TIME SERIES ECONOMETRICS
The course is devoted to the modern time series analysis. We will study various model selection procedures, and get acquainted with general principles of constructing and evaluating forecasting models. We will review popular linear models of the conditional mean dynamics such as ARs and VARs, and then concentrate on nonlinear models, especially those exhibiting regime switching. We will also review such issues as stationarity, trends, unit roots and cointegration. After that, we will turn to models of conditional variance as represented by the ARCH class. Finally, we will study some special methods like the analysis of structural breaks, retrospection and monitoring, and modeling of ultra-high frequency financial data.
+
Modeling the mean and modeling the variance.
+
Model selection: diagnostic testing, information criteria and
prediction criteria.
+
General-to-specific and specific-to-general methodologies. Data mining.
+
Forecasting principles. Forecast evaluation and testing for
predictability.
+
Linear AR models: properties, estimation, inference, forecasting.
+
Nonlinear time series modeling of the mean: SETAR, STAR, MSW models.
+
Linear VAR models: properties, estimation, analysis and forecasting.
Nonlinear VAR.
+
Stochastic and deterministic trends, unit roots. Spurious regression
and cointegration.
+
The class of ARCH models: properties, estimation, inference and
forecasting.
+
Extensions: IGARCH, ARCH-t, ARCD, ARCH-in-mean, multivariate GARCH.
+
Identifying and testing for structural breaks.
+
Retrospection and monitoring.
+
High frequency data models: ACD, UHF-GARCH.