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Robust inference for the Two-Sample 2SLS estimator()

The Two-Sample Two-Stage Least Squares (TS2SLS) data combination estimator is a popular estimator for the parameters in linear models when not all variables are observed jointly in one single data set. Although the limiting normal distribution has been established, the asymptotic variance formula ha...

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Detalles Bibliográficos
Autores principales: Pacini, David, Windmeijer, Frank
Formato: Online Artículo Texto
Lenguaje:English
Publicado: North Holland 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5026329/
https://www.ncbi.nlm.nih.gov/pubmed/27667880
http://dx.doi.org/10.1016/j.econlet.2016.06.033
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author Pacini, David
Windmeijer, Frank
author_facet Pacini, David
Windmeijer, Frank
author_sort Pacini, David
collection PubMed
description The Two-Sample Two-Stage Least Squares (TS2SLS) data combination estimator is a popular estimator for the parameters in linear models when not all variables are observed jointly in one single data set. Although the limiting normal distribution has been established, the asymptotic variance formula has only been stated explicitly in the literature for the case of conditional homoskedasticity. By using the fact that the TS2SLS estimator is a function of reduced form and first-stage OLS estimators, we derive the variance of the limiting normal distribution under conditional heteroskedasticity. A robust variance estimator is obtained, which generalises to cases with more general patterns of variable (non-)availability. Stata code and some Monte Carlo results are provided in an Appendix. Stata code for a nonlinear GMM estimator that is identical to the TS2SLS estimator in just identified models and asymptotically equivalent to the TS2SLS estimator in overidentified models is also provided there.
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spelling pubmed-50263292016-09-23 Robust inference for the Two-Sample 2SLS estimator() Pacini, David Windmeijer, Frank Econ Lett Article The Two-Sample Two-Stage Least Squares (TS2SLS) data combination estimator is a popular estimator for the parameters in linear models when not all variables are observed jointly in one single data set. Although the limiting normal distribution has been established, the asymptotic variance formula has only been stated explicitly in the literature for the case of conditional homoskedasticity. By using the fact that the TS2SLS estimator is a function of reduced form and first-stage OLS estimators, we derive the variance of the limiting normal distribution under conditional heteroskedasticity. A robust variance estimator is obtained, which generalises to cases with more general patterns of variable (non-)availability. Stata code and some Monte Carlo results are provided in an Appendix. Stata code for a nonlinear GMM estimator that is identical to the TS2SLS estimator in just identified models and asymptotically equivalent to the TS2SLS estimator in overidentified models is also provided there. North Holland 2016-09 /pmc/articles/PMC5026329/ /pubmed/27667880 http://dx.doi.org/10.1016/j.econlet.2016.06.033 Text en © 2016 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Pacini, David
Windmeijer, Frank
Robust inference for the Two-Sample 2SLS estimator()
title Robust inference for the Two-Sample 2SLS estimator()
title_full Robust inference for the Two-Sample 2SLS estimator()
title_fullStr Robust inference for the Two-Sample 2SLS estimator()
title_full_unstemmed Robust inference for the Two-Sample 2SLS estimator()
title_short Robust inference for the Two-Sample 2SLS estimator()
title_sort robust inference for the two-sample 2sls estimator()
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5026329/
https://www.ncbi.nlm.nih.gov/pubmed/27667880
http://dx.doi.org/10.1016/j.econlet.2016.06.033
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