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.
