| 教学大纲 1. Review of Probability and Statistics ; 2 classes2. Review of Linear Regression Models ; 2 classes(a) Review of OLS(b) Review of BLUE3. Difference Equations and Lag Operators; 2 classes(a) First order difference equation(b) P-th Order Difference equation(c) Lag operators: p-th order difference equations4. Characteristics of Time Series; 10 classes(a) Time HistoriesDetrendingDifferencingTransformationsLinear Filters(b) Time Series RelationshipsAutocorrelation Function (ACF)Cross Correlation Function (CCF)Partial Autocorrelation Function (PACF)5. Univariate Stationary Time Series Models ; 12 classes(a) White noise(b) MA process and AR process(c) ARMA process(d) Autoregressive integrated moving average (ARIMA) models(e) Seasonal ARIMA models(f) Regression with correlated errors6. Principles of Forecasting ; 4 classes(a) Linear projection and ordinary least squares regression(b) Forecasting an AR(p) process(c) Forecasting an ARMA(p,q) process(d) Optimal forecasts for Gaussian processes(e) Sums of ARMA processes7. Estimation and Inference in Stationary ARMA Models; 4 classes(a) Maximum likelihood estimation: conditional maximum likelihood estimates(b) Numerical optimization(c) Statistical inference with MLE: asymptotic SE, LR test, LM test(d) Forecasting8. Vector Autoregressive Models (VAR); 6 classes,(a) Estimation and hypothesis testing(b) Order selection and checking the model adequacy(c) Granger causality tests(d) The impulse-response function(e) Variance decomposition(f) Forecasting9. Unit Root Processes; 4 classes(a) Asymptotic properties of an AR(1) when the true coefficient is unity(b) Asymptotic properties of an AR(p) and the ADF test for unit roots10. Cointegration; 4 classes(a) Testing for cointegration(b) Estimation and inference in VAR and ECM(c) Forecasting11. Liner Systems of Simultaneous Equations ; 4 classes(a) IV estimation and two-stage least squares: consistency and asymptotic distribution(b) Identification(c) MLE12. Nonlinear Time Series Models and Their Applications (; 10 classes) | 
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