Implementing weak-instrument robust tests for a general class of instrumental-variables models
AbstractWe present a minimum distance approach for conducting hypothesis testing in the presence of potentially weak instruments. Under this approach, we propose size-correct tests for limited dependent variable models with endogenous explanatory variables such as endogenous tobit and probit models. Addition- ally, we extend weak-instrument tests for the linear instrumental-variables model by allowing for variance–covariance estimation that is robust to arbitrary het- eroskedasticity or intracluster dependence. We invert these tests to construct confidence intervals on the coefficient of the endogenous variable. We also provide a postestimation command for Stata, called rivtest, for computing the tests and estimating confidence intervals. Copyright 2009 by StataCorp LP.
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Bibliographic InfoArticle provided by StataCorp LP in its journal Stata Journal.
Volume (Year): 9 (2009)
Issue (Month): 3 (September)
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Other versions of this item:
- Keith Finlay & Leandro M. Magnusson, 2009. "Implementing Weak Instrument Robust Tests for a General Class of Instrumental Variables Models," Working Papers 0901, Tulane University, Department of Economics.
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
- C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
- C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
- C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
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