Econometrics Seminar Series: Real-Time Data should be used in Forecasting Output Growth and Recessionary Events in the US

Speaker: Chrystalleni Aristidou

Speaker biography

Chrystalleni Aristidou is at the final year of her PhD in the University of Nottingham. The title of her PhD thesis is "Structural Breaks, Unit Root tests and Macro-Dynamics". Her thesis explores innovative model averaging techniques in various macroeconomic contexts, ranging from structural breaks inferences and inflation dynamics to forecasting using real-time data. At the same time, emphasis is placed in making valid inference in covariance-augmented unit root testing.

Abstract

The paper investigates whether forecast performance is enhanced by real-time datasets incorporating past data vintages and survey expectations. It proposes a modelling framework and evaluation procedure which allows a real-time and a final assessment of the use of the data in forecasting judged by various statistical and economic criteria. Analysing US output data over 1968q4-2015q1, we find both elements of the real-time data are useful with their contributions varying over time. Revisions data are particularly valuable for point and density forecasts of growth but survey expectations are important in forecasting rare recessionary events.