr;*/
+                    /*
+                    {nam,m,bols,stb,vc,std,sig,cx,rsq,resid,dbw } = ols(0,y,Dpost~Dtreat~(Dtreat.*Dpost~nhood~imr));
+                    fols[rep_cyc,.]= bols';
+                    */
+                rep_cyc=rep_cyc+1;
+                endo;
+/*
+                colname[1,CyCount]=nj[cycle_nj];
+                colname[2,cyCount]=var[cycle_var]/100;
+                colname[3,CyCount]=cor[cycle_cor]/100;
+                colname[4,CyCount]=roh[cycle_roh]/100;
+
+                cyCount=cyCount+1;
+
+*/
+@------------------DATA CHECK----------------------------------------------
+dev=eye(nJ[cycle_nJ]/j[cycle_J]).*.(eye(J[cycle_J])-ones(J[cycle_J],1)*ones(J[cycle_J],1)'./J[cycle_J]); 
+ahat=(1/(nJ[cycle_nJ]-nJ[cycle_nJ]/J[cycle_J]))*(dev*(y-mx))'*(dev*(y-mx));
+bhat=(1/nJ[cycle_nJ])*(y-mx-dev*(y-mx))'*(y-mx-dev*(y-mx))-ahat/J[cycle_J];
+print "variance of epsilon"  (1-cor[cycle_cor]/100)*var[cycle_var]/100 "variance of alpha" var[cycle_var]*cor[cycle_cor]/10000;
+print "variance of epsilon" ahat  "variance of alpha" bhat;
+--------------------------------------------------------------------------@
+
+                 
+         cyc_var=cyc_var+1;
+         endo;
+       
+       cyc_nj=cyc_nj+1;
+       endo;
+         
+/*print colname|bgwr;*/
+
+print "y~g1~g2~Dtreat~Dtime~x~epsilon~trueATE";
+print d;
+
+
+stop;
+print " ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++";
+print "Kernel weight using stdev as bandwidth";
+print dist;
+
+stop;
+
+print "average bias & ase";
+truf=(d[.,2].^2-2.*d[.,2])~sin(6*d[.,2])~sin(-d[.,2])~cos(-d[.,2])~ones(rows(d),1)~0.5*ones(rows(d),1);
+print (meanc(fgwr-truf))'|(meanc((fgwr-truf).^2))';
+/*
+print (bols-meanc(truf))';
+print (meanc((ones(rows(d),1)*bols'-truf).^2))';
+*/
+
+@--------------GRAPHS------------------------------------------------------@
+z1=x~y~(x^2-2*x)~sin(6*x)~sin(-x)~cos(-x)~ones(rows(d),1)~0.5*ones(rows(d),1);
+z1=sortc(z1,1);
+
+/*print "x~y~bgwr~(x^2-2*x)~sin(6*x)~sin(-x)~cos(-x)";print z1;*/
+
+library pgraph;
+graphset;
+_pdate="";
+ylabel("fgwr(x) and y");
+xlabel("x");
+title("Regression");
+_pltype = {6 };
+_plctrl = { -1,0};
+xy(z1[.,1],z1[., 8 14]);
+
+

submitted JASA data/Bryan Graham Stata software/dgp_cross_section_v2_rep500.g

@@ -0,0 +1,193 @@
+/*
+Title: Generating data from a treatment effect model in which 1) coefficient depends on spacial location; 2)The treatment selection depends on covariates. 
+Author: Ke Yang
+date created: 3/1/2017
+last change:  4/13/2018 
+ The selection process for the treatment group. The treatment group is selected based on a funciton of covariates; see Freeman and Berk, 2012  
+Note:This program generates data using the following model: 
+The location variable, X=[x1,x2], both of which follow a positive-half-normal distribution; 
+Y = f0(x) + f1(x).DB 2 + u; with var(u)=1. There are n subjects.
+DB = (a + b.z + V > 0 ) where V is a r.v. following N(0,1). 
