Time-Series Analysis Econometrics pp 373-396 | Cite as Badi H. Baltagi (1) Email author (
[email protected]) 1. Center for Policy Research Department of Economics, Syracuse University, Syracuse, USA Chapter First Online: 09 April 2011 5.8k Downloads Part of the Springer Texts in Business and Economics book series (STBE)
Abstract There has been an enormous amount of research in time-series econometrics, and many economics departments have required a time-series econometrics course in their graduate sequence. Obviously, one chapter on this topic will not do it justice. Therefore, this chapter will focus on some of the basic concepts needed for such a course. Section 14.2 defines what is meant by a stationary time-series, while sections 14.3 and 14.4 briefly review the Box-Jenkins and Vector Autoregression (VAR) methods for time-series analysis. Section 14.5 considers a random walk model and various tests for the existence of a unit root. Section 14.6 studies spurious regressions and trend stationary versus difference stationary models. Section 14.7 gives a simple explanation of the concept of cointegration and illustrates it with an economic example. Finally, section 14.8 looks at Autoregressive Conditionally Heteroskedastic (ARCH) time-series.
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About this chapter Cite this chapter as: Baltagi B.H. (2011) Time-Series Analysis. In: Econometrics. Springer Texts in Business and Economics. Springer, Berlin, Heidelberg DOI (Digital Object Identifier) https://doi.org/10.1007/978-3-642-20059-5_14 Publisher Name Springer, Berlin, Heidelberg Print ISBN 978-3-642-20058-8 Online ISBN 978-3-642-20059-5 eBook Packages Business and Economics About this book Reprints and Permissions
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