Macroeconometric Forecasting (MFx)
Source details
- Canonical URL
- Macroeconometric Forecasting (MFx)
Other formats
Bibliographic details
- Session: OL 24.114
- Location: Course conducted online
- Dates: January 1, 2024 - April 15, 2024 (15 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.
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 usingEViews – 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 the macroeconomic forecasting and modeling skills of participants and their 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.