A Practical Comparison of the Bivariate Probit and Linear IV Estimators
This paper presents asymptotic theory and Monte-Carlo simulations comparing maximum-likelihood bivariate probit and linear instrumental variables estimators of treatment effects in models with a binary endogenous treatment and binary outcome. The t...
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| Format: | Policy Research Working Paper |
| Language: | English |
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2012
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| Online Access: | http://www-wds.worldbank.org/external/default/main?menuPK=64187510&pagePK=64193027&piPK=64187937&theSitePK=523679&menuPK=64187510&searchMenuPK=64187283&siteName=WDS&entityID=000158349_20110317174628 http://hdl.handle.net/10986/3368 |
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okr-10986-33682021-04-23T14:02:09Z A Practical Comparison of the Bivariate Probit and Linear IV Estimators Chiburis, Richard C. Das, Jishnu Lokshin, Michael ASYMPTOTIC DISTRIBUTION BOOTSTRAP CONFIDENCE INTERVALS CRITICAL VALUE CRITICAL VALUES DEGREES OF FREEDOM DEVELOPMENT RESEARCH DISTRIBUTION FUNCTION DUMMY VARIABLE ECONOMETRICS ENDOGENOUS REGRESSORS EQUATION SYSTEM ERROR ERROR TERMS ESTIMATORS EXHIBITS GOODNESS OF FIT HYPOTHESIS TESTING INSTRUMENTAL VARIABLES INSTRUMENTAL VARIABLES ESTIMATION KURTOSIS LIMITED DEPENDENT VARIABLE LIMITED DEPENDENT VARIABLES LOG-LIKELIHOOD FUNCTION LOGISTIC REGRESSION MATRIX MAXIMUM LIKELIHOOD MEAN SQUARE 0 HYPOTHESIS NUMBER OF OBSERVATIONS PREDICTION PROBABILITIES PROBABILITY PUBLIC SERVICES RANDOM VARIABLES REGRESSION MODEL RESEARCH WORKING PAPERS RESEARCHERS SAMPLE SIZE SCIENCES SIMULATION SIMULATIONS SKEWNESS SMALL SAMPLE STANDARD DEVIATION STANDARD ERRORS STANDARD NORMAL DISTRIBUTION STATA STATISTICAL SOFTWARE STRUCTURAL PARAMETERS T-TESTS TECHNIQUES TEST STATISTIC VALIDITY This paper presents asymptotic theory and Monte-Carlo simulations comparing maximum-likelihood bivariate probit and linear instrumental variables estimators of treatment effects in models with a binary endogenous treatment and binary outcome. The three main contributions of the paper are (a) clarifying the relationship between the Average Treatment Effect obtained in the bivariate probit model and the Local Average Treatment Effect estimated through linear IV; (b) comparing the mean-square error and the actual size and power of tests based on these estimators across a wide range of parameter values relative to the existing literature; and (c) assessing the performance of misspecification tests for bivariate probit models. The authors recommend two changes to common practices: bootstrapped confidence intervals for both estimators, and a score test to check goodness of fit for the bivariate probit model. 2012-03-19T18:01:11Z 2012-03-19T18:01:11Z 2011-03-01 http://www-wds.worldbank.org/external/default/main?menuPK=64187510&pagePK=64193027&piPK=64187937&theSitePK=523679&menuPK=64187510&searchMenuPK=64187283&siteName=WDS&entityID=000158349_20110317174628 http://hdl.handle.net/10986/3368 English Policy Research working paper ; no. WPS 5601 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo/ World Bank Publications & Research :: Policy Research Working Paper The World Region The World Region |
| repository_type |
Digital Repository |
| institution_category |
Foreign Institution |
| institution |
Digital Repositories |
| building |
World Bank Open Knowledge Repository |
| collection |
World Bank |
| language |
English |
| topic |
