Advanced Textbooks in Economics – serie

A Textbook on Macroeconomic Knowledge and Analysis

Two important new developments have occurred that have significant impact on the evolution of econometrics, namely, the end of the Cold War and the emergence of the information revolution in nearly all economies of the world.

The information revolution has had significant effect on data flows, making them much more timely, accessible, and descriptive of more parts of the economy. At the same time, it has changed the industrial structure of many economies, giving rise to increasing importance of the tertiary sectors (e.g. services). The new generation of hardware and software enables econometricians to handle larger and more complex problems, especially those that are data intensive and computer intrusive.

These major events require reconsideration and redrafting of some of the materials of the original edition.

The present volume retains the original structure of "Lectures on Microeconomic Theory" and takes up principles of constructing dynamic macroeconometric models and their use in economic analyses and forecasting, while introducing many updates, revisions and extensions. The description of the econometric methodology has been limited to specific applications of time series analysis, and the title has been changed to "Principles of Macroeconometric Modeling".

The first four chapters discuss the principles of specifying equations of structural macromodels, covering both developed marked economies, transition economies and world-wide models. The remaining chapters cover some major issues in the use of macromodels. The point of departure is model simulation, especially of the prevailing non-linear models, which is followed by model validation. The analysis of model dynamics covers economic fluctuations and the relevant implications of non-stationarity. The use of macromodels in policy analysis is presented next; it includes multiplier analysis and scenario simulations. The monograph ends up with forecasting being a special case of simulation analysis.

Rather than describing the various schools of economics or its history, this book aims to present current macroeconomic knowledge, with a few contributions from famous works and authors when absolutely necessary. It is aimed at those with some existing knowledge of the subject.

Rather than describing the various schools of economics or its history, this book aims to present current macroeconomic knowledge, with a few contributions from famous works and authors when absolutely necessary. It is aimed at those with some existing knowledge of the subject.

Theory and application of a variety of mathematical techniques in economics are presented in this volume. Topics discussed include: martingale methods, stochastic processes, optimal stopping, the modeling of uncertainty using a Wiener process, Itô's Lemma as a tool of stochastic calculus, and basic facts about stochastic differential equations. The notion of stochastic ability and the methods of stochastic control are discussed, and their use in economic theory and finance is illustrated with numerous applications. The applications covered include: futures, pricing, job search, stochastic capital theory, stochastic economic growth, the rational expectations hypothesis, a stochastic macroeconomic model, competitive firm under price uncertainty, the Black-Scholes option pricing theory, optimum consumption and portfolio rules, demand for index bonds, term structure of interest rates, the market risk adjustment in project valuation, demand for cash balances and an asset pricing model.

This book deals with microeconomic theory as it concerns general economic equilibrium and the implications for prices and resource allocation. Students with a background in mathematics will find in this volume a broad, comprehensible overview of the ideas which have contributed to our understanding of general equilibrium.

This book serves not only as an introduction, but also as an advanced text and reference source in the field of deterministic optimal control systems governed by ordinary differential equations. It also includes an introduction to the classical calculus of variations. An important feature of the book is the inclusion of a large number of examples, in which the theory is applied to a wide variety of economics problems. The presentation of simple models helps illuminate pertinent qualitative and analytic points, useful when confronted with a more complex reality. These models cover: economic growth in both open and closed economies, exploitation of (non-) renewable resources, pollution control, behaviour of firms, and differential games. A great emphasis on precision pervades the book, setting it apart from the bulk of literature in this area. The rigorous techniques presented should help the reader avoid errors which often recur in the application of control theory within economics.

The book first discusses in depth various aspects of the well-known inconsistency that arises when explanatory variables in a linear regression model are measured with error. Despite this inconsistency, the region where the true regression coeffecients lies can sometimes be characterized in a useful way, especially when bounds are known on the measurement error variance but also when such information is absent. Wage discrimination with imperfect productivity measurement is discussed as an important special case.

Next, it is shown that the inconsistency is not accidental but fundamental. Due to an identification problem, no consistent estimators may exist at all. Additional information is desirable. This information can be of various types. One type is exact prior knowledge about functions of the parameters. This leads to the CALS estimator. Another major type is in the form of instrumental variables. Many aspects of this are discussed, including heteroskedasticity, combination of data from different sources, construction of instruments from the available data, and the LIML estimator, which is especially relevant when the instruments are weak.

The scope is then widened to an embedding of the regression equation with measurement error in a multiple equations setting, leading to the exploratory factor analysis (EFA) model. This marks the step from measurement error to latent variables. Estimation of the EFA model leads to an eigenvalue problem. A variety of models is reviewed that involve eignevalue problems as their common characteristic.

EFA is extended to confirmatory factor analysis (CFA) by including restrictions on the parameters of the factor analysis model, and next by relating the factors to background variables.

These models are all structural equation models (SEMs), a very general and important class of models, with the LISREL model as its best-known representation, encompassing almost all linear equation systems with latent variables.

Estimation of SEMs can be viewed as an application of the generalized method of moments (GMM). GMM in general and for SEM in particular is discussed at great length, including the generality of GMM, optimal weighting, conditional moments, continuous updating, simulation estimation, the link with the method of maximum likelihood, and in particular testing and model evaluation for GMM.

The discussion concludes with nonlinear models. The emphasis is on polynomial models and models that are nonlinear due to a filter on the dependent variables, like discrete choice models or models with ordered categorical variables.