Andrea Naghi obtains a Marie Sklodowska-Curie Individual Fellowship
Andrea Naghi, Assistant Professor at Erasmus School of Economics and ERIM Associate Member, is awarded a Marie Sklodowska-Curie Individual Fellowship for her proposal ‘Competing Forecasts’. This prestigious EU Horizon2020 grant to the amount of 170,000 euro is meant to encourage the mobility of researchers across national borders within the European Union.
On Tuesday 20 February 2018, Dean Philip Hans Franses surprised Andrea with a bouquet of flowers to congratulate her with this achievement.
Since September 2017 Andrea Naghi is working in the Econometrics Department of Erasmus School of Economics. She obtained her PhD in Economics from the University of Warwick, UK in March 2017. During her PhD, she spent two academic years at the UC San Diego Economics Department as visiting PhD student. Her research interests are in the fields of Econometrics, Applied Econometrics and Quantitative Macroeconomics. In particular, she is interested in topics related to performing estimation and inference with models affected by identification deficiencies, predictive accuracy assessment, optimal forecasts and identification in DSGE models.
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.