Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/229681
Authors: 
Castelein, Anoek
Fok, Dennis
Paap, Richard
Year of Publication: 
2020
Series/Report no.: 
Tinbergen Institute Discussion Paper No. TI 2020-061/III
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.
Subjects: 
Individualized variable selection
Dirichlet process
Stochastic search
Heterogeneity
Attribute non-attendance
Feature selection
Bayesian
JEL: 
C23
C11
Document Type: 
Working Paper

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