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Macroeconometric Forecasting

Macroeconometric Forecasting (MFx)

Deadline passed

Session No.: OL 22.120

Location: Course conducted online

Date: January 1, 2022 - April 15, 2022 (15 weeks)

Primary Language: English

    Target Audience

    All government officials are welcome to register. The course is particularly relevant for officials involved in developing forecasts that are used to design and implement macroeconomic policy. The course is offered in English.

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    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. 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.

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    Course Description

    This course, presented by the Institute for Capacity Development, is 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. 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.
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    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.
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