/*
** Economic Convergence Model
** using time-dependent economic distance spatial weights
*/
use gpe2;

load wd[29,29]=tsinghua\smatrix.txt;
@ w=wd[2:29,2:29]./sumc(wd[2:29,2:29]'); @ @ row-standardized w @
w1=wd[2:29,2:29];
w2=denseSubmat(sparseOnes(spwpower(spw(w1),2),28,28),0,0);
w=w1+w2;
@ w=w1; @

load y[29,26]=tsinghua\chinay2.txt; @ rpcgdpm.txt @
y=y[2:29,2:26];
t=12; @ select the terminating year: 12=1989, 25=2002 @
@ 16/17 is the break @
/*
@ economic distance weights based on y[.,1;t] @
ym=meanc(y[.,1:t]'); @ 28 state average y over 12 (t-11:t) years @
w3=diagrv(1/abs(ym-ym'),zeros(28,1));
w=w3.*w; @ this spatial weights matrix depends on t @
*/
w=w./sumc(w'); @ row-standardized w @

_w_=sparseFD(w,0); @ dense matrix w -> sparse matrix _w_ @
_wr=real(eig(w));

print 1/maxc(_wr);
print 1/minc(_wr);

yt=y[.,t];     @ 25=2002 @
y0=y[.,t-11];  @ 1= 1978 @

yv=ln(yt./y0);
wx=sparseTD(_w_,ln(y0));

xv=ln(y0)~ones(rows(yv),1); @ unconditional model @

call sptest1(yv,xv); @ test on ols residuals @

call reset;
_nlopt=1; @ 0=weighted NLSQ or 1=MAXLIK @
_method=5;
_iter=200;
_conv=1;
_print=0;

b=yv/xv;

/* estimate and test various model specifications */
_splag=1; 
_spar=0;
_spma=0;

/*
if t>16; _splag=1; endif;
*/
call spestimate(yv,xv,b);

call sptest2(yv,xv,__b); @ test on estimated model @

_bjtest=1;
_bptest=1;
e=sprf(yv~xv,__b);
@ call diagnose(yv,xv,e); @

end;

#include gpe\spatial.gpe;
#include gpe\diagnose.gpe;