Structural Econometric Models

This volume focuses on recent developments in the use of structural econometric models in empirical economics. The first part looks at recent developments in the estimation of dynamic discrete choice models. The second part looks at recent advances in the area empirical matching models.

Bringing together a collection of previously published work, this book provides a discussion of major considerations relating to the construction of econometric models that work well to explain economic phenomena, predict future outcomes and be useful for policy-making. Analytical relations between dynamic econometric structural models and empirical time series MVARMA, VAR, transfer function, and univariate ARIMA models are established with important application for model-checking and model construction. The theory and applications of these procedures to a variety of econometric modeling and forecasting problems as well as Bayesian and non-Bayesian testing, shrinkage estimation and forecasting procedures are also presented and applied. Finally, attention is focused on the effects of disaggregation on forecasting precision and the Marshallian Macroeconomic Model that features demand, supply and entry equations for major sectors of economies is analysed and described. This volume will prove invaluable to professionals, academics and students alike.

This book focuses on structural changes and economic modeling. It presents papers describing how to model structural changes, as well as those introducing improvements to the existing before-structural-changes models, making it easier to later on combine these models with techniques describing structural changes. The book also includes related theoretical developments and practical applications of the resulting techniques to economic problems. Most traditional mathematical models of economic processes describe how the corresponding quantities change with time. However, in addition to such relatively smooth numerical changes, economical phenomena often undergo more drastic structural change. Describing such structural changes is not easy, but it is vital if we want to have a more adequate description of economic phenomena – and thus, more accurate and more reliable predictions and a better understanding on how best to influence the economic situation.

"Within economics a relatively new way of modeling has dominated important subfields: structural modeling. The goal of this book is to give an overview on how the various streams of literatures in empirical industrial organization and quantitative marketing use structural econometric modeling to estimate the model parameters, give the economic-model-based predictions, and conduct the policy counterfactual experiments. The traditional way of modelling, called "reduced-form" builds its models from simple relationships between variables of interests, which are mostly linear. Structural econometric models start by specifying the structure of the economic model, and the variables are calibrated from real-world data. This method enables better predictions and policy counterfactuals, and has other benefits. When considering a hypothetical policy change using the traditional modeling method ("reduced form"), researchers can often only estimate whether an effect would be positive or negative. With a structural econometric model using real-world data, a researcher can obtain the magnitude of the effects resulting from a hypothetical change. But the ability of quantifying the effects associated with a hypothetical policy change comes with its costs: the nonlinearity from explicitly specifying the possible relationships makes the structural econometric approach generally much more difficult to implement than its reduced-form counterpart. Therefore this book will provide a much-needed resource on how to use these methods effectively in the fields in which they been used the most, empirical industrial organization and quantitative marketing"--

Econometric models are made up of assumptions which never exactly match reality. Among the most contested ones is the requirement that the coefficients of an econometric model remain stable over time. Recent years have therefore seen numerous attempts to test for it or to model possible structural change when it can no longer be ignored. This collection of papers from Empirical Economics mirrors part of this development. The point of departure of most studies in this volume is the standard linear regression model Yt = x;fJt + U (t = I, ... , 1), t where notation is obvious and where the index t emphasises the fact that structural change is mostly discussed and encountered in a time series context. It is much less of a problem for cross section data, although many tests apply there as well. The null hypothesis of most tests for structural change is that fJt = fJo for all t, i.e. that the same regression applies to all time periods in the sample and that the disturbances u are well behaved. The well known Chow test for instance assumes t that there is a single structural shift at a known point in time, i.e. that fJt = fJo (t

The revised edition of the essential resource on macroeconometrics Structural Macroeconometrics provides a thorough overview and in-depth exploration of methodologies, models, and techniques used to analyze forces shaping national economies. In this thoroughly revised second edition, David DeJong and Chetan Dave emphasize time series econometrics and unite theoretical and empirical research, while taking into account important new advances in the field. The authors detail strategies for solving dynamic structural models and present the full range of methods for characterizing and evaluating empirical implications, including calibration exercises, method-of-moment procedures, and likelihood-based procedures, both classical and Bayesian. The authors look at recent strides that have been made to enhance numerical efficiency, consider the expanded applicability of dynamic factor models, and examine the use of alternative assumptions involving learning and rational inattention on the part of decision makers. The treatment of methodologies for obtaining nonlinear model representations has been expanded, and linear and nonlinear model representations are integrated throughout the text. The book offers a rich array of implementation algorithms, sample empirical applications, and supporting computer code. Structural Macroeconometrics is the ideal textbook for graduate students seeking an introduction to macroeconomics and econometrics, and for advanced students pursuing applied research in macroeconomics. The book's historical perspective, along with its broad presentation of alternative methodologies, makes it an indispensable resource for academics and professionals.

Accompanying CD-ROM contains data and sample computer code for empirical problems.

As conceived by the founders of the Econometric Society, econometrics is a field that uses economic theory and statistical methods to address empirical problems in economics. It is a tool for empirical discovery and policy analysis. The chapters in this volume embody this vision and either implement it directly or provide the tools for doing so. This vision is not shared by those who view econometrics as a branch of statistics rather than as a distinct field of knowledge that designs methods of inference from data based on models of human choice behavior and social interactions. All of the essays in this volume and its companion volume 6B offer guidance to the practitioner on how to apply the methods they discuss to interpret economic data. The authors of the chapters are all leading scholars in the fields they survey and extend. *Part of the renowned Handbooks in Economics Series*Updates and expands the exisiting Handbook of Econometrics volumes*An invaluable reference written by some of the world's leading econometricians.