Macroeconometric Forecasting (MFx)
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- Macroeconometric Forecasting (MFx)
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Bibliographic details
- Session: OL 23.199
- Location: Course conducted online
- Dates: May 1, 2023 - August 31, 2023 (18 weeks)
- Delivery method: Online Training
- Primary language: English
- Status: Deadline passed
Target audience and delivery
- Target audience: All government officials are welcome to register; particularly relevant for officials involved in developing forecasts used to design and implement macroeconomic policy.
- Session No.: OL 23.199
- Location: Course conducted online
- Date: May 1, 2023 - August 31, 2023 (18 weeks)
- Delivery Method: Online Training
- Primary Language: English
Qualifications and technical requirements
- Expected background: undergraduate statistics and basic econometrics.
- Essential equipment: access to a computer with a reliable Internet connection and a Google Chrome web browser.
- Software: demonstrations and applications conducted using EViews.
- Licenses: Temporary licenses for EViews are made available for the duration of the course.
Course description and focus areas
- Presented by the Institute for Capacity Development to reinforce macroeconomic forecasting and modeling skills and the use of modern econometric techniques.
- Lectures discuss 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.
Course objectives
- 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.
References