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createallgraphs.log
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createallgraphs.log
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___ ____ ____ ____ ____ (R)
/__ / ____/ / ____/
___/ / /___/ / /___/ 16.0 Copyright 1985-2019 StataCorp LLC
Statistics/Data Analysis StataCorp
4905 Lakeway Drive
MP - Parallel Edition College Station, Texas 77845 USA
800-STATA-PC http://www.stata.com
979-696-4600 stata@stata.com
979-696-4601 (fax)
Single-user 2-core Stata perpetual license:
Serial number: 501606204617
Licensed to: Miklos Koren
CEU MicroData
Notes:
1. Stata is running in batch mode.
2. Unicode is supported; see help unicode_advice.
3. More than 2 billion observations are allowed; see help obs_advice.
4. Maximum number of variables is set to 5000; see help set_maxvar.
. do createallgraphs.do
. use "data/derived/specmeasures.dta", clear
. tsset, clear
.
. drop if rgdpch==0
(0 observations deleted)
.
. gen loggdp = log(rgdpch)
(3,993 missing values generated)
.
. rename COV Sector_Country_Covariance
. rename GSECT Sectoral_Risk
. rename HERF Herfindahl
. rename AVAR Average_Variance
. rename IDIO Idiosyncratic_Risk
. rename CNT Country_Risk
. rename RISK Overall_risk
.
. label var loggdp "Log Real GDP per capita (PPP)"
.
. label var Herfindahl "Concentration"
. label var Average_Variance "Average Idiosyncratic Variance"
. label var Idiosyncratic_Risk "Idiosyncratic Risk"
. label var Sectoral_Risk "Sectoral Risk"
. label var Country_Risk "Country Risk"
. label var Overall_risk "Overall Risk"
. label var Sector_Country_Covariance "Sector-Country Covariance"
.
.
. foreach X of varlist Herfindahl Idiosyncratic_Risk Sectoral_Risk Sector_Coun
> try_Covariance Average_Variance {
2. do drawgraphs `X' loggdp
3. }
. tempvar y1 y2 u
. local lbl : variable label `1'
.
. * if risk measure is strictly positive, take logs
. summarize `1'
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
Herfindahl | 1,507 .1241311 .0560934 .0788353 .5599452
. scalar min = r(min)
. if (min > 0) {
. generate `y1' = ln(`1')
(6,802 missing values generated)
. * demean all logs to have 0 mean
. summarize `y1', meanonly
. replace `y1' = `y1' - r(mean)
(1,507 real changes made)
. label variable `y1' "`lbl' (log)"
. }
. else {
. generate `y1' = `1'
. label variable `y1' "`lbl'"
. }
. clonevar `y2' = `y1'
(6,802 missing values generated)
.
. lowess `y1' `2', bwidth(0.5) msymbol(circle) msize(tiny) scheme(s1manual) plo
> tregion(ilwidth(medthin))
. graph export "graphs/lowess_`1'_`2'.eps", replace
(file graphs/lowess_Herfindahl_loggdp.eps written in EPS format)
.
. xtreg `y1' `2', i(country) fe
Fixed-effects (within) regression Number of obs = 1,427
Group variable: country Number of groups = 45
R-sq: Obs per group:
within = 0.0003 min = 20
between = 0.5224 avg = 31.7
overall = 0.3965 max = 37
F(1,1381) = 0.36
corr(u_i, Xb) = -0.6953 Prob > F = 0.5483
------------------------------------------------------------------------------
__000000 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
loggdp | .007358 .0122536 0.60 0.548 -.0166797 .0313958
_cons | -.0603654 .1083659 -0.56 0.578 -.272945 .1522141
-------------+----------------------------------------------------------------
sigma_u | .3155699
sigma_e | .12783332
rho | .85903596 (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0: F(44, 1381) = 93.08 Prob > F = 0.0000
. predict `u', u
(6,882 missing values generated)
. replace `y2' = `y1' - `u'
(1,507 real changes made, 80 to missing)
.
. lowess `y2' `2', bwidth(0.5) msymbol(circle) msize(tiny) scheme(s1manual) plo
> tregion(ilwidth(medthin))
. graph export "graphs/within_`1'_`2'.eps", replace
(file graphs/within_Herfindahl_loggdp.eps written in EPS format)
.
.
end of do-file
. tempvar y1 y2 u
. local lbl : variable label `1'
.
. * if risk measure is strictly positive, take logs
. summarize `1'
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
Idiosyncra~k | 1,507 .0054294 .0062876 .0003165 .0328147
. scalar min = r(min)
. if (min > 0) {
. generate `y1' = ln(`1')
(6,802 missing values generated)
. * demean all logs to have 0 mean
. summarize `y1', meanonly
. replace `y1' = `y1' - r(mean)
(1,507 real changes made)
. label variable `y1' "`lbl' (log)"
. }
. else {
. generate `y1' = `1'
. label variable `y1' "`lbl'"
. }
. clonevar `y2' = `y1'
(6,802 missing values generated)
.
