Sylabus
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.
Anotace
Course requirements, grading, and attendance policies
• The course presumes reading of textbooks and publications, as well as practical computer work with real data.
• There will be weekly home assignments combining theoretical exercises and empirical practice (20% of the course grade).
• One will need programming econometric software to do empirical exercises. Julia, Python, R, MATLAB, GAUSS and other options are acceptable whenever appropriate.
• One may do empirics using low-level programming and get up to the exercise’s full credit (and master the techniques), or, alternatively, utilize embedded high-level commands/libraries and get up to 25% of the exercise’s full credit (and most likely not learn relevant techniques).
• There will be a presentation/mini-lecture (30-40 minutes) on a particular topic assigned far in advance (20% of the course grade).
• There will be a midterm and a final exam (30% of the grade each).
• All the above components are mandatory (two home assignments are excused – for this count but not for the score) for getting a passing grade.
• Discussion sections will be devoted to solving problems and discussing relevant (both theoretical and applied) literature. Active participation in discussion sections will be awarded by up to bonus 10% of the course grade.
Academic integrity policy
Cheating, plagiarism, and any other violations of academic ethics at CERGE-EI are not tolerated.