Download or read book Simulated Maximum Likelihood Estimation of the Sequential Search Model written by Jae Hyen Chung and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The authors propose a new approach to simulate the likelihood of the sequential search model. By allowing search costs to be heterogeneous across consumers and products, the authors directly compute the joint probability of the search and purchase decision when consumers are searching for the idiosyncratic preference shocks in their utility functions. Under the assumptions of Weitzman's sequential search algorithm, the proposed procedure recursively makes random draws for each quantity that requires numerical integration in order to compute the joint probabilities of consumers' search and purchase decisions. In an extensive simulation study, the proposed method is compared with existing likelihood simulators that have recently been used to estimate the sequential search model. In addition, the proposed method recovers the consumers' relative preferences even if the utility function and/or the search cost distribution is mis-specified. The proposed method is then applied to online search data from Expedia for field-data validation. The more precise estimation of the model parameters and the improved prediction accuracy of the proposed approach stem from attributing researcher uncertainty in the search order to the consumer-product-level distribution of search costs and the randomness in the choice decision to the distribution of match values across consumers and products. From a substantive perspective, the authors find that search costs and "position" effects affect products in the lower part of the product listing page that are searched; more than they do those in the upper part of the page.
Download or read book Estimation of Sequential Search Model written by Jae Hyen Chung and published by . This book was released on 2019 with total page 71 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose a new likelihood-based estimation method for the sequential search model. By allowing search costs to be heterogeneous across consumers and products, we can directly compute the joint probability of the search sequence and the purchase decision when consumers are searching for the idiosyncratic preference shocks in their utility functions. Under this procedure, one recursively makes random draws for each dimension that requires numerical integration to simulate the probabilities associated with the purchase decision and the search sequence under the sequential search algorithm. We then present details from an extensive simulation study that compares the proposed approach with existing estimation methods recently used for sequential search model estimation, viz., the kernel-smoothed frequency simulator (KSFS) and the crude frequency simulator (CFS). In the empirical application, we apply the proposed method to the Expedia dataset from Kaggle which has previously been analyzed using the KSFS estimator and the assumption of homogeneous search costs. We demonstrate that the proposed method has a better predictive performance associated with differences in the estimated effects of various drivers of clicks and purchases, and highlight the importance of the heterogeneous search costs assumption even when KSFS is used to estimate the sequential search model. Lastly, from a managerial perspective, we show that sorting products by their expected utilities can enhance consumer welfare and increase the number of transactions.
Download or read book Maximum Likelihood Estimation for Stochastic Differential Equations Using Sequential Kriging based Optimization written by Grant W. Schneider and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic differential equations (SDEs) are used as statistical models in many disciplines. However, intractable likelihood functions for SDEs make inference challenging, and we need to resort to simulation-based techniques to estimate and maximize the likelihood function. While sequential Monte Carlo methods have allowed for the accurate evaluation of likelihoods at fixed parameter values, there is still a question of how to find the maximum likelihood estimate. In this dissertation we propose an efficient Gaussian-process-based method for exploring the parameter space using estimates of the likelihood from a sequential Monte Carlo sampler. Our method accounts for the inherent Monte Carlo variability of the estimated likelihood, and does not require knowledge of gradients. The procedure adds potential parameter values by maximizing the so-called expected improvement, leveraging the fact that the likelihood function is assumed to be smooth. Our simulations demonstrate that our method has significant computational and efficiency gains over existing grid- and gradient-based techniques. Our method is applied to modeling stock prices over the past ten years and compared to the most commonly used model.
