SESSION 1 – INTRODUCTION TO FUNDAMENTAL CONCEPTS
White noise processes; Autocorrelation and Partial Autocorrelation Functions (ACF & PACF)
SESSION 2 – MODEL IDENTIFICATION
Autoregressive (AR), Moving Average (MA) and ARMA processes
SESSION 3 – PARAMETER ESTIMATION & MODEL DIAGNOSTICS AIC & BIC; Residual Analysis and Overfitting, ARCH-LM test
SESSION 4 – Case Study I
More practices on developing ARIMA model, a look into Forecasting
SESSION 5 – VOLATILITY MODELS
ARCH and GARCH models, Asymmetric GARCH, GARCH-in-Mean
SESSION 6 – MORE EXERCISE ON ARIMA-GARCH-type MODELS
Hands-on exercise on constructing and comparing ARIMA, GARCH and EGARCH models
SESSION 7 – TIME SERIES REGRESSION
Autocorrelation: Detection and Remedy, Durbin-Watson statistic
3 softwares for Time Series Analysis: Minitab, SPSS and EViews
SESSION 8 – UNIT ROOT TESTS
Nonstationarity, Order of Integration, ADF, PP and KPSS tests
SESSION 9 – ENGLE-GRANGER 2-STEP PROCEDURE
Spurious regression, Stationarity of residuals
SESSION 10 – ARDL COINTEGRATION
ARDL Bounds Test: Uniform Lagged, Diagnostic Checks: LM and CUSUM tests
SESSION 11 – ARDL LEVEL RELATION
General to Specific Procedure & Diagnostics, Long-run Coefficients, Short-run EC Representation
SESSION 12 – HANDS-ON ARDL MODELING APPROACH
More Hands-on exercise on ARDL Modeling