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Full-Text Articles in Social and Behavioral Sciences
Nonparametric Inference Based On Conditional Moment Inequalities, Donald W.K. Andrews, Xiaoxia Shi
Nonparametric Inference Based On Conditional Moment Inequalities, Donald W.K. Andrews, Xiaoxia Shi
Cowles Foundation Discussion Papers
This paper develops methods of inference for nonparametric and semiparametric parameters defined by conditional moment inequalities and/or equalities. The parameters need not be identified. Confidence sets and tests are introduced. The correct uniform asymptotic size of these procedures is established. The false coverage probabilities and power of the CS’s and tests are established for fixed alternatives and some local alternatives. Finite-sample simulation results are given for a nonparametric conditional quantile model with censoring and a nonparametric conditional treatment effect model. The recommended CS/test uses a Cramér-von-Mises-type test statistic and employs a generalized moment selection critical value.
Nonparametric Inference Based On Conditional Moment Inequalities, Donald W.K. Andrews, Xiaoxia Shi
Nonparametric Inference Based On Conditional Moment Inequalities, Donald W.K. Andrews, Xiaoxia Shi
Cowles Foundation Discussion Papers
This paper develops methods of inference for nonparametric and semiparametric parameters defined by conditional moment inequalities and/or equalities. The parameters need not be identified. Confidence sets and tests are introduced. The correct uniform asymptotic size of these procedures is established. The false coverage probabilities and power of the CS’s and tests are established for fixed alternatives and some local alternatives. Finite-sample simulation results are given for a nonparametric conditional quantile model with censoring and a nonparametric conditional treatment effect model. The recommended CS/test uses a Cramér-von-Mises-type test statistic and employs a generalized moment selection critical value.
Nonparametric Inference Based On Conditional Moment Inequalities, Donald W.K. Andrews, Xiaoxia Shi
Nonparametric Inference Based On Conditional Moment Inequalities, Donald W.K. Andrews, Xiaoxia Shi
Cowles Foundation Discussion Papers
This paper develops methods of inference for nonparametric and semiparametric parameters defined by conditional moment inequalities and/or equalities. The parameters need not be identified. Confidence sets and tests are introduced. The correct uniform asymptotic size of these procedures is established. The false coverage probabilities and power of the CS’s and tests are established for fixed alternatives and some local alternatives. Finite-sample simulation results are given for a nonparametric conditional quantile model with censoring and a nonparametric conditional treatment effect model. The recommended CS/test uses a Cramér-von-Mises-type test statistic and employs a generalized moment selection critical value.
Sensitivity Analysis In Semiparametric Likelihood Models, Xiaohong Chen, Elie Tamer, Alexander Torgovitsky
Sensitivity Analysis In Semiparametric Likelihood Models, Xiaohong Chen, Elie Tamer, Alexander Torgovitsky
Cowles Foundation Discussion Papers
We provide methods for inference on a finite dimensional parameter of interest, θ in Re ^{ d _θ}, in a semiparametric probability model when an infinite dimensional nuisance parameter, g , is present. We depart from the semiparametric literature in that we do not require that the pair (θ, g ) is point identified and so we construct confidence regions for θ that are robust to non-point identification. This allows practitioners to examine the sensitivity of their estimates of θ to specification of g in a likelihood setup. To construct these confidence regions for θ, we invert a profiled sieve …