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This study of demand analysis links economic theory to empirical analysis. It demonstrates how theory can be used to specify equation systems suitable for empirical analysis, and discusses demand systems estimation using both per capita time series and household budget data.
This book contains some of the results from the research project "Demand for Food in the Nordic Countries", which was initiated in 1988 by Professor Olof Bolin of the Agricultural University in Ultuna, Sweden and by Professor Karl Iohan Weckman, of the University of Helsinki, Finland. A pilot study was carried out by Bengt Assarsson, which in 1989 led to a successful application for a research grant from the NKJ (The Nordic Contact Body for Agricultural Research) through the national research councils for agricultural research in Denmark, Finland, Norway and Sweden. We are very grateful to Olof Bolin and Karl Iohan Weckman, without whom this project would not have come about, and to the national research councils in the Nordic countries for the generous financial support we have received for this project. We have received comments and suggestions from many colleagues, and this has improved our work substantially. At the start of the project a reference group was formed, consisting of Professor Olof Bolin, Professor Anders Klevmarken, Agr. lie. Gert Aage Nielsen, Professor Karl Iohan Weckman and Cando oecon. Per Halvor Vale. Gert Aage Nielsen left the group early in the project for a position in Landbanken, and was replaced by Professor Lars Otto, while Per Halvor Vale soon joined the research staff. The reference group has given us useful suggestions and encouraged us in our work. Weare very grateful to them.
Using Applied Econometrics with SAS: Modeling Demand, Supply, and Risk, you will quickly master SAS applications for implementing and estimating standard models in the field of econometrics. This guide introduces you to the major theories underpinning applied demand and production economics. For each of its three main topics--demand, supply, and risk--a concise theoretical orientation leads directly into consideration of specific economic models and econometric techniques, collectively covering the following: Double-log demand systems Linear expenditure systems Almost ideal demand systems Rotterdam models Random parameters logit demand models Frequency-severity models Compound distribution models Cobb-Douglas production functions Translogarithmic cost functions Generalized Leontief cost functions Density estimation techniques Copula models SAS procedures that facilitate estimation of demand, supply, and risk models include the following, among others: PROC MODEL PROC COPULA PROC SEVERITY PROC KDE PROC LOGISTIC PROC HPCDM PROC IML PROC REG PROC COUNTREG PROC QLIM An empirical example, SAS programming code, and a complete data set accompany each econometric model, empowering you to practice these techniques while reading. Examples are drawn from both major scholarly studies and business applications so that professors, graduate students, government economic researchers, agricultural analysts, actuaries, and underwriters, among others, will immediately benefit.
In the autumn of 1961 Jan Salomon ('Mars') Cramer was appointed to the newly established chair of econometrics at the University of Amsterdam. This volume is published to commemorate this event. It is well-known how much econometrics has developed over the period under consideration, the 25 years that elapsed between 1961 and 1986. This is specifically true for the areas in which Cramer has been actively interested. We mention the theory and measurement of consumer behaviour; money and income; regression, correla tion and forecasting. In the present volume this development will be high lighted. Sixteen contributions have been sollicited from scholars all over the world who have belonged to the circle of academic friends of Cramer for a shorter or longer part of the period of 25 years. The contributions fall broadly speaking into the four areas mentioned above. Theory and measurement of consumer behaviour is represented by four papers, whereas a fifth paper deals with a related area. Richard Blundell and Costas Meghir devote a paper to the estimation of Engel curves. They apply a discrete choice model to British (individual) data from the Family Expenditure Survey 1981. Their aim is to assess the impact of individual characteristics such as income, demographic structure, location, wages and prices on commodity expenditure.
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.
Simulation methods are revolutionizing the practice of applied economic analysis. In this book, leading researchers from around the world discuss interpretation issues, similarities and differences across alternative models, and propose practical solutions for the choice of the model and programming. Case studies show the practical use and the results brought forth by the different methods.
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.
On the asymptotic normality of fourier flexible form estimates.