Identification of Mixtures of Dynamic Discrete Choices
Author | : Ayden Higgins |
Publisher | : |
Total Pages | : |
Release | : 2021 |
Genre | : |
ISBN | : |
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Author | : Ayden Higgins |
Publisher | : |
Total Pages | : |
Release | : 2021 |
Genre | : |
ISBN | : |
Author | : Hiroyuki Kasahara |
Publisher | : |
Total Pages | : 45 |
Release | : 2006 |
Genre | : Mixture distributions (Probability theory) |
ISBN | : 9780771428081 |
In dynamic discrete choice analysis, controlling for unobserved heterogeneity is an im- portant issue, and finite mixture models provide flexible ways to account for unobserved heterogeneity. This paper studies nonparametric identifiability of type probabilities and type-specific component distributions in finite mixture models of dynamic discrete choices. We derive sufficient conditions for nonparametric identification for various finite mixture models of dynamic discrete choices used in applied work. Three elements emerge as the important determinants of identification; the time-dimension of panel data, the number of values the covariates can take, and the heterogeneity of the response of different types to changes in the covariates. For example, in a simple case, a time-dimension of T = 3 is sufficient for identification, provided that the number of values the covariates can take is no smaller than the number of types, and that the changes in the covariates induce sufficiently heterogeneous variations in the choice probabilities across types. Type-specific components are identifiable even when state dependence is present as long as the panel has a moderate time-dimension ( T {u2265} 6). We also develop a series logit estimator for finite mixture models of dynamic discrete choices and derive its convergence rate.
Author | : Thierry Magnac |
Publisher | : |
Total Pages | : 0 |
Release | : 2013 |
Genre | : |
ISBN | : |
In this paper, we analyse the non parametric identification of dynamic discrete choice models using short-panel data. Our identification methodology is based on the ideas explored in the seminal paper of Hotz and Miller (1993) that Bellman equations can be interpreted as moment conditions. We derive the exact degree of underidentification of these models both in the case where random shocks on preferences are independent over time and in a case with correlated fixed effects. We investigate the necessity and power of various identifying restrictions.
Author | : Chao Wang |
Publisher | : |
Total Pages | : 0 |
Release | : 2022 |
Genre | : |
ISBN | : |
Author | : Jaap H. Abbring |
Publisher | : |
Total Pages | : 0 |
Release | : 2020 |
Genre | : |
ISBN | : |
Fang and Wang's (2015) Proposition 2 claims generic identification of a dynamic discrete choice model with hyperbolic discounting under exclusion restrictions. We note that Proposition 2 uses a definition of “generic” that does not preclude that a generically identified model is nowhere identified. We provide two examples of models that are generically identified under this definition, but that are, respectively, everywhere and nowhere identified. We then show that the proof of Proposition 2 is incorrect and incomplete. We conclude that Proposition 2 has no implications for identification of the dynamic discrete choice model and suggest alternative approaches to its identification.
Author | : Jaap H. Abbring |
Publisher | : |
Total Pages | : 39 |
Release | : 2019 |
Genre | : |
ISBN | : |
Empirical research often cites observed choice responses to variation that shifts expected discounted future utilities, but not current utilities, as an intuitive source of information on time preferences. We study the identification of dynamic discrete choice models under such economically motivated exclusion restrictions on primitive utilities. We show that each exclusion restriction leads to an easily interpretable moment condition with the discount factor as the only unknown parameter. The identified set of discount factors that solves this condition is finite, but not necessarily a singleton. Consequently, in contrast to common intuition, an exclusion restriction does not in general give point identification. Finally, we show that exclusion restrictions have nontrivial empirical content: The implied moment conditions impose restrictions on choices that are absent from the unconstrained model.
Author | : Christophe Alain Bruneel-Zupanc |
Publisher | : |
Total Pages | : |
Release | : 2021 |
Genre | : |
ISBN | : |
Author | : Myrto Kalouptsidi |
Publisher | : |
Total Pages | : |
Release | : 2015 |
Genre | : |
ISBN | : |
Dynamic discrete choice models (DDC) are not identified nonparametrically. However, the non-identification of DDC models does not necessarily imply non-identification of counterfactuals of interest. Using a novel approach that can accommodate both nonparametric and restricted payoff functions, we provide necessary and sufficient conditions for the identification of counterfactual behavior and welfare for a broad class of counterfactuals. The conditions are simple to check and can be applied to virtually all counterfactuals in the DDC literature. To explore the robustness of counterfactual results to model restrictions in practice, we consider a numerical example of a monopolist's entry problem, as well as an empirical model of agricultural land use. In each case, we provide examples of both identified and non-identified counterfactuals of interest.
Author | : James Joseph Heckman |
Publisher | : |
Total Pages | : 91 |
Release | : 2005 |
Genre | : Social sciences |
ISBN | : |
This paper considers semiparametric identification of structural dynamic discrete choice models and models for dynamic treatment effects. Time to treatment and counterfactual outcomes associated with treatment times are jointly analyzed. We examine the implicit assumptions of the dynamic treatment model using the structural model as a benchmark. For the structural model we show the gains from using cross equation restrictions connecting choices to associated measurements and outcomes. In the dynamic discrete choice model, we identify both subjective and objective outcomes, distinguishing ex post and ex ante outcomes. We show how to identify agent information sets
Author | : David A. Hensher |
Publisher | : Routledge |
Total Pages | : 485 |
Release | : 2018-04-09 |
Genre | : Business & Economics |
ISBN | : 1351140752 |
Originally published in 1981. Discrete-choice modelling is an area of econometrics where significant advances have been made at the research level. This book presents an overview of these advances, explaining the theory underlying the model, and explores its various applications. It shows how operational choice models can be used, and how they are particularly useful for a better understanding of consumer demand theory. It discusses particular problems connected with the model and its use, and reports on the authors’ own empirical research. This is a comprehensive survey of research developments in discrete choice modelling and its applications.