Evaluation of Econometric Models presents approaches to assessing and enhancing the progress of applied economic research. This book discusses the problems and issues in evaluating econometric models, use of exploratory methods in economic analysis, and model construction and evaluation when theoretical knowledge is scarce. The data analysis by partial least squares, prediction analysis of economic models, and aggregation and disaggregation of nonlinear equations are also elaborated. This text likewise covers the comparison of econometric models by optimal control techniques, role of time series analysis in econometric model evaluation, and hypothesis testing in spectral regression. Other topics include the relevance of laboratory experiments to testing resource allocation theory and token economy and animal models for the experimental analysis of economic behavior. This publication is intended for students and researchers interested in evaluating econometric models.

This book provides advanced theoretical and applied tools for the implementation of modern micro-econometric techniques in evidence-based program evaluation for the social sciences. The author presents a comprehensive toolbox for designing rigorous and effective ex-post program evaluation using the statistical software package Stata. For each method, a statistical presentation is developed, followed by a practical estimation of the treatment effects. By using both real and simulated data, readers will become familiar with evaluation techniques, such as regression-adjustment, matching, difference-in-differences, instrumental-variables and regression-discontinuity-design and are given practical guidelines for selecting and applying suitable methods for specific policy contexts.

Each year, a number of different economic groups in the USA use their own econometric models to forecast what will happen to the economy in the coming year. This volume consists of chapters by distinguished economists comparing the different models now being used.

Models of the American economy exist in government, research institutes, universities, and private corporations. Given the proliferation, it is wise to take stock because these models come from diverse sources and describe different conditions from alternative points of view. They could be saying different things about the economy. The high-level comparative studies in this volume, gathered from several issues of the International Economic Review, with a substantive introduction and the addition of more comparative material, evaluate the performance of eleven models of the American economy: the Wharton Mark Ill Model; Brookings Model; Hickman-Coen Annual Model; Liu-Hwa Monthly Model; Data Resources, Inc. (DRI) Model; Federal Reserve Bank of St. Louis Model; Michigan Quarterly Econometric (MOEM) Model; Wharton Annual and Industry Model; Anticipation Version of the Wharton Mark Ill Model/Fair Model; U.S. Department of Commerce (BEA) Model. Each of the proprietors or builders of these models describes his own system in his own words. These studies come closer than ever before to standardizing model operations for testing purposes. Some of the models are monthly, while others are annual. but the quarterly unit of time is the most frequent. Some are demand oriented, others are supply oriented, and focus on the input-output sectors of the economy. Some use only observed. objective data; others use subjective. anticipatory data. Both large and small models are included. In spite of the diversity, the contributors have cooperated to trace the differences between their models to root causes and to report jointly the results of their research. There are also some general papers that look at model performance from outside the CEME group.

Econometric Modeling provides a new and stimulating introduction to econometrics, focusing on modeling. The key issue confronting empirical economics is to establish sustainable relationships that are both supported by data and interpretable from economic theory. The unified likelihood-based approach of this book gives students the required statistical foundations of estimation and inference, and leads to a thorough understanding of econometric techniques. David Hendry and Bent Nielsen introduce modeling for a range of situations, including binary data sets, multiple regression, and cointegrated systems. In each setting, a statistical model is constructed to explain the observed variation in the data, with estimation and inference based on the likelihood function. Substantive issues are always addressed, showing how both statistical and economic assumptions can be tested and empirical results interpreted. Important empirical problems such as structural breaks, forecasting, and model selection are covered, and Monte Carlo simulation is explained and applied. Econometric Modeling is a self-contained introduction for advanced undergraduate or graduate students. Throughout, data illustrate and motivate the approach, and are available for computer-based teaching. Technical issues from probability theory and statistical theory are introduced only as needed. Nevertheless, the approach is rigorous, emphasizing the coherent formulation, estimation, and evaluation of econometric models relevant for empirical research.

In financially constrained health systems across the world, increasing emphasis is being placed on the ability to demonstrate that health care interventions are not only effective, but also cost-effective. This book deals with decision modelling techniques that can be used to estimate the value for money of various interventions including medical devices, surgical procedures, diagnostic technologies, and pharmaceuticals. Particular emphasis is placed on the importance of the appropriate representation of uncertainty in the evaluative process and the implication this uncertainty has for decision making and the need for future research. This highly practical guide takes the reader through the key principles and approaches of modelling techniques. It begins with the basics of constructing different forms of the model, the population of the model with input parameter estimates, analysis of the results, and progression to the holistic view of models as a valuable tool for informing future research exercises. Case studies and exercises are supported with online templates and solutions. This book will help analysts understand the contribution of decision-analytic modelling to the evaluation of health care programmes. ABOUT THE SERIES: Economic evaluation of health interventions is a growing specialist field, and this series of practical handbooks will tackle, in-depth, topics superficially addressed in more general health economics books. Each volume will include illustrative material, case histories and worked examples to encourage the reader to apply the methods discussed, with supporting material provided online. This series is aimed at health economists in academia, the pharmaceutical industry and the health sector, those on advanced health economics courses, and health researchers in associated fields.

Empirical measurement of impacts of active labour market programmes has started to become a central task of economic researchers. New improved econometric methods have been developed that will probably influence future empirical work in various other fields of economics as well. This volume contains a selection of original papers from leading experts, among them James J. Heckman, Noble Prize Winner 2000 in economics, addressing these econometric issues at the theoretical and empirical level. The theoretical part contains papers on tight bounds of average treatment effects, instrumental variables estimators, impact measurement with multiple programme options and statistical profiling. The empirical part provides the reader with econometric evaluations of active labour market programmes in Canada, Germany, France, Italy, Slovak Republic and Sweden.