Macroeconometric Forecasting (MFx)
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- Macroeconometric Forecasting (MFx)
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Bibliographic details
- Published: October 23, 2020
Upcoming offerings
| Start date | End date | Location | Delivery Method | Session No. | Primary & (Interpretation) language | Apply |
|---|---|---|---|---|---|---|
| May 1, 2026 | August 31, 2026 | Course conducted online | Online Training | OL 26.175 | English | Apply online by August 15, 2026 |
Course overview
- Presented by the Institute for Capacity Development, the course is designed to reinforce the macroeconomic forecasting and modeling skills of participants and their use of modern econometric techniques.
- Lectures cover underlying theory and demonstrations show how to conduct empirical analyses using EViews.
- The course focuses on four aspects of empirical model building and forecasting:
- data and model properties, such as stationarity/non-stationarity and co-integration;
- dynamic specifications, especially the use of error correction models;
- model evaluation, design, and simulation; and
- forecast uncertainty and policy analysis.
- Topic: General Macroeconomics.
Target audience and language
- All government officials are welcome to register.
- Particularly relevant for officials involved in developing forecasts that are used to design and implement macroeconomic policy.
- The course is offered in English.
Qualifications and technical requirements
- Participants are expected to have a background in undergraduate statistics and basic econometrics.
- Access to a computer with a reliable Internet connection and a Google Chrome web browser is essential.
- Demonstrations and applications are conducted using EViews – a popular software for estimating and simulating forecasting models in Windows.
- Temporary licenses for EViews are made available for the duration of the course.
Course objectives
Upon completion of the course, participants should be able to:
- Forecast time series and multiple equation models using EViews.
- Assess the statistical characteristics of time series and apply appropriate methods for forecasting.
- Construct a macroeconometric model using ARMA, VAR, and VECM methods.
- Use a variety of statistical techniques to evaluate the performance of a forecasting model.