Download or read book Estimation of Random Coefficients Logit Demand Models with Interactive Fixed Effects written by Hyungsik Roger Moon and published by . This book was released on 2017 with total page 57 pages. Available in PDF, EPUB and Kindle. Book excerpt: We extend the Berry, Levinsohn and Pakes (BLP, 1995) random coefficients discrete choice demand model, which underlies much recent empirical work in IO. We add interactive fixed effects in the form of a factor structure on the unobserved product characteristics. The interactive fixed effects can be arbitrarily correlated with the observed product characteristics (including price), which accommodates endogeneity and, at the same time, captures strong persistence in market shares across products and markets. We propose a two-step least squares-minimum distance (LS-MD) procedure to calculate the estimator. Our estimator is easy to compute, and Monte Carlo simulations show that it performs well. We consider an empirical illustration to US automobile demand.
Download or read book Reliable Estimation of Random Coefficient Logit Demand Models written by Daniel Brunner 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 Semi Nonparametric Estimation of Random Coefficient Logit Model for Aggregate Demand written by Zhentong Lu and published by . This book was released on 2020 with total page 71 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we propose a two-step semi-nonparametric estimator for the widely used random coefficient logit demand model. In the first step, exploiting the structure of logit choice probabilities, we transform the full demand system into a partial linear model and estimate the fixed (non-random) coefficients using standard linear sieve generalized method of moment (GMM). In the second step, we construct a sieve minimum distance (MD) estimator to uncover the distribution of random coefficients nonparametrically. We establish the asymptotic properties of the estimator and show the semi-nonparametric identification of the model in a large market environment. Monte Carlo simulations and empirical illustrations support the theoretical results and demonstrate the usefulness of our estimator in practice.
Download or read book Random Coefficients Logit Demand Estimation with Zero Valued Market Shares written by Jean-Pierre H. Dubé and published by . This book was released on 2020 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although typically overlooked, many purchase datasets exhibit a high incidence of products with zero sales. We propose a new estimator for the Random-Coefficients Logit demand system for purchase datasets with zero-valued market shares. The identification of the demand parameters is based on a pairwise-differencing approach that constructs moment conditions based on differences in demand between pairs of products. The corresponding estimator corrects non-parametrically for the potential selection of the incidence of zeros on unobserved aspects of demand. The estimator also corrects for the potential endogeneity of marketing variables both in demand and in the selection propensities. Monte Carlo simulations show that our proposed estimator provides reliable small-sample inference both with and without selection-on- unobservables. In an empirical case study, the proposed estimator not only generates different demand estimates than approaches that ignore selection in the incidence of zero shares, it also generates better out-of-sample fit of observed retail contribution margins.
Download or read book A Practitioner s Guide to Estimation of Random Coefficients Logit Models of Demand written by Aviv Nevo and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Estimation of demand is at the heart of many recent studies that examine questions of market power, mergers, innovation, and valuation of new brands in differentiated-products markets. This paper focuses on one of the main methods for estimating demand for differentiated products: random-coefficients logit models. The paper carefully discusses the latest innovations in these methods with the hope of increasing the understanding, and therefore the trust among researchers who have never used them, and reducing the difficulty of their use, thereby aiding in realizing their full potential.
Download or read book Comparing Alternative Procedures for Estimating Random Coefficient Logit Demand Models written by Zsolt Sandor and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We compare several nested fixed point and optimization procedures for computing the estimator of the widely-used empirical market demand model developed by Berry et al. (1995). It is well-known that the optimization may often lead to multiple local optima, which, if ignored, can lead to erroneous policy conclusions. By combining the frequencies of finding the global minima and the computing times, we propose a new indicator that provides the computing time needed for obtaining the global minima. Using this indicator, we find that the Spectral and Squarem methods (Reynaerts et al., 2012) outperform the benchmark contraction iterations method and the MPEC (Dubé et al., 2012) and ABLP (Lee and Seo, 2015) methods. Moreover, in some practically highly relevant cases, two derivative-free optimization algorithms, which require less calculations and coding than derivative-based algorithms, outperform the best derivative-based methods. A simple argument suggests that the latter statement is likely to be true for other versions of the model as well.
