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Bayesian Estimation Of Dynamic Discrete Choice Models
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Book Synopsis Bayesian Estimation of Dynamic Discrete Choice Models by : Susumu Imai
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.
Book Synopsis Bayesian Estimation of Dynamic Discrete Choice Models by :
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:
Book Synopsis Semiparametric Bayesian Estimation of Dynamic Discrete Choice Models by : Andriy Norets
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.
Book Synopsis Bayesian Procedures as a Numerical Tool for the Estimation of Dynamic Discrete Choice Models by : Peter Haan
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:
Book Synopsis A Practitioner's Guide to Bayesian Estimation of Discrete Choice Dynamic Programming Models by : Andrew T. Ching
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.
Book Synopsis Bayesian Inference in Dynamic Discrete Choice Models by : Andriy Norets
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:
Book Synopsis Bayesian Estimation of Finite-Horizon Discrete Choice Dynamic Programming Models by : Masakazu Ishihara
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).
Book Synopsis Semiparametric Bayesian Estimation of Discrete Choice Models by : Sylvie Tchumtchoua
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:
Book Synopsis A Practitioner's Guide to Bayesian Estimation of Discrete Choice Dynamic Programming Models by :
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:
Book Synopsis Discrete Choice Methods with Simulation by : Kenneth Train
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.
Book Synopsis Handbook of Choice Modelling by : Stephane Hess
Download or read book Handbook of Choice Modelling written by Stephane Hess and published by Edward Elgar Publishing. This book was released on 2024-06-05 with total page 797 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thoroughly revised second edition Handbook provides an authoritative and in-depth overview of choice modelling, covering essential topics range from data collection through model specification and estimation to analysis and use of results. It aptly emphasises the broad relevance of choice modelling when applied to a multitude of fields, including but not limited to transport, marketing, health and environmental economics.
Book Synopsis Choice Models in Marketing by : Sandeep R. Chandukala
Download or read book Choice Models in Marketing written by Sandeep R. Chandukala and published by Now Publishers Inc. This book was released on 2008 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: Choice Models in Marketing examines recent developments in the modeling of choice for marketing and reviews a large stream of research currently being developed by both quantitative and qualitative researches in marketing. Choice in marketing differs from other domains in that the choice context is typically very complex, and researchers' desire knowledge of the variables that ultimately lead to demand in marketplace. The marketing choice context is characterized by many choice alternatives. The aim of Choice Models in Marketing is to lay out the foundations of choice models and discuss recent advances. The authors focus on aspects of choice that can be quantitatively modeled and consider models related to a process of constrained utility maximization. By reviewing the basics of choice modeling and pointing to new developments, Choice Models in Marketing provides a platform for future research.
Book Synopsis Discrete Choice Methods with Simulation by : Kenneth Train
Download or read book Discrete Choice Methods with Simulation written by Kenneth Train and published by Cambridge University Press. This book was released on 2003-01-13 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: Table of contents
Book Synopsis Bayesian Estimation Methods for Multidimensional Models for Discrete and Continuous Responses by : Cees A. W. Glas
Download or read book Bayesian Estimation Methods for Multidimensional Models for Discrete and Continuous Responses written by Cees A. W. Glas and published by . This book was released on 2009 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Estimation of Discrete Games of Complete Information by : Sridhar Narayanan
Download or read book Bayesian Estimation of Discrete Games of Complete Information written by Sridhar Narayanan and published by . This book was released on 2011 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Bayesian Approach to Hybrid Choice Models by : Ricardo Alvarez Daziano
Download or read book A Bayesian Approach to Hybrid Choice Models written by Ricardo Alvarez Daziano and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Online Discrete Choice Models by : Mazen Salah Danaf
Download or read book Online Discrete Choice Models written by Mazen Salah Danaf and published by . This book was released on 2019 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discrete choice models have been widely applied in different fields to better understand behavior and forecast market shares. Because of their ability to capture taste heterogeneity, logit mixture models have gained increasing interest among researchers and practitioners. However, since the estimation of these models is computationally expensive, their applications have been limited to offline contexts. On the other hand, online applications (such as recommender systems) require users' preferences to be updated frequently and dynamically. The objective of this dissertation is to develop a methodology for estimating discrete choice models online, while accounting for inter- and intra-consumer heterogeneity. An offline-online framework is proposed to update individual-specific parameters after each choice using Bayesian estimation. The online estimator is computationally efficient, as it uses the data of the individual making the choice only in updating his/her individual preferences. Periodically, data from multiple individuals are pooled, and population parameters are updated offline. Online estimation allows for new and innovative applications of discrete choice models such as personalized recommendations, dynamic personalized pricing, and real-time individual forecasting. This methodology subsumes the utility-based advantages of discrete choice models and the personalization capabilities of common recommendation techniques by making use of all the available data including user-specific, item specific, and contextual variables. In order to enhance online learning, two extensions are proposed to the logit mixture model with inter- and intra-consumer heterogeneity. In the first extension, socio-demographic variables and contextual variables are used to model systematic inter- and intra-consumer taste heterogeneity respectively. In the second extension, a latent class model is used to allow for more flexibility in modeling the inter- and intra-consumer mixing distributions. Finally, the online estimation methodology is applied to Tripod, an app-based travel advisor that aims to incentivize and shift travelers' behavior towards more sustainable alternatives. Stated preferences data are collected in the Greater Boston Area and used to estimate the population parameters, which are then used by the app in online estimation. Using the collected data, a large number of synthetic users is simulated, and the recommendation system is tested over several days, and under different scenarios. The results show that the average hit-rate generally increases over time as we learn individual preferences and population parameters.
