Review of things that you are supposed to know, Autoregressive Models, VAR models and cointegration.
Introduction to MATLAB
Model selection: Small scale models; dynamic factor models. Common factors, principal components and Kalman filter. Output gap and coincident indicators.
Nowcasting and real time forecasting. Incorporating real time information in forecasting models. Real-time out-of-sample evaluation of the models. Data revisions and publication lags. Hard indicators vs indicators based on surveys. Non-seasonally adjusted series. Construction of daily business cycle indicators and forecasts with different models.
Session 4 and 5
Midas, Bridge Equations and Mix-Frequency VAR
Session 6 and 7
Forecasting turning points. Non-linear methods. Markov switching and threshold models, Univariate and multivariate analysis. Dynamic non-linear factor models. Non parametric models. Real-time assessment of recession probabilities with unbalanced information. Leading indicators of turning points. Non-linear real-time models for the US, the Euro-area and other economies. Forecasting stock returns. Forecasting second, third and fourth moments of stock returns. Credit and the business cycle. Forecasting amplitudes, durations, and shapes of recessions and expansions. The role of macroeconomic and financial variables in forecasting amplitudes, durations, and shapes of recessions and expansions. Recent pitfalls in the literature. Real-time tests of structural breaks.
Forecasting using large scale models. Advantages and disadvantages of large scale models. Principal components. Dynamic principal components.
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