Download or read book A Research Assistant s Guide to Random Coefficients Discrete Choice Models of Demand written by Aviv Nevo and published by . This book was released on 1998 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: The study of differentiated-products markets is a central part of empirical industrial organization. Questions regarding market power, mergers, innovation, and valuation of new brands are addressed using cutting-edge econometric methods and relying on economic theory. Unfortunately, difficulty of use and computational costs have limited the scope of application of recent developments in one of the main methods for estimating demand for differentiated products: random coefficients discrete choice models. As our understanding of these models of demand has increased, both the difficulty and costs have been greatly reduced. This paper carefully discusses the latest innovations in these methods with the hope of (1) increasing the understanding, and therefore the trust, among researchers who never used these methods, and (2) reducing the difficulty of use, and therefore aiding in realizing the full potential of these methods.
Download or read book Flexible Estimation of Random Coefficient Logit Models of Differentiated Product Demand written by Johannes Kandelhardt and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Berry, Levinsohn, and Pakes (1995, BLP) model is widely used to obtain parameter estimates of market forces in differentiated product markets. The results are often used as an input to evaluate economic activity in a structural model of demand and supply. Precise estimation of parameter estimates is therefore crucial to obtain realistic economic predictions. The present paper combines the BLP model and the logit mixed logit model of Train (2016) to estimate the distribution of consumer heterogeneity in a flexible and parsimonious way. A Monte Carlo study yields asymptotically normally distributed and consistent estimates of the structural parameters. With access to micro data, the approach allows for the estimation of highly flexible parametric distributions. The estimator further allows to introduce correlations between tastes, yielding more realistic demand patterns without substantially altering the procedure of estimation, making it relevant for practitioners. The BLP estimator is established to yield biased and inconsistent results when the underlying distributional shape is non-normally distributed. An application shows the estimator to perform well on a real world dataset and provides similar estimates as the BLP estimator with the option of specifying consumer heterogeneity as a function of a polynomial, step function or spline, resulting in a flexible estimation procedure.
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 Econometric Models For Industrial Organization written by Matthew Shum and published by World Scientific. This book was released on 2016-12-14 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: Economic Models for Industrial Organization focuses on the specification and estimation of econometric models for research in industrial organization. In recent decades, empirical work in industrial organization has moved towards dynamic and equilibrium models, involving econometric methods which have features distinct from those used in other areas of applied economics. These lecture notes, aimed for a first or second-year PhD course, motivate and explain these econometric methods, starting from simple models and building to models with the complexity observed in typical research papers. The covered topics include discrete-choice demand analysis, models of dynamic behavior and dynamic games, multiple equilibria in entry games and partial identification, and auction models.
Download or read book Improving the Performance of Random Coefficients Demand Models written by Mathias Reynaert and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We shed new light on the performance of Berry, Levinsohn and Pakes' (1995) GMM estimator of the aggregate random coefficient logit model. Based on an extensive Monte Carlo study, we show that the use of Chamberlain's (1987) optimal instruments overcomes most of the problems that have recently been documented with standard, non-optimal instruments. Optimal instruments reduce small sample bias, but prove even more powerful in increasing the estimator's efficiency and stability. Other recent methodological advances (MPEC, polynomial-based integration of the market shares) greatly improve computational speed, but they are only successful in terms of bias and efficiency when combined with optimal instruments.
