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Inference For Parameters Defined By Moment Inequalities: A Recommended Moment Selection Procedure, Donald W.K. Andrews, Panle Jai Barwick
Inference For Parameters Defined By Moment Inequalities: A Recommended Moment Selection Procedure, Donald W.K. Andrews, Panle Jai Barwick
Cowles Foundation Discussion Papers
This paper is concerned with tests and confidence intervals for parameters that are not necessarily identified and are defined by moment inequalities. In the literature, different test statistics, critical value methods, and implementation methods (i.e., the asymptotic distribution versus the bootstrap) have been proposed. In this paper, we compare these methods. We provide a recommended test statistic, moment selection critical value method, and implementation method. We provide data-dependent procedures for choosing the key moment selection tuning parameter kappa and a size-correction factor eta.
Inference For Parameters Defined By Moment Inequalities: A Recommended Moment Selection Procedure, Donald W.K. Andrews, Panle Jai Barwick
Inference For Parameters Defined By Moment Inequalities: A Recommended Moment Selection Procedure, Donald W.K. Andrews, Panle Jai Barwick
Cowles Foundation Discussion Papers
This paper is concerned with tests and confidence intervals for partially-identified parameters that are defined by moment inequalities and equalities. In the literature, different test statistics, critical value methods, and implementation methods (i.e., asymptotic distribution versus the bootstrap) have been proposed. In this paper, we compare a wide variety of these methods. We provide a recommended test statistic, moment selection critical value method, and implementation method. In addition, we provide a data-dependent procedure for choosing the key moment selection tuning parameter and a data-dependent size-correction factor.
Bootstrap For Interval Endpoints Defined By Moment Inequalities, Donald W.K. Andrews, Sukjin Han
Bootstrap For Interval Endpoints Defined By Moment Inequalities, Donald W.K. Andrews, Sukjin Han
Cowles Foundation Discussion Papers
This paper analyzes the finite-sample and asymptotic properties of several bootstrap and m out of n bootstrap methods for constructing confidence interval (CI) endpoints in models defined by moment inequalities. In particular, we consider using these methods directly to construct CI endpoints. By considering two very simple models, the paper shows that neither the bootstrap nor the m out of n bootstrap is valid in finite samples or in a uniform asymptotic sense in general when applied directly to construct CI endpoints. In contrast, other results in the literature show that other ways of applying the bootstrap, m out of …