+*/
+
+new;
+library pgraph; 
+
+/*Section 1: Defines the parameter values in the DGP & estimation procedure*/
+
+rep=500;
+wind=0.15; /*This is the window size used in gwr_knn() estimation*/
+bandk=2;   /*This is the bandwidth used in K()*/
+
+@mux=0; mean of x@
+/*varx={0.08 0.17};  variance of x*/
+varx={14042 18883};
+nj={600 300}; /* 600 number of individuals, n*/
+var={100}; /*variance of error * 100 */
+cor=0;   /*correlation of errors terms from the same individual*/
+roh=0;   /*spatial dependence*/
+nei= 3; /* number of neighborhoods */
+periods=20;
+
+
+
+/*Section 2: DGP cycle starts here*/
+
+/*cyCount=1;*/
+
+ cyc_nj=1;
+   do while cyc_nj<=cols(nj);
+
+         cyc_var=1;
+         do while cyc_var<=cols(var);
+
+
+
+                 field = 1; 
+                 prec = 0; 
+                 fmat = "%*.*lf";  
+                 index_nj = ftos(nj[cyc_nj],fmat,field,prec); 
+                 index_var= ftos(var[cyc_var],fmat,field,prec);
+                 index_rep= ftos(rep,fmat,field,prec);
+
+                 index=index_nj$+index_var$+index_rep; 
+                 outfile="C:/Users/ke/GoogleDrive/keyang/research/vancouvor_housing/mc/cross_section_data"$+index$+".txt";
+                 output file = ^outfile reset;
+
+                 /*Section 2: Defines the vectors to store the regression results*/
+                 d=zeros(nj[cyc_nj]*rep,8); 
+                 fgwr=zeros(nj[cyc_nj]*rep, 7);
+                 fols=zeros(rep,7);
+                 colname= zeros(5,4*cols(nj)*cols(var)*cols(cor)*cols(roh)); 
+
+                 rep_cyc=1; 
+                 do while rep_cyc<=rep;
+
+                    /*read parameter values in this cycle*/
+                    nc=nj[cyc_nj]; /*sample size = nj*/
+                    varc=var[cyc_var]/100; /*variance of the error term*/
+                    corc=cor/100; /*correlations for observations in the same panel*/
+                    rohc=roh/100; /*spacial AR parameter*/
+
+                    /*x follows a half normal (right) distribution; Then we truncate the data outside of 2 stdev above the mean
+                    x=rndn(nc,2); 
+                    x=abs(x); /
+                    flag1=(x[.,1].>2);
+                    flag2=(x[.,2].>2);
+                    x[.,1]=x[.,1]-flag1*2;
+                    x[.,2]=x[.,2]-flag2*2;
+                    */
+                    /*x=x.*varx; */
+
+                    x=rndu(nc, 2).*2;
+
+                    y=zeros(nc,1); 
+                    nhood=ones(nc/nei,1).*.seqa(1,1,nei); /* a category variable that resembles different neiborhoods */ 
+                    times=ones(nc/periods,1).*.seqa(1,1,periods);/* a category variable that resembles different time periods */
+                    imr=rndn(nc,1)*3;
+                    /*Dtreat=(rndn(nc,1)+nhood.*sin(-x)+imr.*cos(-x)+0.3).gt 0;  treatment dummy: Dtreat = (error + b1.nhood + b2.imr + V > 0) */ 
+                    /*Dtreat=(rndn(nc,1)+nhood.*(0.25*x[.,1].^2-0.5*x[.,2])+imr.*(-0.5*x[.,1]+x[.,2])).gt 0;*/ 
+                    /*Dtreat=(rndn(nc,1)+imr.*(-0.5*x[.,1]+x[.,2])).gt 0;*/
+                    /*Dtreat=sqrt((x[.,1]).^2+(x[.,2]).^2).le 0.78;   Dummy based on distance from the center: 0.78 for 30%; 1.12 for 50%; 1.4 for 70%*/
+                    Dtreat=(x[.,1]-1+0.25*x[.,2]-.25).gt 0;
+                    Dtime=times.>=11;
+
+                    dist=zeros(nc,nc);/*matrix gives distances between units*/
+                     i=1;
+                     do while i<=nc;/*print ((x[i,1]-x[.,1]').^2+(x[i,2]-x[.,2]').^2)'~sqrt((x[i,1]-x[.,1]').^2+(x[i,2]-x[.,2]').^2)';stop;*/
+                       dis=sqrt((x[i,1]-x[.,1]').^2+(x[i,2]-x[.,2]').^2)/(stdc(sqrt((x[i,1]-x[.,1]').^2+(x[i,2]-x[.,2]').^2)'));
+                       dist[i,.]=inv(sqrt(2*pi))*exp(-0.5.*dis.^2/1.5); /*gaussion weight*/
+                       i=i+1;
