Macroeconometric Forecasting (MFx)
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Bibliographic details
- Session: OL 24.181
- Location: Course conducted online
- Dates: May 1, 2024 - August 31, 2024 (18 weeks)
- Delivery method: Online Training
- Primary language: English
- Status: Deadline passed
Target audience
- 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.
Session details and logistics
- Session No.: OL 24.181
- Deadline passed
- Location: Course conducted online
- Date: May 1, 2024 - August 31, 2024 (18 weeks)
- Delivery Method: Online Training
- Primary Language: English
- Access requirements:
- Background in undergraduate statistics and basic econometrics is expected.
- 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 description
- Presented by the Institute for Capacity Development.
- Designed to reinforce macroeconomic forecasting and modeling skills and the use of modern econometric techniques.
- Lectures discuss the underlying theory and demonstrations show how to conduct empirical analyses using EViews.
- 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.
- Forecast uncertainty and policy analysis.
Course objectives
Upon completion of this 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.