Esta serie de libros de texto se centra en temas clave de la econometría moderna, abordando temas que los estudiantes y investigadores encuentran a diario. Cada volumen está escrito para ser accesible después de un curso introductorio de econometría, sirviendo como una encuesta autorizada e independiente. Con un énfasis distintivo en la excelencia pedagógica, la serie proporciona un recurso invaluable para profundizar la comprensión en el campo. Es la primera serie en la disciplina que tiene como objetivo explícito a la población estudiantil.
The first volume of this comprehensive two-volume set delves into advanced topics in econometrics, offering in-depth analysis and methodologies for researchers and students. It aims to enhance understanding of complex economic models and statistical techniques, making it a valuable resource for those looking to deepen their knowledge in the field.
This book discusses the econometric foundations of structural vector autoregressive modeling, as used in empirical macroeconomics, finance, and related fields. Inhaltsverzeichnis 1. Introduction; 2. Vector autoregressive models; 3. Vector error correction models; 4. Structural VAR tools; 5. Bayesian VAR analysis; 6. The relationship between VAR models and other macroeconometric models; 7. A historical perspective on causal inference in macroeconometrics; 8. Identification by short-run restrictions; 9. Estimation subject to short-run restrictions; 10. Identification by long-run restrictions; 11. Estimation subject to long-run restrictions; 12. Inference in models identified by short-run or long-run restrictions; 13. Identification by sign restrictions; 14. Identification by heteroskedasticity or non-gaussianity; 15. Identification based on extraneous data; 16. Structural VAR analysis in a data-rich environment; 17. Nonfundamental shocks; 18. Nonlinear structural VAR models; 19. Practical issues related to trends, seasonality, and structural change; References; Index.
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.