Time Series Econometrics

Revised and updated for the second edition, this textbook allows students to work through classic texts in economics and finance, using the original data and replicating their results. In this book, the author rejects the theorem-proof approach as much as possible, and emphasizes 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 & Newbold, and Nelson & Plosser. Readers will learn about structural breaks by replicating papers by Perron, and Zivot & Andrews. They then turn to models of conditional volatility, replicating papers by Bollerslev. Students estimate multi-equation models such as vector autoregressions and vector error-correction mechanisms, replicating the results in influential papers by Sims and Granger. Finally, students estimate static and dynamic panel data models, replicating papers by Thompson, and Arellano & Bond.

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.

How to best start learning time series econometrics? Learning by doing. This is the ethos of this book. What makes this book useful is that it provides numerous worked out examples along with basic concepts. It is a fresh, no-nonsense, practical approach that students will love when they start learning time series econometrics. I recommend this book strongly as a study guide for students who look for hands-on learning experience."

--Professor Sokbae "Simon" Lee, Columbia University, Co-Editor of Econometric Theory and Associate Editor of Econometrics Journal.

Revised and updated for the 2nd edition Provides several worked-out examples that enable a more hands-on approach to learning the subject matter Request lecturer material: sn.pub/lecturer-material

Autorentext
John Levendis is Professor of Business Analytics and Economics and holder of the William Barnett Professorship in Free Enterprise Studies at Loyola University New Orleans (US). 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.

Inhalt
Introduction.- ARMA(p,q) Processes.- Model Selection in ARMA(p,q) processes.- Stationarity and Invertibility.- Non-stationarity and ARIMA(p,d,q) processes.- Seasonal ARMA(p,q) processe.- Unit root tests.- Structural Breaks.- ARCH, GARCH and Time-varying Variance.- Vector Autoregressions I: Basics.- Vector Autoregressions II: Extensions.- Cointegration and VECMs.- Static Panel Data Models.- Dynamic Panel Data Models.- Conclusion.