Time series econometrics : learning through replication


Time series econometrics : learning through replication

John D. Levendis

(Springer texts in business and economics)

Springer, 2021

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"Springer Nature Switzerland AG 2018, corrected publication 2021"--T.p. verso

"Extras onlione"--On cover

Includes bibliographical references (p. 395-404) and index



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.


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.

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