Time Series Econometrics

✍ Scribed by Klaus Neusser (auth.)

• Publisher: Springer International Publishing
• Year: 2016
• Tongue: English
• Leaves: 421
• Series: Springer Texts in Business and Economics
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-xxiv
Front Matter....Pages 1-1
Introduction and Basic Theoretical Concepts....Pages 3-24
Autoregressive Moving-Average Models....Pages 25-44
Forecasting Stationary Processes....Pages 45-66
Estimation of the Mean and the Autocorrelation Function....Pages 67-85
Estimation of ARMA Models....Pages 87-108
Spectral Analysis and Linear Filters....Pages 109-132
Integrated Processes....Pages 133-165
Models of Volatility....Pages 167-193
Front Matter....Pages 195-195
Introduction....Pages 197-199
Definitions and Stationarity....Pages 201-206
Estimation of Mean and Covariance Function....Pages 207-214
Stationary Time Series Models: Vector Autoregressive Moving-Average Processes (VARMA Processes)....Pages 215-224
Estimation of Vector Autoregressive Models....Pages 225-239
Forecasting with VAR Models....Pages 241-253
Interpretation and Identification of VAR Models....Pages 255-294
Cointegration....Pages 295-324
State-Space Models and the Kalman Filter....Pages 325-352
Generalizations of Linear Time Series Models....Pages 353-367
Back Matter....Pages 369-409

✦ Subjects

Econometrics; Macroeconomics/Monetary Economics//Financial Economics; Statistics for Business/Economics/Mathematical Finance/Insurance