Inference in Limited Dependent Variable Models Robust to Weak Identification
AbstractWe propose tests for structural parameters in limited dependent variable models with endogenous explanatory variables using the classical minimum distance framework. These tests have the correct size whether the structural parameters are identified or not. Relating to the current tests, the application of ours is appropriate especially to models whose moment conditions are nonlinear in parameters. Moreover, the computation of ours tests is simple, allowing their implementation in a large number of statistical software packages. We compare our tests with Wald tests by performing simulation experiments. We use our tests to analyze the female labor supply and the demand for cigarette.
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Bibliographic InfoPaper provided by Tulane University, Department of Economics in its series Working Papers with number 0801.
Length: 31 pages
Date of creation: Sep 2008
Date of revision: Apr 2009
weak identification; minimum chi-square estimation; hypothesis testing; limited dependent variable models;
Other versions of this item:
- Leandro M. Magnusson, 2010. "Inference in limited dependent variable models robust to weak identification," Econometrics Journal, Royal Economic Society, vol. 13(3), pages S56-S79, October.
- 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
- C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
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