617 research outputs found
Testing the null hypothesis of no regime switching with an application to GDP growth rates
This paper presents tests for the null hypothesis of no regime switching in Hamilton's (1989) regime switching model. The test procedures exploit similarities between regime switching models, autoregressions with measurement errors, and finite mixture models. The proposed tests are computationally simple and, contrary to likelihood based tests, have a standard distribution under the null. When the methodology is applied to US GDP growth rates, no strong evidence of regime switching is found.regime switching, LM tests, GMM, matching methods, GDP growth rates
Optimal Comparison of Misspecified Moment Restriction Models under a Chosen Measure of Fit
Suppose that the econometrician is interested in comparing two misspecified moment restriction models, where the comparison is performed in terms of some chosen measure of fit. This paper is concerned with describing an optimal test of the Vuong (1989) and Rivers and Vuong (2002) type null hypothesis that the two models are equivalent under the given measure of fit (the ranking may vary for different measures). We adopt the generalized Neyman-Pearson optimality criterion, which focuses on the decay rates of the type I and II error probabilities under fixed non-local alternatives, and derive an optimal but practically infeasible test. Then, as an illustration, by considering the model comparison hypothesis defined by the weighted Euclidean norm of moment restrictions, we propose a feasible approximate test statistic to the optimal one and study its asymptotic properties. Local power properties, one-sided test, and comparison under the generalized empirical likelihood-based measure of fit are also investigated. A simulation study illustrates that our approximate test is more powerful than the Rivers-Vuong test.Moment restriction, Model comparison, Misspecification, Generalized Neyman-Pearson optimality, Generalized method of moments
Supplement to "Quantile-Based Nonparametric Inference for First-Price Auctions"
This paper contains supplemental materials for Marmer and Shneyerov (2010). We discuss here how the approach developed in the aforementioned paper can be applied to conducting inference on the optimal reserve price in first-price auctions, report additional simulations results, and provide a detailed proof of the bootstrap result in Marmer and Shneyerov (2010).First-price auctions, independent private values, nonparametric estimation, kernel estimation, quantiles, optimal reserve price, bootstrap
Quantile-Based Nonparametric Inference for First-Price Auctions
We propose a quantile-based nonparametric approach to inference on the probability density function (PDF) of the private values in first-price sealed-bid auctions with independent private values. Our method of inference is based on a fully nonparametric kernel-based estimator of the quantiles and PDF of observable bids. Our estimator attains the optimal rate of Guerre, Perrigne, and Vuong (2000), and is also asymptotically normal with the appropriate choice of the bandwidth. As an application, we consider the problem of inference on the optimal reserve price.First-price auctions; independent private values; nonparametric estimation; kernel estimation; quantiles; optimal reserve price
Instrumental Variables Estimation and Weak-Identification-Robust Inference Based on a Conditional Quantile Restriction
Extending the L1-IV approach proposed by Sakata (1997, 2007), we develop a new method, named the -IV estimation, to estimate structural equations based on the conditional quantile restriction imposed on the error terms. We study the asymptotic behavior of the proposed estimator and show how to make statistical inferences on the regression parameters. Given practical importance of weak identification, a highlight of the paper is a proposal of a test robust to the weak identification. The statistics used in our method can be viewed as a natural counterpart of the Anderson and Rubin's (1949) statistic in the -IV estimation.quantile regression; instrumental variables; weak identification
Limit Theorems for Network Dependent Random Variables
This paper is concerned with cross-sectional dependence arising because
observations are interconnected through an observed network. Following Doukhan
and Louhichi (1999), we measure the strength of dependence by covariances of
nonlinearly transformed variables. We provide a law of large numbers and
central limit theorem for network dependent variables. We also provide a method
of calculating standard errors robust to general forms of network dependence.
For that purpose, we rely on a network heteroskedasticity and autocorrelation
consistent (HAC) variance estimator, and show its consistency. The results rely
on conditions characterized by tradeoffs between the rate of decay of
dependence across a network and network's denseness. Our approach can
accommodate data generated by network formation models, random fields on
graphs, conditional dependency graphs, and large functional-causal systems of
equations
Optimal Comparison of Misspecified Moment Restriction Models under a Chosen Measure of Fit
Abstract Suppose that the econometrician is interested in comparing two misspecified moment restriction models, where the comparison is performed in terms of some chosen measure of fit. This paper is concerned with describing an optimal test of the Vuong (1989) and Rivers and Vuong (2002) type null hypothesis that the two models are equivalent under the given measure of fit (the ranking may vary for different measures). We adopt the generalized Neyman-Pearson optimality criterion, which focuses on the decay rates of the type I and II error probabilities under fixed non-local alternatives, and derive an optimal but practically infeasible test. Then, as an illustration, by considering the model comparison hypothesis defined by the weighted Euclidean norm of moment restrictions, we propose a feasible approximate test statistic to the optimal one and study its asymptotic properties. Local power properties, one-sided test, and comparison under the generalized empirical likelihood-based measure of fit are also investigated. A simulation study illustrates that our approximate test is more powerful than the Rivers-Vuong test.Moment restriction; Model comparison; Misspecification; Generalized Neyman-Pearson optimality; Empirical likelihood; GMM
Quantile-Based Nonparametric Inference for First-Price Auctions
We propose a quantile-based nonparametric approach to inference on the probability density function (PDF) of the private values in first-price sealed-bid auctions with independent private values. Our method of inference is based on a fully nonparametric kernel-based estimator of the quantiles and PDF of observable bids. Our estimator attains the optimal rate of Guerre, Perrigne, and Vuong (2000), and is also asymptotically normal with the appropriate choice of the bandwidth.First-price auctions; independent private values; nonparametric estimation; kernel estimation; quantiles; optimal reserve price
Exactly Distribution-free Inference in Instrumental Variables Regression with Possibly Weak Instruments
This paper introduces a rank-based test for the instrumental variables regression model that dominates the Anderson-Rubin test in terms of finite sample size and asymptotic power in certain circumstances. The test has correct size for any distribution of the errors with weak or strong instruments. The test has noticeably higher power than the Anderson-Rubin test when the error distribution has thick tails and comparable power otherwise. Like the Anderson-Rubin test, the rank tests considered here perform best, relative to other available tests, in exactly-identified models.Aligned ranks, Anderson-Rubin statistic, categorical covariates, exact size, normal scores, rank test, weak instruments, Wilcoxon scores
Supplement to "What Model for Entry in First-Price Auctions? A Nonparametric Approach"
The paper contains supplemental material for Marmer, Shneyerov, and Xu (2010) "What Model for Entry in First-Price Auctions? A Nonparametric Approach."
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