Time Series Econometrics (Paperback, Softcover reprint of the original 1st ed. 2016)

This text presents modern developments in time series analysis and focuses on their application to economic problems. The book first introduces the fundamental concept of a stationary time series and the basic properties of covariance, investigating the structure and estimation of autoregressive-moving average (ARMA) models and their relations to the covariance structure. The book then moves on to non-stationary time series, highlighting its consequences for modeling and forecasting and presenting standard statistical tests and regressions. Next, the text discusses volatility models and their applications in the analysis of financial market data, focusing on generalized autoregressive conditional heteroskedastic (GARCH) models. The second part of the text devoted to multivariate processes, such as vector autoregressive (VAR) models and structural vector autoregressive (SVAR) models, which have become the main tools in empirical macroeconomics. The text concludes with a discussion of co-integrated models and the Kalman Filter, which is being used with increasing frequency. Mathematically rigorous, yet application-oriented, this self-contained text will help students develop a deeper understanding of theory and better command of the models that are vital to the field. Assuming a basic knowledge of statistics and/or econometrics, this text is best suited for advanced undergraduate and beginning graduate students.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Springer Texts in Business and Economics
Release date:	May 2018
First published:	2016
Authors:	Klaus Neusser
Dimensions:	235 x 155 x 27mm (L x W x T)
Format:	Paperback
Pages:	409
Edition:	Softcover reprint of the original 1st ed. 2016
ISBN-13:	978-3-319-81387-5
Categories:	Books > Business & Economics > Economics > Macroeconomics > General Books > Business & Economics > Economics > Econometrics > Economic statistics
LSN:	3-319-81387-0
Barcode:	9783319813875

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