In this book, the authors reject the theorem-proof approach as much as possible, and emphasize the practical application of econometrics. They show with examples how to calculate and interpret the numerical results.This book begins with students estimating simple univariate models, in a step by step fashion, using the popular Stata software system. Students then test for stationarity, while replicating the actual results from hugely influential papers such as those by Granger and Newbold, and Nelson and Plosser. Readers will learn about structural breaks by replicating papers by Perron, and Zivot and Andrews. They then turn to models of conditional volatility, replicating papers by Bollerslev. Finally, students estimate multi-equation models such as vector autoregressions and vector error-correction mechanisms, replicating the results in influential papers by Sims and Granger.

The book contains many worked-out examples, and many data-driven exercises. While intended primarily for graduate students and advanced undergraduates, practitioners will also find the book useful.

Auteur

John Levendis is an Associate Professor of Economics at Loyola University New Orleans, and is the Dr. John V. Connor Professor of Economics and Finance. Professor Levendis earned his Ph.D. in Economics from the University of Iowa. He has taught at Cornell College, the Economics University of Prague, the University of Iowa, and Southeastern Louisiana University.

Résumé

In this book, the author rejects the theorem-proof approach as much as possible, and emphasize the practical application of econometrics. They show with examples how to calculate and interpret the numerical results.

This book begins with students estimating simple univariate models, in a step by step fashion, using the popular Stata software system. Students then test for stationarity, while replicating the actual results from hugely influential papers such as those by Granger and Newbold, and Nelson and Plosser. Readers will learn about structural breaks by replicating papers by Perron, and Zivot and Andrews. They then turn to models of conditional volatility, replicating papers by Bollerslev. Finally, students estimate multi-equation models such as vector autoregressions and vector error-correction mechanisms, replicating the results in influential papers by Sims and Granger.

The book contains many worked-out examples, and many data-driven exercises. While intended primarily for graduate students and advanced undergraduates, practitioners will also find the book useful.

Contenu
Chapter 1: Introduction.- Chapter 2: ARMA (p,q) Processes.- Chapter 3: Non-Stationary and ARIMA (p,d,q) Processes.- Chapter 4: Unit Root and Stationarity Tests.- Chapter 5: Structural Breaks and Non-Stationairty.- Chapter 6: ARCH, GARCH and Time-Varying Variance.- Chapter 7: Multiple Time Series and Vector Autoregressions.- Chapter 8: Multiple Time Series and Cointegration.