Two-Step Generalized Least Squares Estimators in Multi-Equation Generated Regressor Models
Despite the critical analysis of Pagan (1984) and several subsequent applied studies, empirical models characterized by expectations are often estimated with regressor proxies that are treated as ordinary nonstochastic This paper offers a Generalized Least Squares estimator designed to cope with the nonscalar disturbance matrix precipatated by generated The approach is designed as a natural extension of Pagan's analysis and the author demonstrates how it may be applied to multi-equation models. Experimentation with numerical examples reveals the potential severity of ignoring the problem. These results also suggest an easily calculated indicator of potential inference distortion in models that fail to account for regressors. Copyright 1987 by MIT Press.
- DOI
- 10.2307/1927242
- Volume
- 69 (2)
- Pages
- 336
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