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Essays on Multinomial Choice Models.

Ouyang, Fu.

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Essays on Multinomial Choice Models.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Essays on Multinomial Choice Models./
Author:	Ouyang, Fu.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	184 p.
Notes:	Source: Dissertation Abstracts International, Volume: 78-09(E), Section: A.
Contained By:	Dissertation Abstracts International78-09A(E).
Subject:	Economic theory. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10261440
ISBN:	9781369724585

Essays on Multinomial Choice Models.
Ouyang, Fu.

Essays on Multinomial Choice Models. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 184 p.

Source: Dissertation Abstracts International, Volume: 78-09(E), Section: A.

Thesis (Ph.D.)--Duke University, 2017.

This item is not available from ProQuest Dissertations & Theses.

My dissertation contains three chapters which develop new identification and estimation methods for multinomial choice models in both cross-sectional and panel data settings. In the first chapter, I propose a new semiparametric identification and estimation approach to multinomial choice models using cross-sectional data. The approach relies on the rank-order property proposed by Manski (1975) and employed by recent studies such as Fox (2007) and Yan (2013), which is a distribution-free restriction on the random utility framework underlying a multinomial choice model. From the rank-order property, a novel reparameterization provides a multivariate nonlinear least squares (population) criterion identifying the structural parameters. This identification result then motivates a sieve-based estimation procedure, which is the first in the semiparametric literature to allow joint estimation of regression coefficients and reduced-form parameters such as choice probabilities and marginal effects. Asymptotic properties of two functional estimators are developed. A Monte Carlo study indicates that these functional estimators perform well in finite samples. I illustrate the implementation of the estimation procedure via estimating a model of college major choice using UCOP data of 1998-2003. As extensions, I also propose estimators for the model using a choice-based sample and the model with ranking information.

ISBN: 9781369724585Subjects--Topical Terms:

1556984
Economic theory.

Essays on Multinomial Choice Models.
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245 1 0 $a Essays on Multinomial Choice Models.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 184 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-09(E), Section: A.
500 $a Advisers: Shakeeb Khan; Matthew Masten.
502 $a Thesis (Ph.D.)--Duke University, 2017.
506 $a This item is not available from ProQuest Dissertations & Theses.
520 $a My dissertation contains three chapters which develop new identification and estimation methods for multinomial choice models in both cross-sectional and panel data settings. In the first chapter, I propose a new semiparametric identification and estimation approach to multinomial choice models using cross-sectional data. The approach relies on the rank-order property proposed by Manski (1975) and employed by recent studies such as Fox (2007) and Yan (2013), which is a distribution-free restriction on the random utility framework underlying a multinomial choice model. From the rank-order property, a novel reparameterization provides a multivariate nonlinear least squares (population) criterion identifying the structural parameters. This identification result then motivates a sieve-based estimation procedure, which is the first in the semiparametric literature to allow joint estimation of regression coefficients and reduced-form parameters such as choice probabilities and marginal effects. Asymptotic properties of two functional estimators are developed. A Monte Carlo study indicates that these functional estimators perform well in finite samples. I illustrate the implementation of the estimation procedure via estimating a model of college major choice using UCOP data of 1998-2003. As extensions, I also propose estimators for the model using a choice-based sample and the model with ranking information.
520 $a The estimation problem in the second chapter is motivated by the local nonlinear least squares (LNLS) estimation of preference parameters (regression coefficients) in the multinomial choice model under uncertainty in which the decision rule is affected by conditional expectations. I propose a two-stage LNLS estimation procedure for the preference parameters. In the first stage, conditional expectations are estimated nonparametrically. Then, in the second stage, the preference parameters are estimated by the LNLS estimator of multinomial choice model, using the choice data and first-stage estimates. The two-stage estimator has the advantage of being easily implementable using standard software packages. In this chapter, I establish consistency of the two-stage LNLS estimator. Monte Carlo simulation results illustrate that the proposed two-stage LNLS estimator performs well in finite sample.
520 $a The third chapter is a part of a co-authored project with Shakeeb Khan and Elie Tamer. In this work, we consider identification, estimation, and inference on regression coefficients in semiparametric multinomial response models. Our identification result is constructive and estimation is based on a localized rank objective function, loosely analogous to that used in Abrevaya et al. (2010). We show this achieves sharp identification which is in contrast to existing procedures in the literature such as, for example, Ahn et al. (2015). In that sense, our procedure is adaptive (Khan and Tamer (2009)) in the sense that it provides an estimator of the sharp set when point identification does not hold, and a consistent point estimator when it does. Furthermore, our rank procedure extends to panel data settings for inference in models with fixed effects, including dynamic panel models with lagged dependent variables as covariates. A simulation study establishes adequate nite sample properties of our new procedures.
590 $a School code: 0066.
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710 2 $a Duke University. $b Economics. $3 1018522
773 0 $t Dissertation Abstracts International $g 78-09A(E).
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