clear
set memory 100m
set matsize 8000
set more off
log using dual_acemoglu.log ,replace

insheet using "c:\Documents and Settings\chansen1\My Documents\activeresearch\victor_weakiv\examples\acemoglu_col.txt"

gen logmort = log(mort)

/* Table 4 (1) */

/* Homoskedastic */
reg exprop logmort
predict v if e(sample) , resid
ivreg gdp (exprop = logmort)
predict e if e(sample) , resid

correlate e v

forvalues i = 0(1)5000 {
	
	quietly gen dep = gdp - ((`i'/1000) - 1)*exprop
      quietly reg dep logmort
      matrix A = e(b)
      matrix B = A[1,"logmort"]
      quietly test logmort , matvlc(V1)
      matrix C = (nullmat(C),B*inv(V1)*B')
      matrix D = (nullmat(D),`i'/1000 - 1)
	drop dep 
}
matrix C = C'
matrix D = D'
svmat double C , name(duals1)
svmat double D , name(betas1)

gen pvals1 = chi2tail(duals1,1)
gen interval1 = (pvals1 >= .95)
gen ub1 = betas1 + 1/1000
gen lb1 = betas1 - 1/1000
sum ub1 lb1 if interval1 == 0

outsheet pvals1 duals1 betas1 using homo1.txt , replace

drop e v pvals1 interval1 ub1 lb1 betas1 duals1
matrix drop A B C D

/* Heteroskedastic */
reg exprop logmort , robust
predict v if e(sample) , resid
ivreg gdp (exprop = logmort) , robust
predict e if e(sample) , resid

correlate e v

forvalues i = 0(1)5000 {
	
	quietly gen dep = gdp - ((`i'/1000) - 1)*exprop
      quietly reg dep logmort , robust
      matrix A = e(b)
      matrix B = A[1,"logmort"]
      quietly test logmort , matvlc(V1)
      matrix C = (nullmat(C),B*inv(V1)*B')
      matrix D = (nullmat(D),`i'/1000 - 1)
	drop dep 
}
matrix C = C'
matrix D = D'
svmat double C , name(duals1)
svmat double D , name(betas1)

gen pvals1 = chi2tail(duals1,1)
gen interval1 = (pvals1 >= .95)
gen ub1 = betas1 + 1/1000
gen lb1 = betas1 - 1/1000
sum ub1 lb1 if interval1 == 0

outsheet pvals1 duals1 betas1 using hetero1.txt , replace

drop e v pvals1 interval1 ub1 lb1 betas1 duals1
matrix drop A B C D



/* Table 4 (2) */

/* Homoskedastic */
reg exprop logmort latitude
predict v if e(sample) , resid
ivreg gdp (exprop = logmort) latitude
predict e if e(sample) , resid

correlate e v

forvalues i = 0(1)5000 {
	
	quietly gen dep = gdp - ((`i'/1000) - 1)*exprop
      quietly reg dep logmort latitude
      matrix A = e(b)
      matrix B = A[1,"logmort"]
      quietly test logmort , matvlc(V1)
      matrix C = (nullmat(C),B*inv(V1)*B')
      matrix D = (nullmat(D),`i'/1000 - 1)
	drop dep 
}
matrix C = C'
matrix D = D'
svmat double C , name(duals1)
svmat double D , name(betas1)

gen pvals1 = chi2tail(duals1,1)
gen interval1 = (pvals1 >= .95)
gen ub1 = betas1 + 1/1000
gen lb1 = betas1 - 1/1000
sum ub1 lb1 if interval1 == 0

outsheet pvals1 duals1 betas1 using homo2.txt , replace

drop e v pvals1 interval1 ub1 lb1 betas1 duals1
matrix drop A B C D

/* Heteroskedastic */
reg exprop logmort latitude , robust
predict v if e(sample) , resid
ivreg gdp (exprop = logmort) latitude , robust
predict e if e(sample) , resid

correlate e v

forvalues i = 0(1)5000 {
	
	quietly gen dep = gdp - ((`i'/1000) - 1)*exprop
      quietly reg dep logmort latitude , robust
      matrix A = e(b)
      matrix B = A[1,"logmort"]
      quietly test logmort , matvlc(V1)
      matrix C = (nullmat(C),B*inv(V1)*B')
      matrix D = (nullmat(D),`i'/1000 - 1)
	drop dep 
}
matrix C = C'
matrix D = D'
svmat double C , name(duals1)
svmat double D , name(betas1)

gen pvals1 = chi2tail(duals1,1)
gen interval1 = (pvals1 >= .95)
gen ub1 = betas1 + 1/1000
gen lb1 = betas1 - 1/1000
sum ub1 lb1 if interval1 == 0

