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Learning models : an assessment of progress, challenges, and new developments

Ching, Andrew; Erdem, Tülin; Keane, Michael P. - In: Marketing science 32 (2013) 6, pp. 913-938

Persistent link: https://www.econbiz.de/10010231794

Bayesian Estimation of Finite-Horizon Discrete Choice Dynamic Programming Models

Ishihara, Masakazu - 2016

We develop a Bayesian Markov chain Monte Carlo (MCMC) algorithm for estimating finite-horizon discrete choice dynamic programming (DDP) models. The proposed algorithm has the potential to reduce the computational burden significantly when some of the state variables are continuous. In a...

Persistent link: https://www.econbiz.de/10012983458

Bayesian estimation of dynamic discrete choice models

Imai, Susumu (contributor); Jain, Neelam (contributor); … - 2006

Persistent link: https://www.econbiz.de/10003412464

Bayesian estimation of dynamic discrete choice models

Imai, Susumu; Jain, Neelam; Ching, Andrew - In: Econometrica : journal of the Econometric Society, an … 77 (2009) 6, pp. 1865-1899

Persistent link: https://www.econbiz.de/10003943433

Identification and estimation of forward-looking behavior : the case of consumer stockpiling

Ching, Andrew; Osborne, Matthew - In: Marketing science 39 (2020) 4, pp. 707-726

Persistent link: https://www.econbiz.de/10012294600

The Structural Estimation of Behavioral Models: Discrete Choice Dynamic Programming Methods and Applications

Keane, Michael P.; Todd, Petra E.; Wolpin, Kenneth I. - In: Handbook of Labor Economics

The purpose of this chapter is twofold: (1) to provide an accessible introduction to the methods of structural estimation of discrete choice dynamic programming (DCDP) models and (2) to survey the contributions of applications of these methods to substantive and policy issues in labor economics....

Persistent link: https://www.econbiz.de/10009642958