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Full-Text Articles in Economics
Inference In Moment Inequality Models That Is Robust To Spurious Precision Under Model Misspecification, Donald W.K. Andrews, Soonwoo Kwon
Inference In Moment Inequality Models That Is Robust To Spurious Precision Under Model Misspecification, Donald W.K. Andrews, Soonwoo Kwon
Cowles Foundation Discussion Papers
Standard tests and confidence sets in the moment inequality literature are not robust to model misspecification in the sense that they exhibit spurious precision when the identified set is empty. This paper introduces tests and confidence sets that provide correct asymptotic inference for a pseudo-true parameter in such scenarios, and hence, do not suffer from spurious precision.
Inference In Moment Inequality Models That Is Robust To Spurious Precision Under Model Misspecification, Donald W.K. Andrews, Soonwoo Kwon
Inference In Moment Inequality Models That Is Robust To Spurious Precision Under Model Misspecification, Donald W.K. Andrews, Soonwoo Kwon
Cowles Foundation Discussion Papers
Standard tests and confidence sets in the moment inequality literature are not robust to model misspecification in the sense that they exhibit spurious precision when the identified set is empty. This paper introduces tests and confidence sets that provide correct asymptotic inference for a pseudo-true parameter in such scenarios, and hence, do not suffer from spurious precision.
Misspecified Moment Inequality Models: Inference And Diagnostics, Donald W.K. Andrews, Soonwoo Kwon
Misspecified Moment Inequality Models: Inference And Diagnostics, Donald W.K. Andrews, Soonwoo Kwon
Cowles Foundation Discussion Papers
This paper is concerned with possible model misspecification in moment inequality models. Two issues are addressed. First, standard tests and confidence sets for the true parameter in the moment inequality literature are not robust to model misspecification in the sense that they exhibit spurious precision when the identified set is empty. This paper introduces tests and confidence sets that provide correct asymptotic inference for a pseudo-true parameter in such scenarios, and hence, do not suffer from spurious precision. Second, specification tests have relatively low power against a range of misspecified models. Thus, failure to reject the null of correct specification …
Misspecified Moment Inequality Models: Inference And Diagnostics, Donald W.K. Andrews, Soonwoo Kwon
Misspecified Moment Inequality Models: Inference And Diagnostics, Donald W.K. Andrews, Soonwoo Kwon
Cowles Foundation Discussion Papers
This paper is concerned with possible model misspecification in moment inequality models. Two issues are addressed. First, standard tests and confidence sets for the true parameter in the moment inequality literature are not robust to model misspecification in the sense that they exhibit spurious precision when the identified set is empty. This paper introduces tests and confidence sets that provide correct asymptotic inference for a pseudo-true parameter in such scenarios, and hence, do not suffer from spurious precision. Second, specification tests have relatively low power against a range of misspecified models. Thus, failure to reject the null of correct specification …
A Note On Minimax Testing And Confidence Intervals In Moment Inequality Models, Timothy B. Armstrong
A Note On Minimax Testing And Confidence Intervals In Moment Inequality Models, Timothy B. Armstrong
Cowles Foundation Discussion Papers
This note uses a simple example to show how moment inequality models used in the empirical economics literature lead to general minimax relative efficiency comparisons. The main point is that such models involve inference on a low dimensional parameter, which leads naturally to a definition of “distance” that, in full generality, would be arbitrary in minimax testing problems. This definition of distance is justified by the fact that it leads to a duality between minimaxity of confidence intervals and tests, which does not hold for other definitions of distance. Thus, the use of moment inequalities for inference in a low …
On The Choice Of Test Statistic For Conditional Moment Inequalities, Timothy B. Armstrong
On The Choice Of Test Statistic For Conditional Moment Inequalities, Timothy B. Armstrong
Cowles Foundation Discussion Papers
This paper derives asymptotic power functions for Cramer-von Mises (CvM) style tests for conditional moment inequality models in the set identified case. Combined with power results for Kolmogorov-Smirnov (KS) tests, these results can be used to choose the optimal test statistic, weighting function and, for tests based on kernel estimates, kernel bandwidth. The results show that KS tests are preferred to CvM tests, and that a truncated variance weighting is preferred to bounded weightings under a minimax criterion, and for a class of alternatives that arises naturally in these models. The results also provide insight into how moment selection and …
On The Choice Of Test Statistic For Conditional Moment Inequalities, Timothy B. Armstrong
On The Choice Of Test Statistic For Conditional Moment Inequalities, Timothy B. Armstrong
Cowles Foundation Discussion Papers
This paper derives asymptotic approximations to the power of Cramer-von Mises (CvM) style tests for inference on a finite dimensional parameter defined by conditional moment inequalities in the case where the parameter is set identified. Combined with power results for Kolmogorov-Smirnov (KS) tests, these results can be used to choose the optimal test statistic, weighting function and, for tests based on kernel estimates, kernel bandwidth. The results show that, in the setting considered here, KS tests are preferred to CvM tests, and that a truncated variance weighting is preferred to bounded weightings.