outsheet pvals1 duals1 betas1 using hetero2.txt , replace

drop e v pvals1 interval1 ub1 lb1 betas1 duals1
matrix drop A B C D


/* Table 4 (3) */

/* Homoskedastic */
reg exprop logmort if neo == 0
predict v if e(sample) , resid
ivreg gdp (exprop = logmort) if neo == 0
predict e if e(sample) , resid

correlate e v

forvalues i = 0(1)5000 {
	
	quietly gen dep = gdp - ((`i'/1000) - 1)*exprop
      quietly reg dep logmort if neo == 0
      matrix A = e(b)
      matrix B = A[1,"logmort"]
      quietly test logmort , matvlc(V1)
      matrix C = (nullmat(C),B*inv(V1)*B')
      matrix D = (nullmat(D),`i'/1000 - 1)
	drop dep 
}
matrix C = C'
matrix D = D'
svmat double C , name(duals1)
svmat double D , name(betas1)

gen pvals1 = chi2tail(duals1,1)
gen interval1 = (pvals1 >= .95)
gen ub1 = betas1 + 1/1000
gen lb1 = betas1 - 1/1000
sum ub1 lb1 if interval1 == 0

outsheet pvals1 duals1 betas1 using homo3.txt , replace

drop e v pvals1 interval1 ub1 lb1 betas1 duals1
matrix drop A B C D

/* Heteroskedastic */
reg exprop logmort if neo == 0, robust
predict v if e(sample) , resid
ivreg gdp (exprop = logmort) if neo == 0, robust
predict e if e(sample) , resid

correlate e v

forvalues i = 0(1)5000 {
	
	quietly gen dep = gdp - ((`i'/1000) - 1)*exprop
      quietly reg dep logmort if neo == 0, robust
      matrix A = e(b)
      matrix B = A[1,"logmort"]
      quietly test logmort , matvlc(V1)
      matrix C = (nullmat(C),B*inv(V1)*B')
      matrix D = (nullmat(D),`i'/1000 - 1)
	drop dep 
}
matrix C = C'
matrix D = D'
svmat double C , name(duals1)
svmat double D , name(betas1)

gen pvals1 = chi2tail(duals1,1)
gen interval1 = (pvals1 >= .95)
gen ub1 = betas1 + 1/1000
gen lb1 = betas1 - 1/1000
sum ub1 lb1 if interval1 == 0

outsheet pvals1 duals1 betas1 using hetero3.txt , replace

drop e v pvals1 interval1 ub1 lb1 betas1 duals1
matrix drop A B C D



/* Table 4 (4) */

/* Homoskedastic */
reg exprop logmort latitude if neo == 0
predict v if e(sample) , resid
ivreg gdp (exprop = logmort) latitude if neo == 0
predict e if e(sample) , resid

correlate e v

forvalues i = 0(1)5000 {
	
	quietly gen dep = gdp - ((`i'/1000) - 1)*exprop
      quietly reg dep logmort latitude if neo == 0
      matrix A = e(b)
      matrix B = A[1,"logmort"]
      quietly test logmort , matvlc(V1)
      matrix C = (nullmat(C),B*inv(V1)*B')
      matrix D = (nullmat(D),`i'/1000 - 1)
	drop dep 
}
matrix C = C'
matrix D = D'
svmat double C , name(duals1)
svmat double D , name(betas1)

gen pvals1 = chi2tail(duals1,1)
gen interval1 = (pvals1 >= .95)
gen ub1 = betas1 + 1/1000
gen lb1 = betas1 - 1/1000
sum ub1 lb1 if interval1 == 0

outsheet pvals1 duals1 betas1 using homo4.txt , replace

drop e v pvals1 interval1 ub1 lb1 betas1 duals1
matrix drop A B C D

/* Heteroskedastic */
reg exprop logmort latitude if neo == 0, robust
predict v if e(sample) , resid
ivreg gdp (exprop = logmort) latitude if neo == 0 , robust
predict e if e(sample) , resid

correlate e v

forvalues i = 0(1)5000 {
	
	quietly gen dep = gdp - ((`i'/1000) - 1)*exprop
      quietly reg dep logmort latitude if neo == 0, robust
      matrix A = e(b)
      matrix B = A[1,"logmort"]
      quietly test logmort , matvlc(V1)
      matrix C = (nullmat(C),B*inv(V1)*B')
      matrix D = (nullmat(D),`i'/1000 - 1)
	drop dep 
}
matrix C = C'
matrix D = D'
svmat double C , name(duals1)
svmat double D , name(betas1)

gen pvals1 = chi2tail(duals1,1)
gen interval1 = (pvals1 >= .95)
gen ub1 = betas1 + 1/1000
gen lb1 = betas1 - 1/1000
sum ub1 lb1 if interval1 == 0