Download or read book Maximum Likelihood Estimation and Inference written by Russell B. Millar and published by John Wiley & Sons. This book was released on 2011-07-26 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book takes a fresh look at the popular and well-established method of maximum likelihood for statistical estimation and inference. It begins with an intuitive introduction to the concepts and background of likelihood, and moves through to the latest developments in maximum likelihood methodology, including general latent variable models and new material for the practical implementation of integrated likelihood using the free ADMB software. Fundamental issues of statistical inference are also examined, with a presentation of some of the philosophical debates underlying the choice of statistical paradigm. Key features: Provides an accessible introduction to pragmatic maximum likelihood modelling. Covers more advanced topics, including general forms of latent variable models (including non-linear and non-normal mixed-effects and state-space models) and the use of maximum likelihood variants, such as estimating equations, conditional likelihood, restricted likelihood and integrated likelihood. Adopts a practical approach, with a focus on providing the relevant tools required by researchers and practitioners who collect and analyze real data. Presents numerous examples and case studies across a wide range of applications including medicine, biology and ecology. Features applications from a range of disciplines, with implementation in R, SAS and/or ADMB. Provides all program code and software extensions on a supporting website. Confines supporting theory to the final chapters to maintain a readable and pragmatic focus of the preceding chapters. This book is not just an accessible and practical text about maximum likelihood, it is a comprehensive guide to modern maximum likelihood estimation and inference. It will be of interest to readers of all levels, from novice to expert. It will be of great benefit to researchers, and to students of statistics from senior undergraduate to graduate level. For use as a course text, exercises are provided at the end of each chapter.
Download or read book Simulated Maximum Likelihood Estimation of Discrete Models with Group Data written by Lung-Fei Lee and published by . This book was released on 1993 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Maximum Likelihood Estimation written by Scott R. Eliason and published by SAGE. This book was released on 1993 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a short introduction to Maximum Likelihood (ML) Estimation. It provides a general modeling framework that utilizes the tools of ML methods to outline a flexible modeling strategy that accommodates cases from the simplest linear models (such as the normal error regression model) to the most complex nonlinear models linking endogenous and exogenous variables with non-normal distributions. Using examples to illustrate the techniques of finding ML estimators and estimates, the author discusses what properties are desirable in an estimator, basic techniques for finding maximum likelihood solutions, the general form of the covariance matrix for ML estimates, the sampling distribution of ML estimators; the use of ML in the normal as well as other distributions, and some useful illustrations of likelihoods.
Download or read book Simulated Maximum Likelihood Estimation Based on First Order Conditions written by Michael P. Keane and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: I describe a strategy for structural estimation that uses simulated maximum likelihood (SML) to estimate the structural parameters appearing in a model's first-order conditions (FOCs). Generalized method of moments (GMM) is often the preferred method for estimation of FOCs, as it avoids distributional assumptions on stochastic terms, provided all structural errors enter the FOCs additively, giving a single composite additive error. But SML has advantages over GMM in models where multiple structural errors enter the FOCs nonadditively. I develop new simulation algorithms required to implement SML based on FOCs, and I illustrate the method using a model of U.S. multinational corporations.
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 Structural Econometric Modeling in Industrial Organization and Quantitative Marketing written by Ali Hortaçsu and published by Princeton University Press. This book was released on 2023-10-24 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: A concise and rigorous introduction to widely used approaches in structural econometric modeling Structural econometric modeling specifies the structure of an economic model and estimates the model’s parameters from real-world data. Structural econometric modeling enables better economic theory–based predictions and policy counterfactuals. This book offers a primer on recent developments in these modeling techniques, which are used widely in empirical industrial organization, quantitative marketing, and related fields. It covers such topics as discrete choice modeling, demand modes, estimation of the firm entry models with strategic interactions, consumer search, and theory/empirics of auctions. The book makes highly technical material accessible to graduate students, describing key insights succinctly but without sacrificing rigor. • Concise overview of the most widely used structural econometric models • Rigorous and systematic treatment of the topics, emphasizing key insights • Coverage of demand estimation, estimation of static and dynamic game theoretic models, consumer search, and auctions • Focus on econometric models while providing concise reviews of relevant theoretical models
Download or read book Maximum Likelihood Estimation for Sample Surveys written by Raymond L. Chambers and published by CRC Press. This book was released on 2012-05-02 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sample surveys provide data used by researchers in a large range of disciplines to analyze important relationships using well-established and widely used likelihood methods. The methods used to select samples often result in the sample differing in important ways from the target population and standard application of likelihood methods can lead to
Download or read book Handbook of Simulation Optimization written by Michael C Fu and published by Springer. This book was released on 2014-11-13 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook of Simulation Optimization presents an overview of the state of the art of simulation optimization, providing a survey of the most well-established approaches for optimizing stochastic simulation models and a sampling of recent research advances in theory and methodology. Leading contributors cover such topics as discrete optimization via simulation, ranking and selection, efficient simulation budget allocation, random search methods, response surface methodology, stochastic gradient estimation, stochastic approximation, sample average approximation, stochastic constraints, variance reduction techniques, model-based stochastic search methods and Markov decision processes. This single volume should serve as a reference for those already in the field and as a means for those new to the field for understanding and applying the main approaches. The intended audience includes researchers, practitioners and graduate students in the business/engineering fields of operations research, management science, operations management and stochastic control, as well as in economics/finance and computer science.
