Comparing the Predictive Ability of Forecasting Models

Project Information

Competing Forecasts

Grant agreement ID: 797286

DOI

10.3030/797286

Project closed

EC signature date 19 March 2018

Start date 1 September 2018

End date 31 August 2020

Funded under

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Total cost

€ 165 598,80

EU contribution

€ 165 598,80

Coordinated by

ERASMUS UNIVERSITEIT ROTTERDAM
Netherlands

Objective

Constructing accurate predictions on different macroeconomic variables is a key issue for any central bank and other policy making institutions. For example, obtaining accurate inflation forecasts is important for setting interest rates. These institutions typically rely on a set of models to construct their forecasts and the question that often arises is which of these models performs the best in terms of predictive ability. The purpose of this project is to show that, when strong identification on these models is lost (an issue that is prevalent in many models used for prediction), our inference based on standard tests, that compare these models' predictive accuracy, can be misleading. A policy maker could thus falsely conclude that a particular model outperforms some other models in her set of competing models. This project will answer thus the question of how to perform correct inference about predictions in the setting in which the models are affected by identification deficiencies. To this end, I propose methods that make the standard predictive ability tests robust to this issue, while appropriately accounting for the parameter estimation error. The asymptotic distribution of the statistic will be derived under loss of strong identification. Bootstrap inference will be developed in order to obtain correct critical values. Monte Carlo simulations will analyze the finite sample properties of bootstrap critical values. Empirical studies will illustrate the consequences of using a standard vs. a bootstrap critical value. Results emerging from this project, will be of interest to a large academic community, central banks and other governmental organizations - that could take-up the new knowledge for policy making, as well as businesses that produce predictions - that could improve their forecast evaluation methodologies.

Keywords

• predictive ability assessment
• identification
• forecasting
• robust inference

Programme(s)

• H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions MAIN PROGRAMME
  
• H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
  

Topic(s)

• MSCA-IF-2017 - Individual Fellowships
  

Funding Scheme

MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)

Call for proposal

H2020-MSCA-IF-2017

Coordinator

ERASMUS UNIVERSITEIT ROTTERDAM

• Net EU contribution: € 165 598,80
• Address: BURGEMEESTER OUDLAAN 50 ; 3062 PA Rotterdam ; Netherlands
• Activity type: Higher or Secondary Education Establishments
• Total cost: € 165 598,80