Background

In today’s interconnected global economy, the ability to make well-informed decisions based on accurate economic analysis is paramount. Traditional statistical methods often face challenges in incorporating prior knowledge and effectively managing uncertainty. In contrast, Bayesian modelling offers a sophisticated framework that not only addresses these challenges but also enhances the reliability and precision of predictions. For MEFMI member countries where achieving economic stability and fostering growth are paramount, adopting Bayesian methods can revolutionise how policymakers analyse data, forecast trends, and formulate robust economic policies. By embracing Bayesian modelling, these countries can leverage existing knowledge and empirical data more effectively, thereby improving their capacity to navigate complex economic landscapes and optimize resource allocation strategies. This course aims to empower participants with the skills and tools needed to harness the full potential of Bayesian statistics, fostering a new era of evidence-based decision-making in macroeconomic policy in the region.

Objective

The objective of this course is to empower participants with the requisite skills and tools to harness the full potential of Bayesian statistics, fostering a new era of evidence-based decision-making in macroeconomic policy in the region.

Course Content

The course will cover the following:

• i. Overview of Linear regression and VAR models;
• ii. Bayesian approach-advantages;
• iii. Probability Theory;
  • • Prior and posterior distributions;
  • • Likelihood function and Bayes’ formula;
  • • Conjugate priors and posterior inference;
• iv. Computational Methods;
• v. Applications; and
  • • Bayesian Linear Regression estimation
  • • Bayesian VAR estimation
• vi. Emerging issues in Bayesian Modelling.

Target Group

This course targets junior to mid-level officials responsible for macroeconomic modelling and forecasting at central banks, ministries of finance/ planning and statistics offices.