Heterogeneous variable selection in nonlinear panel data models: A semiparametric Bayesian approach

Anoek Castelein, Dennis Fok and Richard Paap
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Anoek Castelein: Erasmus University Rotterdam

No 20-061/III, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: In this paper, we develop a general method for heterogeneous variable selection in Bayesian nonlinear panel data models. Heterogeneous variable selection refers to the possibility that subsets of units are unaffected by certain variables. It may be present in applications as diverse as health treatments, consumer choice-making, macroeconomics, and operations research. Our method additionally allows for other forms of cross-sectional heterogeneity. We consider a two-group approach for the model's unit-specific parameters: each unit-specific parameter is either equal to zero (heterogeneous variable selection) or comes from a Dirichlet process (DP) mixture of multivariate normals (other cross-sectional heterogeneity). We develop our approach for general nonlinear panel data models, encompassing multinomial logit and probit models, poisson and negative binomial count models, exponential models, among many others. For inference, we develop an efficient Bayesian MCMC sampler. In a Monte Carlo study, we find that our approach is able to capture heterogeneous variable selection whereas a ``standard'' DP mixture is not. In an empirical application, we find that accounting for heterogeneous variable selection and non-normality of the continuous heterogeneity leads to an improved in-sample and out-of-sample performance and interesting insights. These findings illustrate the usefulness of our approach.

Keywords: Individualized variable selection; Dirichlet process; Stochastic search; Heterogeneity; Attribute non-attendance; Feature selection; Bayesian (search for similar items in EconPapers)
JEL-codes: C11 C23 (search for similar items in EconPapers)
Date: 2020-09-22
New Economics Papers: this item is included in nep-dcm, nep-ecm and nep-ore
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