Time Series Econometrics (springer Texts In Business And Economics)
by Klaus Neusser /
2016 / English / EPUB
5.8 MB Download
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.
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.