outsheet pvals1 duals1 betas1 using hetero4.txt , replace

drop e v pvals1 interval1 ub1 lb1 betas1 duals1
matrix drop A B C D

/* Table 4 (5) */

/* Homoskedastic */
reg exprop logmort if africa == 0
predict v if e(sample) , resid
ivreg gdp (exprop = logmort) if africa == 0
predict e if e(sample) , resid

correlate e v

forvalues i = 0(1)5000 {
	
	quietly gen dep = gdp - ((`i'/1000) - 1)*exprop
      quietly reg dep logmort if africa == 0
      matrix A = e(b)
      matrix B = A[1,"logmort"]
      quietly test logmort , matvlc(V1)
      matrix C = (nullmat(C),B*inv(V1)*B')
      matrix D = (nullmat(D),`i'/1000 - 1)
	drop dep 
}
matrix C = C'
matrix D = D'
svmat double C , name(duals1)
svmat double D , name(betas1)

gen pvals1 = chi2tail(duals1,1)
gen interval1 = (pvals1 >= .95)
gen ub1 = betas1 + 1/1000
gen lb1 = betas1 - 1/1000
sum ub1 lb1 if interval1 == 0

outsheet pvals1 duals1 betas1 using homo5.txt , replace

drop e v pvals1 interval1 ub1 lb1 betas1 duals1
matrix drop A B C D

/* Heteroskedastic */
reg exprop logmort if africa == 0, robust
predict v if e(sample) , resid
ivreg gdp (exprop = logmort) if africa == 0, robust
predict e if e(sample) , resid

correlate e v

forvalues i = 0(1)5000 {
	
	quietly gen dep = gdp - ((`i'/1000) - 1)*exprop
      quietly reg dep logmort if africa == 0, robust
      matrix A = e(b)
      matrix B = A[1,"logmort"]
      quietly test logmort , matvlc(V1)
      matrix C = (nullmat(C),B*inv(V1)*B')
      matrix D = (nullmat(D),`i'/1000 - 1)
	drop dep 
}
matrix C = C'
matrix D = D'
svmat double C , name(duals1)
svmat double D , name(betas1)

gen pvals1 = chi2tail(duals1,1)
gen interval1 = (pvals1 >= .95)
gen ub1 = betas1 + 1/1000
gen lb1 = betas1 - 1/1000
sum ub1 lb1 if interval1 == 0

outsheet pvals1 duals1 betas1 using hetero5.txt , replace

drop e v pvals1 interval1 ub1 lb1 betas1 duals1
matrix drop A B C D



/* Table 4 (6) */

/* Homoskedastic */
reg exprop logmort latitude if africa == 0
predict v if e(sample) , resid
ivreg gdp (exprop = logmort) latitude if africa == 0
predict e if e(sample) , resid

correlate e v

forvalues i = 0(1)5000 {
	
	quietly gen dep = gdp - ((`i'/1000) - 1)*exprop
      quietly reg dep logmort latitude if africa == 0
      matrix A = e(b)
      matrix B = A[1,"logmort"]
      quietly test logmort , matvlc(V1)
      matrix C = (nullmat(C),B*inv(V1)*B')
      matrix D = (nullmat(D),`i'/1000 - 1)
	drop dep 
}
matrix C = C'
matrix D = D'
svmat double C , name(duals1)
svmat double D , name(betas1)

gen pvals1 = chi2tail(duals1,1)
gen interval1 = (pvals1 >= .95)
gen ub1 = betas1 + 1/1000
gen lb1 = betas1 - 1/1000
sum ub1 lb1 if interval1 == 0

outsheet pvals1 duals1 betas1 using homo6.txt , replace

drop e v pvals1 interval1 ub1 lb1 betas1 duals1
matrix drop A B C D

/* Heteroskedastic */
reg exprop logmort latitude if africa == 0, robust
predict v if e(sample) , resid
ivreg gdp (exprop = logmort) latitude if africa == 0 , robust
predict e if e(sample) , resid

correlate e v

forvalues i = 0(1)5000 {
	
	quietly gen dep = gdp - ((`i'/1000) - 1)*exprop
      quietly reg dep logmort latitude if africa == 0, robust
      matrix A = e(b)
      matrix B = A[1,"logmort"]
      quietly test logmort , matvlc(V1)
      matrix C = (nullmat(C),B*inv(V1)*B')
      matrix D = (nullmat(D),`i'/1000 - 1)
	drop dep 
}
matrix C = C'
matrix D = D'
svmat double C , name(duals1)
svmat double D , name(betas1)

gen pvals1 = chi2tail(duals1,1)
gen interval1 = (pvals1 >= .95)
gen ub1 = betas1 + 1/1000
gen lb1 = betas1 - 1/1000
sum ub1 lb1 if interval1 == 0

