Showing 1 - 10 of 14
This paper studies a model widely used in the weak instruments literature and establishes admissibility of the weighted average power likelihood ratio tests recently derived by Andrews, Moreira, and Stock (2004, NBER Technical Working Paper 199). The class of tests covered by this admissibility...
Persistent link: https://www.econbiz.de/10004972599
In this paper we introduce a new method of projection-type inference and describe it in the context of two stage least squares–based split-sample inference on subsets of structural coefficients in a linear instrumental variables regression model. The use of the new method not only guards...
Persistent link: https://www.econbiz.de/10008739870
Econometric applications of kernel estimators are proliferating, suggesting the need for convenient variance estimates and conditions for asymptotic normality. This paper develops a general “delta-method” variance estimator for functionals of kernel estimators. Also, regularity conditions...
Persistent link: https://www.econbiz.de/10005411687
We consider the linear regression model with censored dependent variable, where the disturbance terms are restricted only to have zero conditional median (or other prespecified quantile) given the regressors and the censoring point. Thus, the functional form of the conditional distribution of...
Persistent link: https://www.econbiz.de/10005411734
Persistent link: https://www.econbiz.de/10005411940
This paper derives the limiting distributions of alternative jackknife instrumental variables (JIV) estimators and gives formulas for accompanying consistent standard errors in the presence of heteroskedasticity and many instruments. The asymptotic framework includes the many instrument sequence...
Persistent link: https://www.econbiz.de/10011067367
Persistent link: https://www.econbiz.de/10005104632
Persistent link: https://www.econbiz.de/10005140598
Persistent link: https://www.econbiz.de/10005250101
Two-step estimators, where the first step is the predicted value from a nonparametric regression, are useful in many contexts. Examples include a non-parametric residual variance, probit with nonparametric generated regressors, efficient GMM estimation with randomly missing data,...
Persistent link: https://www.econbiz.de/10005250196