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38
The Limiting Distribution of the Maximum Rank Correlation Estimator
 Econometrica
, 1993
"... Han’s maximum rank correlation (MRC) estimator is shown to be√ nconsistent and asymptotically normal. The proof rests on a general method for determining the asymptotic distribution of a maximization estimator, a simple Ustatistic decomposition, and a uniform bound for degenerate Uprocesses. A co ..."
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Cited by 71 (0 self)
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Han’s maximum rank correlation (MRC) estimator is shown to be√ nconsistent and asymptotically normal. The proof rests on a general method for determining the asymptotic distribution of a maximization estimator, a simple Ustatistic decomposition, and a uniform bound for degenerate Uprocesses. A consistent estimator of the asymptotic covariance matrix is provided, along with a result giving the explicit form of this matrix for any model within the scope of the MRC estimator. The latter result is applied to the binary choice model, and it is found that the MRC estimator does not achieve the semiparametric efficiency bound.
Semiparametric Estimation of a Simultaneous Game with Incomplete Information
 Journal of Econometrics
, 2010
"... We analyze a 2 × 2 simultaneous game. We start by showing that a likelihood function defined over the set of four observable outcomes and all possible variations of the game exists only if players have incomplete information. We assume a general incomplete information structure, where players ’ beli ..."
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Cited by 55 (8 self)
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We analyze a 2 × 2 simultaneous game. We start by showing that a likelihood function defined over the set of four observable outcomes and all possible variations of the game exists only if players have incomplete information. We assume a general incomplete information structure, where players ’ beliefs are conditioned on a vector of signals Z observable by the researcher but whose exact distribution is known only to the players. The resulting BayesianNash equilibrium (BNE) is characterized as a vector of conditional moment restrictions. We show how to exploit the information contained in these equilibrium conditions efficiently. The proposal takes the form of a twostep estimator. The first step estimates the unknown equilibrium beliefs using semiparametric restrictions analog to the population BNE conditions. The second step maximizes a trimmed loglikelihood function using the estimates from the first step as plugins for the unknown equilibrium beliefs. The trimming set is an interior subset of the support of Z where the BNE conditions have a unique solution. The resulting estimator of the vector of structural parameters ‘θ ’ is √ N−consistent and exploits all information in the model efficiently. We allow Z to
Quantile regression under random censoring
 Journal of Econometrics 109
"... Censored regression models have received a great deal of attention in both the theoretical and applied econometric literature. Most of the existing estimation procedures for either cross sectional or panel data models are designed only for models with ¯xed censoring. In this paper, a new procedure f ..."
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Cited by 31 (4 self)
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Censored regression models have received a great deal of attention in both the theoretical and applied econometric literature. Most of the existing estimation procedures for either cross sectional or panel data models are designed only for models with ¯xed censoring. In this paper, a new procedure for adapting these estimators designed for ¯xed censoring to models with random censorship is proposed. This procedure is then applied to the CLAD and quantile estimators of Powell(1984,1986a) to obtain an estimator of the regression coe±cients under a mild conditional quantile restriction on the error term that is applicable to samples exhibiting ¯xed or random censoring. The resulting estimator is shown to have desirable asymptotic properties, and performs well in a small scale simulation study.
Set Estimation and Inference with Models Characterized by Conditional Moment Inequalities
, 2008
"... This paper studies set estimation and inference of models characterized by conditional moment inequalities. A consistent set estimator and a con…dence set are provided in this paper. Previous studies on set estimation and inference with conditional moment inequalities typically use only a …nite set ..."
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Cited by 27 (0 self)
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This paper studies set estimation and inference of models characterized by conditional moment inequalities. A consistent set estimator and a con…dence set are provided in this paper. Previous studies on set estimation and inference with conditional moment inequalities typically use only a …nite set of moment inequalities implied by the conditional moment inequalities. We consider an alternative strategy that preserves all the information from the conditional moment inequalities. Potentially, this will enable us to obtain a smaller set estimator and a tighter con…dence region than those from other methods in the current literature, which lose some information by using only a subset of moment inequalties.
Nonparametric Estimation of Distributional Policy Effects
, 2008
"... PRELIMINARY VERSION. COMMENTS ARE WELCOME. This paper proposes a fully nonparametric procedure to evaluate the effect of a counterfactual change in the distribution of some covariates on the unconditional distribution of an outcome variable of interest. In contrast to other methods, we do not restri ..."
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Cited by 19 (3 self)
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PRELIMINARY VERSION. COMMENTS ARE WELCOME. This paper proposes a fully nonparametric procedure to evaluate the effect of a counterfactual change in the distribution of some covariates on the unconditional distribution of an outcome variable of interest. In contrast to other methods, we do not restrict attention to the effect on the mean. In particular, our method can be used to conduct inference on the change of the distribution function as a whole, its moments and quantiles, inequality measures such as the Lorenz curve or Gini coefficient, and to test for stochastic dominance. The practical applicability of our procedure is illustrated via a simulation study and an empirical application.
Testing Monotonicity Of Regression
, 1998
"... this article, we study this problem and construct asymptotically valid tests. Our test statistics are suitable functionals of a stochastic process which may be viewed as a local version of Kendall's tau statistic and have simple natural interpretations. The process involved is a degreetwo Upro ..."
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Cited by 19 (0 self)
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this article, we study this problem and construct asymptotically valid tests. Our test statistics are suitable functionals of a stochastic process which may be viewed as a local version of Kendall's tau statistic and have simple natural interpretations. The process involved is a degreetwo Uprocess, as in Nolan and Pollard (1987). The asymptotic behaviour of the test statistics are studied in three major steps: Approximation of the Uprocess by the empirical process defined by the H'ajek projection, strong approximation of the empirical process by a Gaussian process and finally the extreme value theory for stationary Gaussian processes. The paper is organized as follows. In Section 2, we introduce two different types of test statistics. We also formally describe the model and the hypothesis and explain the notation and regularity conditions in this section. In Section 3, we investigate the asymptotic behaviour of the Uprocess and establish the Gaussian process approximation. Section 4 is devoted to the study of the limiting distribution of the first test statistics using the extreme value theory for stationary Gaussian processes and the results of Section 3. In Section 5, we show that this test is consistent against all alternatives and also determine the minimal rate so that alternatives further apart than this rate can be effectively tested. The second test statistic is studied in Section 6. Technical proofs are presented in Section 7 and the appendix. 2. The Test Statistics
Estimation in Regression Models with Interval Censoring
, 1993
"... In this paper we discuss estimation in semiparametric regression models with interval censoring, with emphasis on estimation ofthe regression parameter B. The first section surveys some of the existing literature concerning these models and the various existing approaches to estimation, including a ..."
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Cited by 6 (3 self)
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In this paper we discuss estimation in semiparametric regression models with interval censoring, with emphasis on estimation ofthe regression parameter B. The first section surveys some of the existing literature concerning these models and the various existing approaches to estimation, including a selected subset of the enormous literature on the binary choice model used in econometrics. In section 2 we calculate efficient score functions and information bounds for the regression parameter B in the linear regression model with interval censoring, the binary choice model, and the Cox model with interval censoring. Profile likelihood approaches to maximum likelihood estimates are discussed and reviewed in section 3, and profile likelihod is used in section 4 to discuss maximum likelihood estimation for the linear regression model with interval censoring, and some of the remaining problems.