. lowess `y1' `2', bwidth(0.5) msymbol(circle) msize(tiny) scheme(s1manual) plo
> tregion(ilwidth(medthin))
. graph export "graphs/lowess_`1'_`2'.eps", replace
(file graphs/lowess_Idiosyncratic_Risk_loggdp.eps written in EPS format)
.
. xtreg `y1' `2', i(country) fe
Fixed-effects (within) regression Number of obs = 1,427
Group variable: country Number of groups = 45
R-sq: Obs per group:
within = 0.0245 min = 20
between = 0.6884 avg = 31.7
overall = 0.6009 max = 37
F(1,1381) = 34.65
corr(u_i, Xb) = -0.8006 Prob > F = 0.0000
------------------------------------------------------------------------------
__000000 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
loggdp | .0757578 .0128698 5.89 0.000 .0505114 .1010042
_cons | -.6485447 .1138148 -5.70 0.000 -.8718132 -.4252761
-------------+----------------------------------------------------------------
sigma_u | 1.290303
sigma_e | .13426106
rho | .98928876 (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0: F(44, 1381) = 997.68 Prob > F = 0.0000
. predict `u', u
(6,882 missing values generated)
. replace `y2' = `y1' - `u'
(1,507 real changes made, 80 to missing)
.
. lowess `y2' `2', bwidth(0.5) msymbol(circle) msize(tiny) scheme(s1manual) plo
> tregion(ilwidth(medthin))
. graph export "graphs/within_`1'_`2'.eps", replace
(file graphs/within_Idiosyncratic_Risk_loggdp.eps written in EPS format)
.
.
end of do-file
. tempvar y1 y2 u
. local lbl : variable label `1'
.
. * if risk measure is strictly positive, take logs
. summarize `1'
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
Sectoral_R~k | 1,507 .0037045 .0004958 .0026052 .0062056
. scalar min = r(min)
. if (min > 0) {
. generate `y1' = ln(`1')
(6,802 missing values generated)
. * demean all logs to have 0 mean
. summarize `y1', meanonly
. replace `y1' = `y1' - r(mean)
(1,507 real changes made)
. label variable `y1' "`lbl' (log)"
. }
. else {
. generate `y1' = `1'
. label variable `y1' "`lbl'"
. }
. clonevar `y2' = `y1'
(6,802 missing values generated)
.
. lowess `y1' `2', bwidth(0.5) msymbol(circle) msize(tiny) scheme(s1manual) plo
> tregion(ilwidth(medthin))
. graph export "graphs/lowess_`1'_`2'.eps", replace
(file graphs/lowess_Sectoral_Risk_loggdp.eps written in EPS format)
.
. xtreg `y1' `2', i(country) fe
Fixed-effects (within) regression Number of obs = 1,427
Group variable: country Number of groups = 45
R-sq: Obs per group:
within = 0.5114 min = 20
between = 0.5927 avg = 31.7
overall = 0.5607 max = 37
F(1,1381) = 1445.40
corr(u_i, Xb) = -0.3117 Prob > F = 0.0000
------------------------------------------------------------------------------
__000000 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
loggdp | -.1364966 .0035903 -38.02 0.000 -.1435396 -.1294536
_cons | 1.210405 .0317508 38.12 0.000 1.14812 1.27269
-------------+----------------------------------------------------------------
sigma_u | .08114316
sigma_e | .03745473
rho | .82435899 (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0: F(44, 1381) = 134.63 Prob > F = 0.0000
. predict `u', u
(6,882 missing values generated)
. replace `y2' = `y1' - `u'
(1,507 real changes made, 80 to missing)
.
. lowess `y2' `2', bwidth(0.5) msymbol(circle) msize(tiny) scheme(s1manual) plo
> tregion(ilwidth(medthin))
. graph export "graphs/within_`1'_`2'.eps", replace
(file graphs/within_Sectoral_Risk_loggdp.eps written in EPS format)
.
.
end of do-file
. tempvar y1 y2 u
. local lbl : variable label `1'
.
. * if risk measure is strictly positive, take logs
. summarize `1'
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
Sector_Cou~e | 1,507 -.0000518 .0081577 -.0206801 .0443218
. scalar min = r(min)
. if (min > 0) {
. generate `y1' = ln(`1')
. * demean all logs to have 0 mean
. summarize `y1', meanonly
. replace `y1' = `y1' - r(mean)
. label variable `y1' "`lbl' (log)"
. }
. else {
. generate `y1' = `1'
(6,802 missing values generated)
. label variable `y1' "`lbl'"
. }
. clonevar `y2' = `y1'
(6,802 missing values generated)
.
