Bücher der Reihe Themes in Modern Econometrics

The principal objective of this volume is to offer a complete presentation of the theory of GMM estimation as well as insights into the use of these methods in empirical studies. It is also designed to serve as a unified framework for teaching estimation theory in econometrics. The book's contributors are well-known authorities in the field.

Time series analysis has undergone many changes in recent years with the advent of unit roots and cointegration. Maddala and Kim present a comprehensive review of these important developments and examine structural change. The volume provides an analysis of unit root tests, problems with unit root testing, estimation of cointegration systems, cointegration tests, and econometric estimation with integrated regressors. The authors also present the Bayesian approach to these problems and bootstrap methods for small-sample inference. The chapters on structural change discuss the problems of unit root tests and cointegration under structural change, outliers and robust methods, the Markov-switching model and Harvey's structural time series model. Unit Roots, Cointegration and Structural Change is a major contribution to Themes in Modern Econometrics, of interest both to specialists and graduate and upper-undergraduate students.

Many economic theories depend on the presence or absence of a unit root for their validity, making familiarity with unit roots extremely important to econometric and statistical theory. This book introduces the literature on unit roots in a comprehensive manner to empirical and theoretical researchers in economics and other areas.

The recent financial crisis has heightened the need for appropriate methodologies for managing and monitoring complex risks in financial markets. The measurement, management, and regulation of risks in portfolios composed of credits, credit derivatives, or life insurance contracts is difficult because of the nonlinearities of risk models, dependencies between individual risks, and the several thousands of contracts in large portfolios. The granularity principle was introduced in the Basel regulations for credit risk to solve these difficulties in computing capital reserves. In this book, authors Patrick Gagliardini and Christian Gourieroux provide the first comprehensive overview of the granularity theory and illustrate its usefulness for a variety of problems related to risk analysis, statistical estimation, and derivative pricing in finance and insurance. They show how the granularity principle leads to analytical formulas for risk analysis that are simple to implement and accurate even when the portfolio size is large.

This is the second volume in a major two-volume set of advanced texts in econometrics. It is a work of synthesis that covers both the basic and more sophisticated models. The books are distinctive for their attention to intuitive reasoning and the presentation of many real-world economic examples.

This book provides simple and flexible (nonparametric) techniques for analyzing regression data. It includes a series of empirical examples including estimation of Engel curves and equivalence scales, scale economies, household gasoline consumption, housing prices, option prices and state price density estimation.

The principal objective of this volume is to offer a complete presentation of the theory of GMM estimation as well as insights into the use of these methods in empirical studies. It is also designed to serve as a unified framework for teaching estimation theory in econometrics. The book's contributors are well-known authorities in the field.

Economic and financial time series feature important seasonal fluctuations. Despite their regular and predictable patterns over the year, month or week, they pose many challenges to economists and econometricians. This book provides a thorough review of the recent developments in the econometric analysis of seasonal time series.

The cointegration revolution has had a substantial impact on applied analysis. The methods for conducting this analysis are sketched out, reminding the reader of the ideas underlying them and giving sufficient background for empirical work. The treatment can be used as a textbook for courses on applied time series econometrics.

This book is intended for use in a rigorous introductory PhD level course in econometrics, or in a field course in econometric theory. It covers the measure-theoretical foundation of probability theory, the multivariate normal distribution with its application to classical linear regression analysis, various laws of large numbers, central limit theorems and related results for independent random variables as well as for stationary time series, with applications to asymptotic inference of M-estimators, and maximum likelihood theory. Some chapters have their own appendices containing the more advanced topics and/or difficult proofs. Moreover, there are three appendices with material that is supposed to be known. Appendix I contains a comprehensive review of linear algebra, including all the proofs. Appendix II reviews a variety of mathematical topics and concepts that are used throughout the main text, and Appendix III reviews complex analysis. Therefore, this book is uniquely self-contained.

The cointegration revolution has had a substantial impact on applied analysis. The methods for conducting this analysis are sketched out, reminding the reader of the ideas underlying them and giving sufficient background for empirical work. The treatment can be used as a textbook for courses on applied time series econometrics.

This is the first volume in a major two-volume set of advanced texts in econometrics. It is a work of synthesis that covers both the basic and the more sophisticated statistical models. The books are distinctive for their attention to intuitive reasoning and the presentation of many real-world economic examples.

The first book to discuss the principles of the nonparametric approach to the topics covered in a first year graduate course in econometrics. The book will provide a new perspective on teaching and research in applied subjects in general and econometrics and statistics in particular.

This is the first fully comprehensive textbook to integrate traditional and modern time series econometric modelling. The mathematical rigour of the book is high but excessive technicalities have been avoided. The coverage represents a major reference tool for graduate students, researchers and applied economists.

This is the first volume in a major two-volume set of advanced texts in econometrics. It is a work of synthesis that covers both the basic and the more sophisticated statistical models. The books are distinctive for their attention to intuitive reasoning and the presentation of many real-world economic examples.

Structural vector autoregressive (VAR) models are widely used in many fields of economics. This book traces the evolution of the structural VAR approach and reviews its econometric foundations. It provides guidance to empirical researchers as to the most appropriate methods of estimating and evaluating structural VAR models.

This book provides a general framework for specifying, estimating and testing time series econometric models. Special emphasis is given to estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalised method of moments estimation, nonparametric estimation and estimation by simulation. An important advantage of adopting the principle of maximum likelihood as the unifying framework for the book is that many of the estimators and test statistics proposed in econometrics can be derived within a likelihood framework, thereby providing a coherent vehicle for understanding their properties and interrelationships. In contrast to many existing econometric textbooks, which deal mainly with the theoretical properties of estimators and test statistics through a theorem-proof presentation, this book squarely addresses implementation to provide direct conduits between the theory and applied work.

This textbook introduces students progressively to various aspects of qualitative models and assumes a knowledge of basic principles of statistics and econometrics. Inferring qualitative characteristics of data on socioeconomic class, education, employment status, and the like - given their discrete nature - requires an entirely different set of tools from those applied to purely quantitative data. Written in accessible language and offering cogent examples, students are given valuable means to gauge real-world economic phenomena. After the introduction, early chapters present models with endogenous qualitative variables, examining dichotomous models, model specification, estimation methods, descriptive usage, and qualitative panel data. Professor Gourieroux also looks at Tobit models, in which the exogenous variable is sometimes qualitative and sometimes quantitative, and changing-regime models, in which the dependent variable is qualitative but expressed in quantitative terms. The final two chapters describe models which explain variables assumed by discrete or continuous positive variables.