Download or read book Semiparametric Bayesian Estimation of Dynamic Discrete Choice Models written by Andriy Norets and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose a tractable semiparametric estimation method for dynamic discrete choice models. The distribution of additive utility shocks is modeled by location-scale mixtures of extreme value distributions with varying numbers of mixture components. Our approach exploits the analytical tractability of extreme value distributions and the flexibility of the location-scale mixtures. We implement the Bayesian approach to inference using Hamiltonian Monte Carlo and an approximately optimal reversible jump algorithm. For binary dynamic choice model, our approach delivers estimation results that are consistent with the previous literature. We also apply the proposed method to multinomial choice models, for which previous literature does not provide tractable estimation methods in general settings without distributional assumptions on the utility shocks. In our simulation experiments, we show that the standard dynamic logit model can deliver misleading results, especially about counterfactuals, when the shocks are not extreme value distributed. Our semiparametric approach delivers reliable inference in these settings. We develop theoretical results on approximations by location-scale mixtures in an appropriate distance and posterior concentration of the set identified utility parameters and the distribution of shocks in the model.

Download or read book Semiparametric Bayesian Estimation of Discrete Choice Models written by Sylvie Tchumtchoua and published by . This book was released on 2007 with total page 62 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Bayesian Estimation of Dynamic Discrete Choice Models written by Susumu Imai and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose a new methodology for structural estimation of infinite horizon dynamic discrete choice models. We combine the Dynamic Programming (DP) solution algorithm with the Bayesian Markov Chain Monte Carlo algorithm into a single algorithm that solves the DP problem and estimates the parameters simultaneously. As a result, the computational burden of estimating a dynamic model becomes comparable to that of a static model. Another feature of our algorithm is that even though per solution-estimation iteration, the number of grid points on the state variable is small, the number of effective grid points increases with the number of estimation iterations. This is how we help ease the "Curse of Dimensionality." We simulate and estimate several versions of a simple model of entry and exit to illustrate our methodology. We also prove that under standard conditions, the parameters converge in probability to the true posterior distribution, regardless of the starting values.

Download or read book Bayesian Estimation of Dynamic Discrete Choice Models written by and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Bayesian Procedures as a Numerical Tool for the Estimation of Dynamic Discrete Choice Models written by Peter Haan and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Practitioner s Guide to Bayesian Estimation of Discrete Choice Dynamic Programming Models written by Andrew T. Ching and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper provides a step-by-step guide to estimating infinite horizon discrete choice dynamic programming (DDP) models using a new Bayesian estimation algorithm (Imai, Jain and Ching, Econometrica 77:1865-1899, 2009) (IJC). In the conventional nested fixed point algorithm, most of the information obtained in the past iterations remains unused in the current iteration. In contrast, the IJC algorithm extensively uses the computational results obtained from the past iterations to help solve the DDP model at the current iterated parameter values. Consequently, it has the potential to significantly alleviate the computational burden of estimating DDP models. To illustrate this new estimation method, we use a simple dynamic store choice model where stores offer "frequent-buyer" type reward programs. We show that the parameters of this model, including the discount factor, are well-identified. Our Monte Carlo results demonstrate that the IJC method is able to recover the true parameter values of this model quite precisely. We also show that the IJC method could reduce the estimation time significantly when estimating DDP models with unobserved heterogeneity, especially when the discount factor is close to 1.

Download or read book Bayesian Estimation of Finite Horizon Discrete Choice Dynamic Programming Models written by Masakazu Ishihara and published by . This book was released on 2016 with total page 29 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 conventional approach to estimating such a finite-horizon DDP model, researchers achieve a reduction in estimation time by evaluating value functions at only a subset of state points and applying an interpolation method to approximate value functions at the remaining state points (e.g., Keane and Wolpin 1994). Although this approach has proven to be effective, the computational burden could still be high if the model has multiple continuous state variables or the number of periods in the time horizon is large. We propose a new estimation algorithm to reduce the computational burden for estimating this class of models. It extends the Bayesian MCMC algorithm for stationary infinite-horizon DDP models proposed by Imai, Jain and Ching (2009) (IJC). In our algorithm, we solve value functions at only one randomly chosen state point per time period, store those partially solved value functions period by period, and approximate expected value functions nonparametrically using the set of those partially solved value functions. We conduct Monte Carlo exercises and show that our algorithm is able to recover the true parameter values well. Finally, similar to IJC, our algorithm allows researchers to incorporate flexible unobserved heterogeneity, which is often computationally infeasible in the conventional two-step estimation approach (e.g., Hotz and Miller 1993; Aguirregabiria and Mira 2002).

Download or read book Semiparametric Identi tion and Estimation of Multinomial Discrete Choice Models Using Error Symmetry written by Arthur Lewbel and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Semiparametric Identification and Estimation of Discrete Choice Models for Bundles written by Fu Ouyang and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Bayesian Inference in Dynamic Discrete Choice Models written by Andriy Norets and published by . This book was released on 2007 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Discrete Choice Methods with Simulation written by Kenneth Train and published by Cambridge University Press. This book was released on 2009-07-06 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.

Download or read book Estimation of Dynamic Discrete Choice Models in Continuous Time written by Peter Arcidiacono and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper provides a method for estimating large-scale dynamic discrete choice models within a continuous time framework. An advantage of our model is that state changes occur sequentially, rather than simultaneously, avoiding a substantial curse of dimensionality that arises in multi-agent settings. Eliminating this computational bottleneck is the key to providing a seamless link between estimating the model and performing post-estimation counterfactuals. While recently developed two-step estimation techniques have made it possible to estimate large-scale problems, solving for equilibria remains computationally challenging. By modeling decisions in continuous time, we are able to take advantage of the recent advances in estimation while preserving a tight link between estimation and policy experiments. We address the most commonly encountered situation in empirical work in which only discrete-time data are available and the actual sequence of events that occur between two points in time is unobserved. We apply our techniques to examine the effects of Walmart's entry into the retail grocery industry, showing that even the threat of entry by Walmart has a substantial effect on market structure.

Download or read book A Practitioner s Guide to Bayesian Estimation of Discrete Choice Dynamic Programming Models written by and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Semiparametric Bayesian Approach to a New Dynamic Zero Inflated Model written by and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays on the Simulation based Estimation of Dynamic Discrete Choice Models written by Ben Waltmann and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Semiparametric Discrete Choice Models for Bundles written by Fu Ouyang and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Handbook of Econometrics written by J.J. Heckman and published by Elsevier. This book was released on 2001-11-22 with total page 737 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook is a definitive reference source and teaching aid for econometricians. It examines models, estimation theory, data analysis and field applications in econometrics. Comprehensive surveys, written by experts, discuss recent developments at a level suitable for professional use by economists, econometricians, statisticians, and in advanced graduate econometrics courses. For more information on the Handbooks in Economics series, please see our home page on http://www.elsevier.nl/locate/hes