Semiparametric Estimation of Binary Choice Models PDF Download
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Author: Kazumitsu Nawata
Publisher:
ISBN:
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Languages : en
Pages : 26
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Author: Kazumitsu Nawata
Publisher:
ISBN:
Category :
Languages : en
Pages : 26
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Book Description
Author: Kazumitsu Nawata
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 66
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Author: Kazumitsu Nawata
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Margarida Genius
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Category : Estimation theory
Languages : en
Pages : 274
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Author: Fu Ouyang
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Category :
Languages : en
Pages :
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Author: Chuan-Zhong Li
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Category :
Languages : en
Pages : 19
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Author: Joakim Westerlund
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Category :
Languages : en
Pages : 0
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One of the most cited studies within the field of binary choice models is that of Klein and Spady (1993), in which the authors propose a semiparametric estimator for use when the distribution of the error term is unknown. However, although theoretically appealing, the estimator has been found to be difficult to implement, and therefore not very attractive from an applied point of view. The current study offers an indirect inference-based solution to this problem. The new estimator is not only simple with good small-sample properties, but also consistent and asymptotically normal.
Author: Trueman Scott Thompson
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Category :
Languages : en
Pages : 310
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Author:
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Category :
Languages : en
Pages : 0
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In the first chapter I propose a semiparametric estimator that allows for a flexible form of heteroskedasticity for multinomial discrete choice (MDC) models. Despite being semiparametric, the rate of convergence of the smoothed maximum score (SMS) estimator is not affected by the number of alternative choices. I show the strong consistency and asymptotic normality of the proposed estimator. The rate of convergence of the SMS estimator for MDC models can be made arbitrarily close to the inverse of the square root of the sample size, which is the same as the rate of convergence of Horowitz's (1992) SMS estimator for the binary response model. Monte Carlo experiments provide evidence that the proposed estimator has a smaller mean squared error than both the conditional logit estimator and the maximum score estimator when heteroskedasticity exists. I apply the SMS estimator to study the college decisions of high school graduates using a subset of Chilean data from 2011. The estimation results of the SMS estimator differ significantly from the results of the conditional logit estimator, which suggests possible misspecification of parametric models and the usefulness of considering the SMS estimator as an alternative for estimating MDC models. Many MDC applications include potentially endogenous regressors. To allow for endogeneity, in the second chapter I propose a two-stage instrumental variables estimator where the endogenous variable is replaced by a linear estimate, and then the preference parameters in the MDC equation are estimated by the SMS estimator described in the first chapter. In neither stage do I specify the distribution of the error terms, so this two-stage estimation method is semiparametric. This estimator is a generalization of the estimator proposed by Fox (2007). Fox suggests applying the maximum score estimator in the second stage of estimation. This chapter is the first to derive the statistical properties of an estimator allowing for endogeneity in this semiparametric setting. The two-stage instrument variables estimator is consistent when the linear function of instrument variables and other covariates can rank order the choice probabilities. The second chapter also provides results of some Monte Carlo experiments.
Author: Keith Allan Heyen
Publisher:
ISBN:
Category :
Languages : en
Pages : 302
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