Robust Inference for the Two-Sample 2SLS Estimator

David Pacini, Frank Windmeijer

Research output: Contribution to journalArticle (Academic Journal)peer-review

9 Citations (Scopus)
449 Downloads (Pure)

Abstract

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.
Original languageEnglish
Pages (from-to)50-54
Number of pages5
JournalEconomics Letters
Volume146
Early online date19 Jul 2016
DOIs
Publication statusPublished - 1 Sep 2016

Keywords

  • Linear Model
  • Data Combination
  • Instrumental Variables
  • Robust Inference
  • Nonlinear GMM

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