Estimating Semi-Parametric Panel Multinomial Choice Models Using Cyclic Monotonicity PDF Download
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Author: Xiaoxia Shi
Publisher:
ISBN:
Category :
Languages : en
Pages : 32
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Book Description
This paper proposes a new semi-parametric identification and estimation approach to multinomial choice models in a panel data setting with individual fixed effects. Our approach is based on cyclic monotonicity, which is a defining convex-analytic feature of the random utility framework underlying multinomial choice models. From the cyclic monotonicity property, we derive identifying inequalities without requiring any shape restrictions for the distribution of the random utility shocks. These inequalities point identify model parameters under straightforward assumptions on the covariates. We propose a consistent estimator based on these inequalities.
Author: Xiaoxia Shi
Publisher:
ISBN:
Category :
Languages : en
Pages : 32
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Book Description
This paper proposes a new semi-parametric identification and estimation approach to multinomial choice models in a panel data setting with individual fixed effects. Our approach is based on cyclic monotonicity, which is a defining convex-analytic feature of the random utility framework underlying multinomial choice models. From the cyclic monotonicity property, we derive identifying inequalities without requiring any shape restrictions for the distribution of the random utility shocks. These inequalities point identify model parameters under straightforward assumptions on the covariates. We propose a consistent estimator based on these inequalities.
Author: Khai Chiong
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ISBN:
Category :
Languages : en
Pages : 34
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We introduce sparse random projection, an important tool from machine learning, for the estimation of discrete-choice models with high-dimensional choice sets. First, the high-dimensional data are compressed into a lower-dimensional Euclidean space using random projections. In the second step, estimation proceeds using the cyclic monotonicity inequalities implied by the multinomial choice model; the estimation procedure is semi-parametric and does not require explicit distributional assumptions to be made regarding the random utility errors. The random projection procedure is justified via the Johnson-Lindenstrauss Lemma: - the pairwise distances between data points are preserved during data compression, which we exploit to show convergence of our estimator. The estimator works well in computational simulation and in a application to a real-world supermarket scanner dataset.
Author:
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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: Gabriel A. Picone
Publisher:
ISBN:
Category : Consumers' preferences
Languages : en
Pages : 176
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Author: Rafic H. Fahs
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ISBN:
Category : Economics
Languages : en
Pages : 276
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Author: Hashimzade, Nigar
Publisher: Edward Elgar Publishing
ISBN: 1788976487
Category : Business & Economics
Languages : en
Pages : 672
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Book Description
Written in a comprehensive yet accessible style, this Handbook introduces readers to a range of modern empirical methods with applications in microeconomics, illustrating how to use two of the most popular software packages, Stata and R, in microeconometric applications.
Author: Margarida Genius
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 274
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Author: Kazumitsu Nawata
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 66
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Author: Trueman Scott Thompson
Publisher:
ISBN:
Category :
Languages : en
Pages : 310
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Author: Weiren Wang
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 54
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