I. Basics of time series analysis

· Stationarity and ergodicity. Linear processes. Lag operator.

· Innovations and Wold decomposition. AR, MA, ARMA, ARIMA.

· Trend stationarity and difference stationarity.

· Nonlinear processes. Processes with time-varying parameters.

II. Modeling methodology and model selection

· Structural and non-structural time series modeling.

· Object of dynamic modeling: conditional mean, conditional variance, conditional quantile, conditional direction, conditional density.

· Model selection: diagnostic testing, information criteria and prediction criteria. Model confidence sets.

· General-to-specific and specific-to-general methodologies. Data mining.

· Predictability and testing for predictability.

III. Modeling conditional mean

· Stationary AR models: properties, estimation, inference, forecasting.

· Stochastic and deterministic trends, unit root testing. Brownian motion, FCLT.

· Nonlinear autoregressions: threshold autoregressions, smooth transition autoregressions, Markov switching models, state-space models.

· Stationary VAR models: properties, estimation, analysis and forecasting. Nonlinear VAR.

· Spurious regression, cointegrating regression, and their asymptotics. Engle-Granger test.

IV. Modeling conditional variance and volatility

· The class of ARCH models: properties, estimation, inference and forecasting.

· Extensions: IGARCH, ARCH-t. Time-varying risk and ARCH-in-mean.

· Multivariate GARCH: vech, BEKK, CCC, DCC, DECO. Variance targeting.

· Other measures of financial volatility: RiskMetrics, ranges, realized volatility.

· MEM models for RV and ranges. HAR models for RV. Models for jumps.

V. Other topics on modeling and forecasting

· Ultra-high frequency data models: ACD, UHF–GARCH.

· Modeling and forecasting conditional density. ARCD modeling.

· Multivariate dynamic densities. Copula machinery.

· Modeling and forecasting direction-of-change. Directional predictability.

· Modeling and forecasting conditional quantiles. Value-at-risk. CAViaR model.

· Generalized autoregressive score models. MIDAS models.

VI. Analysis of structural stability

· Identification, estimation and testing for structural breaks. Andrews and Bai-Perron tests.

· Retrospection and monitoring for structural stability. CUSUM and other sequential tests.