Three Essays on Simulation-based Estimation of Multivariate Models with Unobserved Heterogeneity
Author: Murat Khairzhanuly Munkin
Publisher:
Published: 2001
Total Pages: 374
ISBN-13:
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Author: Murat Khairzhanuly Munkin
Publisher:
Published: 2001
Total Pages: 374
ISBN-13:
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Published: 2009-05
Total Pages: 582
ISBN-13:
DOWNLOAD EBOOKAuthor: Kenneth Train
Publisher: Cambridge University Press
Published: 2009-07-06
Total Pages: 399
ISBN-13: 0521766559
DOWNLOAD EBOOKThis 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.
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Publisher:
Published: 2001
Total Pages: 776
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DOWNLOAD EBOOKAuthor: Young gui Kim
Publisher:
Published: 2009
Total Pages: 212
ISBN-13:
DOWNLOAD EBOOKAuthor: David M. Zimmer
Publisher:
Published: 2004
Total Pages: 474
ISBN-13:
DOWNLOAD EBOOKAuthor: Matthew Shum
Publisher: World Scientific
Published: 2016-12-14
Total Pages: 154
ISBN-13: 981310967X
DOWNLOAD EBOOKEconomic 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.
Author: Juan J. Dolado
Publisher: Emerald Group Publishing
Published: 2022-09-21
Total Pages: 188
ISBN-13: 1803828331
DOWNLOAD EBOOKBoth parts of Volume 44 of Advances in Econometrics pay tribute to Fabio Canova for his major contributions to economics over the last four decades.
Author: Wolfgang A. Gaul
Publisher: Springer Science & Business Media
Published: 2012-12-06
Total Pages: 517
ISBN-13: 3642582508
DOWNLOAD EBOOK"Data Analysis" in the broadest sense is the general term for a field of activities of ever-increasing importance in a time called the information age. It covers new areas with such trendy labels as, e.g., data mining or web mining as well as traditional directions emphazising, e.g., classification or knowledge organization. Leading researchers in data analysis have contributed to this volume and delivered papers on aspects ranging from scientific modeling to practical application. They have devoted their latest contributions to a book edited to honor a colleague and friend, Hans-Hermann Bock, who has been active in this field for nearly thirty years.
Author: Brian Everitt
Publisher: Springer Science & Business Media
Published: 2011-04-23
Total Pages: 284
ISBN-13: 1441996508
DOWNLOAD EBOOKThe majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data.