+                     endo;
+                    /*dist=diagrv(dist,zeros(nc,1)); replace diaganal element with zero*/
+                     
+
+                    /*error process: alpha-unit specific error with spatial dependence*/
+
+                    alpha=rndn(nc,1)*sqrt(corc*varc); 
+                    epsilon=alpha+rndn(nc,1)*sqrt((1-corc)*varc);/*one-way error, grouped by unit*/
+                    alpha=rohc*dist*alpha;/*first order spatial autoregressive process*/
+                     
+                    
+                    /*regression model with difference-in-differnce structure */
+                    /*y=(x^2-2*x)+sin(6*x).*Dtreat +epsilon/8;    */
+                    /*y=(3-x) .*Dtreat + epsilon/8; */
+                    /*trueATE=10*inv(2*pi)*exp(-0.5*(x[.,1].^2+x[.,2].^2)); */
+                    trueATE=1/5*(-x[.,1].^0.3-x[.,2]^0.3);
+                    y=trueATE.*Dtreat.*Dtime + 0.2*imr +epsilon/8;
+                                        
+                    d[nc*(rep_cyc-1)+1:nc*rep_cyc,.] = y~x~Dtreat~Dtime~imr~epsilon/8~trueATE;    /*data matrix to be generated  */
+
+                    /*Estimate all four parameter functions with gwr_knn() procedure GWR-"" OLS-""*/
+                    /*{proj_y, bgwr,weits,Lmat}=gwr_knn(y, Dpost~Dtreat~(Dtreat.*Dpost) ,(x-x'),zeros(njc,njc), wind*njc,wind*njc);*/ /*using zeros(nj,nj) for year_dt to ignore that weight scheme*/
+                    /*{proj_y, bgwr}=gwr(y, dpost~Dtreat~(Dtreat.*Dpost)~nhood~imr, (x-x'), zeros(njc, njc), (maxc(x)-minc(x))/8, (maxc(x)-minc(x))/8);
+                    fgwr[nJc*(rep_cyc-1)+1:nJc*rep_cyc,.]=bgwr;*/
+                    /*
+                    {nam,m,bols,stb,vc,std,sig,cx,rsq,resid,dbw } = ols(0,y,Dpost~Dtreat~(Dtreat.*Dpost~nhood~imr));
+                    fols[rep_cyc,.]= bols';
+                    */
+                rep_cyc=rep_cyc+1;
+                endo;
+/*
+                colname[1,CyCount]=nj[cycle_nj];
+                colname[2,cyCount]=var[cycle_var]/100;
+                colname[3,CyCount]=cor[cycle_cor]/100;
+                colname[4,CyCount]=roh[cycle_roh]/100;
+
+                cyCount=cyCount+1;
+
+*/
+@------------------DATA CHECK----------------------------------------------
+dev=eye(nJ[cycle_nJ]/j[cycle_J]).*.(eye(J[cycle_J])-ones(J[cycle_J],1)*ones(J[cycle_J],1)'./J[cycle_J]); 
+ahat=(1/(nJ[cycle_nJ]-nJ[cycle_nJ]/J[cycle_J]))*(dev*(y-mx))'*(dev*(y-mx));
+bhat=(1/nJ[cycle_nJ])*(y-mx-dev*(y-mx))'*(y-mx-dev*(y-mx))-ahat/J[cycle_J];
+print "variance of epsilon"  (1-cor[cycle_cor]/100)*var[cycle_var]/100 "variance of alpha" var[cycle_var]*cor[cycle_cor]/10000;
+print "variance of epsilon" ahat  "variance of alpha" bhat;
+--------------------------------------------------------------------------@
+
+                 
+         cyc_var=cyc_var+1;
+         endo;
+       
+       cyc_nj=cyc_nj+1;
+       endo;
+         
+/*print colname|bgwr;*/
+
+print "y~g1~g2~Dtreat~Dtime~x~epsilon~trueATE";
+print d;
+
+
+stop;
+
+print " ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++";
+print "Kernel weight using stdev as bandwidth";
+print dist;
+
+stop;
+
+print "average bias & ase";
+truf=(d[.,2].^2-2.*d[.,2])~sin(6*d[.,2])~sin(-d[.,2])~cos(-d[.,2])~ones(rows(d),1)~0.5*ones(rows(d),1);
+print (meanc(fgwr-truf))'|(meanc((fgwr-truf).^2))';
+/*
+print (bols-meanc(truf))';
+print (meanc((ones(rows(d),1)*bols'-truf).^2))';
+*/
+
+@--------------GRAPHS------------------------------------------------------@
+z1=x~y~(x^2-2*x)~sin(6*x)~sin(-x)~cos(-x)~ones(rows(d),1)~0.5*ones(rows(d),1);
+z1=sortc(z1,1);
+
+/*print "x~y~bgwr~(x^2-2*x)~sin(6*x)~sin(-x)~cos(-x)";print z1;*/
+
+library pgraph;
+graphset;
+_pdate="";
+ylabel("fgwr(x) and y");
+xlabel("x");
+title("Regression");
+_pltype = {6 };
+_plctrl = { -1,0};
+xy(z1[.,1],z1[., 8 14]);
+
+