scholarly journals Accounting for Expectational and Structural Error in Binary Choice Problems: A Moment Inequality Approach

2013 ◽  
Author(s):  
Michael Dickstein ◽  
Eduardo Morales
Keyword(s):  
Binary Choice ◽  
Moment Inequality ◽  
Structural Error

Learning to Compete, Coordinate and Cooperate: Social Interaction in Sequential Binary Choice

2014 ◽  
Author(s):  
Christin Schulze ◽  
Ben Newell
Keyword(s):  
Social Interaction ◽  
Binary Choice

A binary choice model with partial observability for panel data

2020 ◽  
Vol 15 (4) ◽  
pp. 315-322
Author(s):  
Ekaterina Batalova ◽  
Kirill Furmanov ◽  
Ekaterina Shelkova
Keyword(s):  
Job Search ◽  
Latent Class ◽  
Choice Model ◽  
Maximum Likelihood Estimates ◽  
Binary Choice ◽  
Panel Model ◽  
Binary Response Variable ◽  
Monitoring Survey ◽  
Binary Choice Model ◽  
Time Invariant

We consider a panel model with a binary response variable that is a product of two unobservable factors, each determined by a separate binary choice equation. One of these factors is assumed to be time-invariant and may be interpreted as a latent class indicator. A simulation study shows that maximum likelihood estimates from even the shortest panel are much more reliable than those obtained from a cross-section. As an illustrative example, the model is applied to Russian Longitudinal Monitoring Survey data to estimate a proportion of the non-employed population who are participating in job search.

scholarly journals A Fast Estimator for Binary Choice Models with Spatial, Temporal, and Spatio-Temporal Interdependence

2021 ◽  
pp. 1-7
Author(s):  
Julian Wucherpfennig ◽  
Aya Kachi ◽  
Nils-Christian Bormann ◽  
Philipp Hunziker
Keyword(s):  
Computational Cost ◽  
Choice Models ◽  
Reduced Form ◽  
Binary Choice ◽  
Monte Carlo Experiments ◽  
Binary Choice Models ◽  
Spatio Temporal ◽  
Computationally Intensive ◽  
Pseudo Maximum Likelihood ◽  
Parameter Values

Abstract Binary outcome models are frequently used in the social sciences and economics. However, such models are difficult to estimate with interdependent data structures, including spatial, temporal, and spatio-temporal autocorrelation because jointly determined error terms in the reduced-form specification are generally analytically intractable. To deal with this problem, simulation-based approaches have been proposed. However, these approaches (i) are computationally intensive and impractical for sizable datasets commonly used in contemporary research, and (ii) rarely address temporal interdependence. As a way forward, we demonstrate how to reduce the computational burden significantly by (i) introducing analytically-tractable pseudo maximum likelihood estimators for latent binary choice models that exhibit interdependence across space and time and by (ii) proposing an implementation strategy that increases computational efficiency considerably. Monte Carlo experiments show that our estimators recover the parameter values as good as commonly used estimation alternatives and require only a fraction of the computational cost.

scholarly journals Nash equilibria for non-binary choice rules

2006 ◽  
Vol 35 (3) ◽  
pp. 455-464 ◽  
Author(s):  
José C. R. Alcantud ◽  
Carlos Alós-Ferrer
Keyword(s):  
Nash Equilibria ◽  
Binary Choice

Preferences for skewness: evidence from a binary choice experiment

2011 ◽  
Vol 17 (7) ◽  
pp. 525-538 ◽  
Author(s):  
Tobias Brünner ◽  
René Levínský ◽  
Jianying Qiu
Keyword(s):  
Choice Experiment ◽  
Binary Choice

scholarly journals Rational and Boundedly Rational Behavior in a Binary Choice Sender–Receiver Game

2008 ◽  
Vol 52 (5) ◽  
pp. 665-686
Author(s):  
Massimiliano Landi ◽  
Domenico Colucci
Keyword(s):  
Binary Choice ◽  
Rational Behavior ◽  
Boundedly Rational Behavior
Sign in / Sign up

Export Citation Format

Share Document