Macroeconometric Forecasting (MFx)
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- Macroeconometric Forecasting (MFx)
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Bibliographic details
- Session: OL 26.175
- Location: Course conducted online
- Dates: May 1, 2026 - August 31, 2026 (17 weeks)
- Delivery method: Online Training
- Primary language: English
- Status: Deadline passed
Session and logistics
- Session No.: OL 26.175
- Deadline: Deadline passed
- Location: Course conducted online
- Date: May 1, 2026 - August 31, 2026 (17 weeks)
- Delivery Method: Online Training
- Primary Language: English
- Software: Demonstrations and applications are conducted using EViews
- Licenses: Temporary licenses for EViews are made available for the duration of the course
Target audience and qualifications
- 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
- Language: The course is offered in English
- Qualifications:
- 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
Course description and core content
- Presenting institution: Institute for Capacity Development
- Aim: To reinforce macroeconomic forecasting and modeling skills and the use of modern econometric techniques
- Instructional approach:
- Lectures discuss the underlying theory
- Demonstrations show how to conduct empirical analyses using EViews
- Focus areas (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
Learning 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.