ASYMPTOTIC DISTRIBUTION BOOTSTRAP CONFIDENCE INTERVALS CRITICAL VALUE CRITICAL VALUES DEGREES OF FREEDOM DEVELOPMENT RESEARCH DISTRIBUTION FUNCTION DUMMY VARIABLE ECONOMETRICS ENDOGENOUS REGRESSORS EQUATION SYSTEM ERROR ERROR TERMS ESTIMATORS EXHIBITS GOODNESS OF FIT HYPOTHESIS TESTING INSTRUMENTAL VARIABLES INSTRUMENTAL VARIABLES ESTIMATION KURTOSIS LIMITED DEPENDENT VARIABLE LIMITED DEPENDENT VARIABLES LOG-LIKELIHOOD FUNCTION LOGISTIC REGRESSION MATRIX MAXIMUM LIKELIHOOD MEAN SQUARE 0 HYPOTHESIS NUMBER OF OBSERVATIONS PREDICTION PROBABILITIES PROBABILITY PUBLIC SERVICES RANDOM VARIABLES REGRESSION MODEL RESEARCH WORKING PAPERS RESEARCHERS SAMPLE SIZE SCIENCES SIMULATION SIMULATIONS SKEWNESS SMALL SAMPLE STANDARD DEVIATION STANDARD ERRORS STANDARD NORMAL DISTRIBUTION STATA STATISTICAL SOFTWARE STRUCTURAL PARAMETERS T-TESTS TECHNIQUES TEST STATISTIC VALIDITY |
| spellingShingle |
ASYMPTOTIC DISTRIBUTION BOOTSTRAP CONFIDENCE INTERVALS CRITICAL VALUE CRITICAL VALUES DEGREES OF FREEDOM DEVELOPMENT RESEARCH DISTRIBUTION FUNCTION DUMMY VARIABLE ECONOMETRICS ENDOGENOUS REGRESSORS EQUATION SYSTEM ERROR ERROR TERMS ESTIMATORS EXHIBITS GOODNESS OF FIT HYPOTHESIS TESTING INSTRUMENTAL VARIABLES INSTRUMENTAL VARIABLES ESTIMATION KURTOSIS LIMITED DEPENDENT VARIABLE LIMITED DEPENDENT VARIABLES LOG-LIKELIHOOD FUNCTION LOGISTIC REGRESSION MATRIX MAXIMUM LIKELIHOOD MEAN SQUARE 0 HYPOTHESIS NUMBER OF OBSERVATIONS PREDICTION PROBABILITIES PROBABILITY PUBLIC SERVICES RANDOM VARIABLES REGRESSION MODEL RESEARCH WORKING PAPERS RESEARCHERS SAMPLE SIZE SCIENCES SIMULATION SIMULATIONS SKEWNESS SMALL SAMPLE STANDARD DEVIATION STANDARD ERRORS STANDARD NORMAL DISTRIBUTION STATA STATISTICAL SOFTWARE STRUCTURAL PARAMETERS T-TESTS TECHNIQUES TEST STATISTIC VALIDITY Chiburis, Richard C. Das, Jishnu Lokshin, Michael A Practical Comparison of the Bivariate Probit and Linear IV Estimators |
| geographic_facet |
The World Region The World Region |
| relation |
Policy Research working paper ; no. WPS 5601 |
| description |
This paper presents asymptotic theory
and Monte-Carlo simulations comparing maximum-likelihood
bivariate probit and linear instrumental variables
estimators of treatment effects in models with a binary
endogenous treatment and binary outcome. The three main
contributions of the paper are (a) clarifying the
relationship between the Average Treatment Effect obtained
in the bivariate probit model and the Local Average
Treatment Effect estimated through linear IV; (b) comparing
the mean-square error and the actual size and power of tests
based on these estimators across a wide range of parameter
values relative to the existing literature; and (c)
assessing the performance of misspecification tests for
bivariate probit models. The authors recommend two changes
to common practices: bootstrapped confidence intervals for
both estimators, and a score test to check goodness of fit
for the bivariate probit model. |
| format |
Publications & Research :: Policy Research Working Paper |
| author |
Chiburis, Richard C. Das, Jishnu Lokshin, Michael |
| author_facet |
Chiburis, Richard C. Das, Jishnu Lokshin, Michael |
| author_sort |
Chiburis, Richard C. |
| title |
A Practical Comparison of the Bivariate Probit and Linear IV Estimators |
| title_short |
A Practical Comparison of the Bivariate Probit and Linear IV Estimators |
| title_full |
A Practical Comparison of the Bivariate Probit and Linear IV Estimators |
| title_fullStr |
A Practical Comparison of the Bivariate Probit and Linear IV Estimators |
| title_full_unstemmed |
A Practical Comparison of the Bivariate Probit and Linear IV Estimators |
| title_sort |
practical comparison of the bivariate probit and linear iv estimators |
| publishDate |
2012 |
| url |
http://www-wds.worldbank.org/external/default/main?menuPK=64187510&pagePK=64193027&piPK=64187937&theSitePK=523679&menuPK=64187510&searchMenuPK=64187283&siteName=WDS&entityID=000158349_20110317174628 http://hdl.handle.net/10986/3368 |
| _version_ |
1764386869375664128 |