outsheet pvals1 duals1 betas1 using hetero6.txt , replace

drop e v pvals1 interval1 ub1 lb1 betas1 duals1
matrix drop A B C D


/* Table 4 (7) */

gen other = asia == 0 & africa == 0 & namer == 0 & samer == 0

/* Homoskedastic */
reg exprop logmort asia africa other
predict v if e(sample) , resid
ivreg gdp (exprop = logmort) asia africa other
predict e if e(sample) , resid

correlate e v

forvalues i = 0(1)5000 {
	
	quietly gen dep = gdp - ((`i'/1000) - 1)*exprop
      quietly reg dep logmort asia africa other
      matrix A = e(b)
      matrix B = A[1,"logmort"]
      quietly test logmort , matvlc(V1)
      matrix C = (nullmat(C),B*inv(V1)*B')
      matrix D = (nullmat(D),`i'/1000 - 1)
	drop dep 
}
matrix C = C'
matrix D = D'
svmat double C , name(duals1)
svmat double D , name(betas1)

gen pvals1 = chi2tail(duals1,1)
gen interval1 = (pvals1 >= .95)
gen ub1 = betas1 + 1/1000
gen lb1 = betas1 - 1/1000
sum ub1 lb1 if interval1 == 0

outsheet pvals1 duals1 betas1 using homo7.txt , replace

drop e v pvals1 interval1 ub1 lb1 betas1 duals1
matrix drop A B C D

/* Heteroskedastic */
reg exprop logmort asia africa other , robust
predict v if e(sample) , resid
ivreg gdp (exprop = logmort) asia africa other , robust
predict e if e(sample) , resid

correlate e v

forvalues i = 0(1)5000 {
	
	quietly gen dep = gdp - ((`i'/1000) - 1)*exprop
      quietly reg dep logmort asia africa other , robust
      matrix A = e(b)
      matrix B = A[1,"logmort"]
      quietly test logmort , matvlc(V1)
      matrix C = (nullmat(C),B*inv(V1)*B')
      matrix D = (nullmat(D),`i'/1000 - 1)
	drop dep 
}
matrix C = C'
matrix D = D'
svmat double C , name(duals1)
svmat double D , name(betas1)

gen pvals1 = chi2tail(duals1,1)
gen interval1 = (pvals1 >= .95)
gen ub1 = betas1 + 1/1000
gen lb1 = betas1 - 1/1000
sum ub1 lb1 if interval1 == 0

outsheet pvals1 duals1 betas1 using hetero7.txt , replace

drop e v pvals1 interval1 ub1 lb1 betas1 duals1
matrix drop A B C D



/* Table 4 (8) */

/* Homoskedastic */
reg exprop logmort latitude asia africa other
predict v if e(sample) , resid
ivreg gdp (exprop = logmort) latitude asia africa other
predict e if e(sample) , resid

correlate e v

forvalues i = 0(1)5000 {
	
	quietly gen dep = gdp - ((`i'/1000) - 1)*exprop
      quietly reg dep logmort latitude asia africa other
      matrix A = e(b)
      matrix B = A[1,"logmort"]
      quietly test logmort , matvlc(V1)
      matrix C = (nullmat(C),B*inv(V1)*B')
      matrix D = (nullmat(D),`i'/1000 - 1)
	drop dep 
}
matrix C = C'
matrix D = D'
svmat double C , name(duals1)
svmat double D , name(betas1)

gen pvals1 = chi2tail(duals1,1)
gen interval1 = (pvals1 >= .95)
gen ub1 = betas1 + 1/1000
gen lb1 = betas1 - 1/1000
sum ub1 lb1 if interval1 == 0

outsheet pvals1 duals1 betas1 using homo8.txt , replace

drop e v pvals1 interval1 ub1 lb1 betas1 duals1
matrix drop A B C D

/* Heteroskedastic */
reg exprop logmort latitude asia africa other , robust
predict v if e(sample) , resid
ivreg gdp (exprop = logmort) latitude asia africa other , robust
predict e if e(sample) , resid

correlate e v

forvalues i = 0(1)5000 {
	
	quietly gen dep = gdp - ((`i'/1000) - 1)*exprop
      quietly reg dep logmort latitude asia africa other , robust
      matrix A = e(b)
      matrix B = A[1,"logmort"]
      quietly test logmort , matvlc(V1)
      matrix C = (nullmat(C),B*inv(V1)*B')
      matrix D = (nullmat(D),`i'/1000 - 1)
	drop dep 
}
matrix C = C'
matrix D = D'
svmat double C , name(duals1)
svmat double D , name(betas1)

gen pvals1 = chi2tail(duals1,1)
gen interval1 = (pvals1 >= .95)
gen ub1 = betas1 + 1/1000
gen lb1 = betas1 - 1/1000
sum ub1 lb1 if interval1 == 0

outsheet pvals1 duals1 betas1 using hetero8.txt , replace

drop e v pvals1 interval1 ub1 lb1 betas1 duals1
matrix drop A B C D



log close