Download or read book Simulation based Maximum Likelihood Estimation in Linear Latent Variable Models with Normal and Ordinal Outcomes written by Klaus Larsen and published by . This book was released on 2000 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Sequential Search Model written by Raluca Ursu and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We provide a detailed overview of the empirical implementation of the sequential search model proposed by Weitzman (1979). We discuss the assumptions underlying the model, the identifica-tion of search cost and preference parameters, the necessary normalizations of utility parameters, counterfactuals that require a search model framework, and different estimation approaches. The goal of this paper is to consolidate knowledge and provide a unified treatment of various aspects of sequential search models that are relevant for empirical work.
Download or read book Encyclopedia of Mathematical Geosciences written by B. S. Daya Sagar and published by Springer Nature. This book was released on 2023-07-13 with total page 1744 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Encyclopedia of Mathematical Geosciences is a complete and authoritative reference work. It provides concise explanation on each term that is related to Mathematical Geosciences. Over 300 international scientists, each expert in their specialties, have written around 350 separate articles on different topics of mathematical geosciences including contributions on Artificial Intelligence, Big Data, Compositional Data Analysis, Geomathematics, Geostatistics, Geographical Information Science, Mathematical Morphology, Mathematical Petrology, Multifractals, Multiple Point Statistics, Spatial Data Science, Spatial Statistics, and Stochastic Process Modeling. Each topic incorporates cross-referencing to related articles, and also has its own reference list to lead the reader to essential articles within the published literature. The entries are arranged alphabetically, for easy access, and the subject and author indices are comprehensive and extensive.
Download or read book New Horizon Testing written by David J. Weiss and published by Elsevier. This book was released on 2014-06-28 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: New Horizon Testing
Download or read book Simulating Sequential Search Models with Genetic Algorithms written by Ian M. McCarthy and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper studies advertising, price ceilings and taxes in a sequential search model with bilateral heterogeneities in production and search costs. We estimate equilibria using a genetic algorithm (GA) applied to over 100 market scenarios, each differing based on the number of firms, number of consumers, existence of price ceilings or taxes, costs of production, costs of advertising, consumers' susceptibility to advertising and consumers' search costs. We compare our equilibrium results to those of the standard theoretical consumer search literature and analyze the welfare effects of advertising, price ceilings and sales taxes. We find that price ceilings and uninformative advertising can improve welfare, especially if search costs are sufficiently high.
Download or read book Introduction to Stochastic Search and Optimization written by James C. Spall and published by John Wiley & Sons. This book was released on 2005-03-11 with total page 620 pages. Available in PDF, EPUB and Kindle. Book excerpt: * Unique in its survey of the range of topics. * Contains a strong, interdisciplinary format that will appeal to both students and researchers. * Features exercises and web links to software and data sets.
