This textbook series focuses on core themes in modern econometrics, addressing topics students and researchers encounter daily. Each volume is written to be accessible after an introductory econometrics course, serving as an authoritative, standalone survey. With a distinct emphasis on pedagogical excellence, the series provides an invaluable resource for deepening understanding in the field. It is the first series in the discipline to explicitly aim at the student population.
Time series econometrics is used for predicting future developments of variables of interest such as economic growth, stock market volatility or interest rates. A model has to be constructed, accordingly, to describe the data generation process and to estimate its parameters. Modern tools to accomplish these tasks are provided in this volume, which also demonstrates by example how the tools can be applied.
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.
Designed for students with a foundational understanding of econometrics and statistics, this textbook delves into qualitative econometric models. It provides a comprehensive introduction to the concepts and methodologies essential for analyzing qualitative data, emphasizing their application in economic research.