Download or read book Estimation of Random Coefficient Demand Models written by Christopher R. Knittel and published by . This book was released on 2008 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Empirical exercises in economics frequently involve estimation of highly nonlinear models. The criterion function may not be globally concave or convex and exhibit many local extrema. Choosing among these local extrema is non-trivial for a variety of reasons. In this paper, we analyze the sensitivity of parameter estimates, and most importantly of economic variables of interest, to both starting values and the type of non-linear optimization algorithm employed. We focus on a class of demand models for differentiated products that have been used extensively in industrial organization, and more recently in public and labor. We find that convergence may occur at a number of local extrema, at saddles and in regions of the objective function where the first-order conditions are not satisfied. We find own- and cross-price elasticities that differ by a factor of over 100 depending on the set of candidate parameter estimates. In an attempt to evaluate the welfare effects of a change in an industry's structure, we undertake a hypothetical merger exercise. Our calculations indicate consumer welfare effects can vary between positive values to negative seventy billion dollars depending on the set of parameter estimates used"--National Bureau of Economic Research web site
Download or read book Bayesian Statistics and Marketing written by Peter E. Rossi and published by John Wiley & Sons. This book was released on 2012-05-14 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past decade has seen a dramatic increase in the use of Bayesian methods in marketing due, in part, to computational and modelling breakthroughs, making its implementation ideal for many marketing problems. Bayesian analyses can now be conducted over a wide range of marketing problems, from new product introduction to pricing, and with a wide variety of different data sources. Bayesian Statistics and Marketing describes the basic advantages of the Bayesian approach, detailing the nature of the computational revolution. Examples contained include household and consumer panel data on product purchases and survey data, demand models based on micro-economic theory and random effect models used to pool data among respondents. The book also discusses the theory and practical use of MCMC methods. Written by the leading experts in the field, this unique book: Presents a unified treatment of Bayesian methods in marketing, with common notation and algorithms for estimating the models. Provides a self-contained introduction to Bayesian methods. Includes case studies drawn from the authors’ recent research to illustrate how Bayesian methods can be extended to apply to many important marketing problems. Is accompanied by an R package, bayesm, which implements all of the models and methods in the book and includes many datasets. In addition the book’s website hosts datasets and R code for the case studies. Bayesian Statistics and Marketing provides a platform for researchers in marketing to analyse their data with state-of-the-art methods and develop new models of consumer behaviour. It provides a unified reference for cutting-edge marketing researchers, as well as an invaluable guide to this growing area for both graduate students and professors, alike.
Download or read book Chimeras and Consciousness written by Lynn Margulis and published by MIT Press. This book was released on 2011 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scientists elucidate the astounding collective sensory capacity of Earth and its evolution through time.
Download or read book Panel Data Econometrics with R written by Yves Croissant and published by John Wiley & Sons. This book was released on 2018-08-10 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Panel Data Econometrics with R provides a tutorial for using R in the field of panel data econometrics. Illustrated throughout with examples in econometrics, political science, agriculture and epidemiology, this book presents classic methodology and applications as well as more advanced topics and recent developments in this field including error component models, spatial panels and dynamic models. They have developed the software programming in R and host replicable material on the book’s accompanying website.
Download or read book Bayesian Non and Semi parametric Methods and Applications written by Peter Rossi and published by Princeton University Press. This book was released on 2014-04-27 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews and develops Bayesian non-parametric and semi-parametric methods for applications in microeconometrics and quantitative marketing. Most econometric models used in microeconomics and marketing applications involve arbitrary distributional assumptions. As more data becomes available, a natural desire to provide methods that relax these assumptions arises. Peter Rossi advocates a Bayesian approach in which specific distributional assumptions are replaced with more flexible distributions based on mixtures of normals. The Bayesian approach can use either a large but fixed number of normal components in the mixture or an infinite number bounded only by the sample size. By using flexible distributional approximations instead of fixed parametric models, the Bayesian approach can reap the advantages of an efficient method that models all of the structure in the data while retaining desirable smoothing properties. Non-Bayesian non-parametric methods often require additional ad hoc rules to avoid "overfitting," in which resulting density approximates are nonsmooth. With proper priors, the Bayesian approach largely avoids overfitting, while retaining flexibility. This book provides methods for assessing informative priors that require only simple data normalizations. The book also applies the mixture of the normals approximation method to a number of important models in microeconometrics and marketing, including the non-parametric and semi-parametric regression models, instrumental variables problems, and models of heterogeneity. In addition, the author has written a free online software package in R, "bayesm," which implements all of the non-parametric models discussed in the book.
Download or read book Consistent Estimation of Fixed Effects Ordered Logit Model written by Gregori Baetschmann and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
