Time Series Econometrics: Learning Through Replication
by John D. Levendis (Author)

About the Author
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.

About this book
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.

Brief contents
1. Introduction
2. ARMA(p,q) Processes
3. Model Selection in ARMA(p,q) Processes
4. Stationarity and Invertibility
5. Non-stationarity and ARIMA(p,d,q) Processes
6. Seasonal ARMA(p,q) Processes
7. Unit Root Tests
8. Structural Breaks
9. ARCH, GARCH and Time-Varying Variance
10. Vector Autoregressions I: Basics
11. Vector Autoregressions II: Extensions
12. Cointegration and VECMs
13. Conclusion

Series: Springer Texts in Business and Economics
Pages: 409 pages
Publisher: Springer; 1st ed. 2018 edition (February 1, 2019)
Language: English
ISBN-10: 3319982818
ISBN-13: 978-3319982816

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