Whether you deal with forecasting at a Central Bank, public institution, bank or consultancy firm; or you use forecasting techniques in your research, this is the perfect course to bring you up to date with the latest methods in the forecasting profession. We begin softly by reviewing some classic time series methods and standard point and density forecasting tools (fan charts), but rapidly turn to the state-of-the-art forecasting methods such as Mixed Frequency Data Sampling (MIDAS), Regime (or Markov) Switching models, and Bayesian forecasting techniques.
The focus will be more on the empirical implementation of the techniques than on their theoretical underpinnings. The techniques will be illustrated with several empirical applications, and then implemented in EViews. While experience in forecasting is advantageous, the course is equally suitable for professionals who have just recently began to forecast macroeconomic and financial indicators. We are flexible and the course can easily be accommodated to the level of the participants. Previous knowledge and experience in econometrics is however, essential.
This course is aimed at:
- Economists and statisticians at Central Banks, public institutions, financial institutions, consultancy firms, or firms who deal with forecasting in their daily work;
- Academics and research economists who use, or are interested in forecasting techniques for their research;
- Professionals involved in rating activities.
This comprehensive webinar is hosted through Zoom and runs over a total of 9 hours, with 4 hours each day (2 in the morning and 2 in the afternoon) with an extra Q&A session on the second day.
Day 1: Point and density Forecasting
1. Density forecasting with univariate linear models
- Review of basic forecasting formulae for the linear regression model;
- ARMA models: specification, estimation, testing and forecasting;
- Multistep estimation vs iterated formulae for h-step ahead forecasting;
- Rolling vs. recursive forecasting schemes;
- Pooling alternative forecasts;
2. Density forecasting with VARs and Bayesian VARs
- VAR models: specification, estimation and testing;
- VAR models: density forecasting via the bootstrap
- Forecasting with cointegrated VARs;
- Forecasting with Bayesian Vector Autoregressions;
Day 2: Mixed Frequency Data (MIDAS), Nowcasting, Volatility Models, Regime Switching Models
1. Forecasting with mixed frequency data
- Bridge models;
- MIDAS models: specification, estimation, forecasting;
- Unrestricted MIDAS models;
2. FModels for forecasting volatility
- Estimation and specification of GARCH models;
- E-GARCH models;
- Constructing point, interval and density forecasts;
3. Forecasting with regime switching models
- Nonlinear models
- Smooth transition and threshold autoregressive models
- Markov switching models
The EViews Help Files (PDF manuals) provide detailed information regarding each topic, so it is advised that delegates familiarise themselves with the manual readings to be prepared for the course.
The course assumes that attendees:
- Have an intermediate to advanced level University training (or equivalent) in econometrics.
- Have an intermediate level University training (or equivalent) in macroeconomics and/or finance.
- Have experience in forecasting.
- Have a working knowledge of EViews software.
Terms & Conditions
- Student registrations: Attendees must provide proof of full time student status at the time of booking to qualify for student registration rate (valid student ID card or authorised letter of enrolment).
- Additional discounts are available for multiple registrations.
- Delegates are provided with temporary licences for the software(s) used in the course and will be instructed to download and install the software prior to the start of the course.
- Payment of course fees required prior to the course start date.
- Registration closes 1 Calendar days prior to the start of the course.
- 100% fee returned for cancellations made over 28-calendar days prior to start of the course.
- 50% fee returned for cancellations made 14-calendar days prior to the start of the course.
- No fee returned for cancellations made less than 14-calendar days prior to the start of the course.
The number of delegates is restricted. Please register early to guarantee your place.