scholarly journals Nonparametric Identification of Dynamic Games with Multiple Equilibria and Unobserved Heterogeneity

2016 ◽  
Author(s):  
Ruli Xiao
Keyword(s):  
Dynamic Games ◽  
Multiple Equilibria ◽  
Unobserved Heterogeneity ◽  
Nonparametric Identification

scholarly journals Identification of games of incomplete information with multiple equilibria and unobserved heterogeneity

10.3982/qe666 ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 1659-1701 ◽  
Author(s):  
Victor Aguirregabiria ◽  
Pedro Mira
Keyword(s):  
Private Information ◽  
Multiple Equilibria ◽  
Unobserved Heterogeneity ◽  
Common Knowledge ◽  
Sufficient Conditions ◽  
Finite Mixture Model ◽  
Payoff Function ◽  
Finite Mixture ◽  
Relevant Variables ◽  
Necessary And Sufficient

This paper deals with identification of discrete games of incomplete information when we allow for three types of unobservables: payoff‐relevant variables, both players' private information and common knowledge, and nonpayoff‐relevant variables that determine the selection between multiple equilibria. The specification of the payoff function and the distributions of the common knowledge unobservables is nonparametric with finite support (i.e., finite mixture model). We provide necessary and sufficient conditions for the identification of all the primitives of the model. Two types of conditions play a key role in our identification results: independence between players' private information, and an exclusion restriction in the payoff function. When using a sequential identification approach, we find that the up‐to‐label‐swapping identification of the finite mixture model in the first step creates a problem in the identification of the payoff function in the second step: unobserved types have to be correctly matched across different values of observable explanatory variables. We show that this matching‐type problem appears in the sequential estimation of other structural models with nonparametric finite mixtures. We derive necessary and sufficient conditions for identification, and show that additive separability of unobserved heterogeneity in the payoff function is a sufficient condition to deal with this problem. We also compare sequential and joint identification approaches.

SEMI-NONPARAMETRIC INTERVAL-CENSORED MIXED PROPORTIONAL HAZARD MODELS: IDENTIFICATION AND CONSISTENCY RESULTS

2008 ◽  
Vol 24 (3) ◽  
pp. 749-794 ◽  
Author(s):  
Herman J. Bierens
Keyword(s):  
Legendre Polynomials ◽  
Unobserved Heterogeneity ◽  
Unit Interval ◽  
Hazard Models ◽  
Nonparametric Identification ◽  
Proportional Hazard ◽  
Proportional Hazard Models ◽  
Consistency Results ◽  
Interval Censored ◽  
Mixed Proportional Hazard Models

In this paper I propose estimating distributions on the unit interval semi-nonparametrically using orthonormal Legendre polynomials. This approach will be applied to the interval-censored mixed proportional hazard (ICMPH) model, where the distribution of the unobserved heterogeneity is modeled semi-nonparametrically. Various conditions for the nonparametric identification of the ICMPH model are derived. I will prove general consistency results for M-estimators of (partly) non-euclidean parameters under weak and easy-to-verify conditions and specialize these results to sieve estimators. Special attention is paid to the case where the support of the covariates is finite.

University Dropout

2008 ◽  
Vol 29 (3) ◽  
pp. 134-147 ◽  
Author(s):  
Manuel C. Voelkle ◽  
Nicolas Sander
Keyword(s):  
Grade Point Average ◽  
Structural Equation ◽  
Unobserved Heterogeneity ◽  
School Grade ◽  
Grade Point ◽  
High School Grade ◽  
Hands On ◽  
University Dropout ◽  
The Individual ◽  
A Prospective Study

University dropout is a politically and economically important factor. While a number of studies address this issue cross-sectionally by analyzing different cohorts, or retrospectively via questionnaires, few of them are truly longitudinal and focus on the individual as the unit of interest. In contrast to these studies, an individual differences perspective is adopted in the present paper. For this purpose, a hands-on introduction to a recently proposed structural equation (SEM) approach to discrete-time survival analysis is provided ( Muthén & Masyn, 2005 ). In a next step, a prospective study with N = 1096 students, observed across four semesters, is introduced. As expected, average university grade proved to be an important predictor of future dropout, while high-school grade-point average (GPA) yielded no incremental predictive validity but was completely mediated by university grade. Accounting for unobserved heterogeneity, three latent classes could be identified with differential predictor-criterion relations, suggesting the need to pay closer attention to the composition of the student population.

Sign in / Sign up

Export Citation Format

Share Document