On The Choice Of Test Statistic For Conditional Moment Inequalities, Timothy B. Armstrong
On The Choice Of Test Statistic For Conditional Moment Inequalities, Timothy B. Armstrong
Cowles Foundation Discussion Papers
This paper derives asymptotic power functions for Cramer-von Mises (CvM) style tests for inference on a finite dimensional parameter defined by conditional moment inequalities in the case where the parameter is set identified. Combined with power results for Kolmogorov-Smirnov (KS) tests, these results can be used to choose the optimal test statistic, weighting function and, for tests based on kernel estimates, kernel bandwidth. The results show that KS tests are preferred to CvM tests, and that a truncated variance weighting is preferred to bounded weightings under a minimax criterion, and for a class of alternatives that arises naturally in these …
Multiscale Adaptive Inference On Conditional Moment Inequalities, Timothy B. Armstrong, Hock Peng Chan
Multiscale Adaptive Inference On Conditional Moment Inequalities, Timothy B. Armstrong, Hock Peng Chan
Cowles Foundation Discussion Papers
This paper considers inference for conditional moment inequality models using a multiscale statistic. We derive the asymptotic distribution of this test statistic and use the result to propose feasible critical values that have a simple analytic formula. We also propose critical values based on a modified bootstrap procedure and prove their asymptotic validity. The asymptotic distribution is extreme value, and the proof uses new techniques to overcome several technical obstacles. We provide power results that show that our test detects local alternatives that approach the identified set at the best possible rate under a set of conditions that hold generically …
Multiscale Adaptive Inference On Conditional Moment Inequalities, Timothy B. Armstrong, Hock Peng Chan
Multiscale Adaptive Inference On Conditional Moment Inequalities, Timothy B. Armstrong, Hock Peng Chan
Cowles Foundation Discussion Papers
This paper considers inference for conditional moment inequality models using a multiscale statistic. We derive the asymptotic distribution of this test statistic and use the result to propose feasible critical values that have a simple analytic formula, and to prove the asymptotic validity of a modified bootstrap procedure. The asymptotic distribution is extreme value, and the proof uses new techniques to overcome several technical obstacles. The test detects local alternatives that approach the identified set at the best rate in a broad class of models, and is adaptive to the smoothness properties of the data generating process. Our results also …
Multiscale Adaptive Inference On Conditional Moment Inequalities, Timothy B. Armstrong, Hock Peng Chan
Multiscale Adaptive Inference On Conditional Moment Inequalities, Timothy B. Armstrong, Hock Peng Chan
Cowles Foundation Discussion Papers
This paper considers inference for conditional moment inequality models using a multiscale statistic. We derive the asymptotic distribution of this test statistic and use the result to propose feasible critical values that have a simple analytic formula, and to prove the asymptotic validity of a modified bootstrap procedure. The asymptotic distribution is extreme value, and the proof uses new techniques to overcome several technical obstacles. The test detects local alternatives that approach the identified set at the best rate among available tests in a broad class of models, and is adaptive to the smoothness properties of the data generating process. …
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.