. lowess `y1' `2', bwidth(0.5) msymbol(circle) msize(tiny) scheme(s1manual) plo
> tregion(ilwidth(medthin))
. graph export "graphs/lowess_`1'_`2'.eps", replace
(file graphs/lowess_Sector_Country_Covariance_loggdp.eps written in EPS format)
.
. xtreg `y1' `2', i(country) fe
Fixed-effects (within) regression Number of obs = 1,427
Group variable: country Number of groups = 45
R-sq: Obs per group:
within = 0.0019 min = 20
between = 0.0083 avg = 31.7
overall = 0.0002 max = 37
F(1,1381) = 2.57
corr(u_i, Xb) = -0.0275 Prob > F = 0.1093
------------------------------------------------------------------------------
__000000 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
loggdp | .000127 .0000792 1.60 0.109 -.0000285 .0002824
_cons | -.0012659 .0007008 -1.81 0.071 -.0026407 .0001089
-------------+----------------------------------------------------------------
sigma_u | .00939441
sigma_e | .0008267
rho | .99231559 (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0: F(44, 1381) = 3220.66 Prob > F = 0.0000
. predict `u', u
(6,882 missing values generated)
. replace `y2' = `y1' - `u'
(1,507 real changes made, 80 to missing)
.
. lowess `y2' `2', bwidth(0.5) msymbol(circle) msize(tiny) scheme(s1manual) plo
> tregion(ilwidth(medthin))
. graph export "graphs/within_`1'_`2'.eps", replace
(file graphs/within_Sector_Country_Covariance_loggdp.eps written in EPS format)
.
.
end of do-file
. tempvar y1 y2 u
. local lbl : variable label `1'
.
. * if risk measure is strictly positive, take logs
. summarize `1'
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
Average_Va~e | 1,507 .0388369 .0398654 .0035157 .2396667
. scalar min = r(min)
. if (min > 0) {
. generate `y1' = ln(`1')
(6,802 missing values generated)
. * demean all logs to have 0 mean
. summarize `y1', meanonly
. replace `y1' = `y1' - r(mean)
(1,507 real changes made)
. label variable `y1' "`lbl' (log)"
. }
. else {
. generate `y1' = `1'
. label variable `y1' "`lbl'"
. }
. clonevar `y2' = `y1'
(6,802 missing values generated)
.
. lowess `y1' `2', bwidth(0.5) msymbol(circle) msize(tiny) scheme(s1manual) plo
> tregion(ilwidth(medthin))
. graph export "graphs/lowess_`1'_`2'.eps", replace
(file graphs/lowess_Average_Variance_loggdp.eps written in EPS format)
.
. xtreg `y1' `2', i(country) fe
Fixed-effects (within) regression Number of obs = 1,427
Group variable: country Number of groups = 45
R-sq: Obs per group:
within = 0.0423 min = 20
between = 0.5837 avg = 31.7
overall = 0.5162 max = 37
F(1,1381) = 60.96
corr(u_i, Xb) = -0.7484 Prob > F = 0.0000
------------------------------------------------------------------------------
__000000 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
loggdp | .0683998 .0087609 7.81 0.000 .0512138 .0855858
_cons | -.5881792 .0774774 -7.59 0.000 -.7401653 -.4361931
-------------+----------------------------------------------------------------
sigma_u | 1.0933657
sigma_e | .09139584
rho | .99306098 (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0: F(44, 1381) = 1881.07 Prob > F = 0.0000
. predict `u', u
(6,882 missing values generated)
. replace `y2' = `y1' - `u'
(1,507 real changes made, 80 to missing)
.
. lowess `y2' `2', bwidth(0.5) msymbol(circle) msize(tiny) scheme(s1manual) plo
> tregion(ilwidth(medthin))
. graph export "graphs/within_`1'_`2'.eps", replace
(file graphs/within_Average_Variance_loggdp.eps written in EPS format)
.
.
end of do-file
.
. replace Country_Risk = ln(Country_Risk)
(1,507 real changes made)
. * since log does not have a scale, set mean to 0
. summarize Country_Risk, meanonly
. replace Country_Risk = Country_Risk - r(mean)
(1,507 real changes made)
. label var Country_Risk "Country Risk (log)"
.
. lowess Country_Risk loggdp if year==1980, msize(tiny) scheme(s1mono)
. graph export "graphs/lowess_Country_Risk_loggdp.eps", replace
(file graphs/lowess_Country_Risk_loggdp.eps written in EPS format)
.
end of do-file