Inference Based On Conditional Moment Inequalities, Donald W.K. Andrews, Xiaoxia Shi
Inference Based On Conditional Moment Inequalities, Donald W.K. Andrews, Xiaoxia Shi
Cowles Foundation Discussion Papers
In this paper, we propose an instrumental variable approach to constructing confidence sets (CS’s) for the true parameter in models defined by conditional moment inequalities/equalities. We show that by properly choosing instrument functions, one can transform conditional moment inequalities/equalities into unconditional ones without losing identification power. Based on the unconditional moment inequalities/equalities, we construct CS’s by inverting Cramér-von Mises-type or Kolmogorov-Smirnov-type tests. Critical values are obtained using generalized moment selection (GMS) procedures. We show that the proposed CS’s have correct uniform asymptotic coverage probabilities. New methods are required to establish these results because an infinite-dimensional nuisance parameter affects the asymptotic …
Inference Based On Conditional Moment Inequalities, Donald W.K. Andrews, Xiaoxia Shi
Inference Based On Conditional Moment Inequalities, Donald W.K. Andrews, Xiaoxia Shi
Cowles Foundation Discussion Papers
In this paper, we propose an instrumental variable approach to constructing confidence sets (CS’s) for the true parameter in models defined by conditional moment inequalities/equalities. We show that by properly choosing instrument functions, one can transform conditional moment inequalities/equalities into unconditional ones without losing identification power. Based on the unconditional moment inequalities/equalities, we construct CS’s by inverting Cramér–von Mises-type or Kolmogorov–Smirnov-type tests. Critical values are obtained using generalized moment selection (GMS) procedures. We show that the proposed CS’s have correct uniform asymptotic coverage probabilities. New methods are required to establish these results because an infinite-dimensional nuisance parameter affects the asymptotic …
Inference Based On Conditional Moment Inequalities, Donald W.K. Andrews, Xiaoxia Shi
Inference Based On Conditional Moment Inequalities, Donald W.K. Andrews, Xiaoxia Shi
Cowles Foundation Discussion Papers
In this paper, we propose an instrumental variable approach to constructing confidence sets (CS’s) for the true parameter in models defined by conditional moment inequalities/equalities. We show that by properly choosing instrument functions, one can transform conditional moment inequalities/equalities into unconditional ones without losing identification power. Based on the unconditional moment inequalities/equalities, we construct CS’s by inverting Cramér-von Mises-type or Kolmogorov-Smirnov-type tests. Critical values are obtained using generalized moment selection (GMS) procedures. We show that the proposed CS’s have correct uniform asymptotic coverage probabilities. New methods are required to establish these results because an infinite-dimensional nuisance parameter affects the asymptotic …
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.
Inference For Parameters Defined By Moment Inequalities Using Generalized Moment Selection, Donald W.K. Andrews, Patrik Guggenberger
Inference For Parameters Defined By Moment Inequalities Using Generalized Moment Selection, Donald W.K. Andrews, Patrik Guggenberger
Cowles Foundation Discussion Papers
The topic of this paper is inference in models in which parameters are defined by moment inequalities and/or equalities. The parameters may or may not be identified. This paper introduces a new class of confidence sets and tests based on generalized moment selection (GMS). GMS procedures are shown to have correct asymptotic size in a uniform sense and are shown not to be asymptotically conservative. The power of GMS tests is compared to that of subsampling, m out of n bootstrap, and “plug-in asymptotic” (PA) tests. The latter three procedures are the only general procedures in the literature that have …
Validity Of Subsampling And ‘Plug-In Asymptotic’ Inference For Parameters Defined By Moment Inequalities, Donald W.K. Andrews, Patrik Guggenberger
Validity Of Subsampling And ‘Plug-In Asymptotic’ Inference For Parameters Defined By Moment Inequalities, Donald W.K. Andrews, Patrik Guggenberger
Cowles Foundation Discussion Papers
This paper considers inference for parameters defined by moment inequalities and equalities. The parameters need not be identified. For a specified class of test statistics, this paper establishes the uniform asymptotic validity of subsampling, m out of n bootstrap, and “plug-in asymptotic” tests and confidence intervals for such parameters. Establishing uniform asymptotic validity is crucial in moment inequality problems because the test statistics of interest have discontinuities in their pointwise asymptotic distributions. The size results are quite general because they hold without specifying the particular form of the moment conditions — only 2 + δ moments finite are required. The …