Econometrics: Forecast evaluation and selection in unstable environments
Dr Katja Smetanina, University of Chicago Booth
'Forecast Evaluation and Selection in Unstable Environments'
Friday 17 December 4pm-5pm
Zoom online seminar
Out-of-sample tests are widely used for evaluating and selecting between models forecasts in economics and finance. Although widely used, underlying these tests is often the assumption of constant relative performance between competing models which is invalid for many practical applications. We propose a new two-step methodology designed specifically for forecast evaluation and selection in a world of changing relative performance. In the first step we estimate the time-varying mean and variance of the series for forecast loss differences, and in the second step we use these estimates to compare and rank models in a changing world. We show that our tests have high power against a variety of fixed and local alternatives.
Ekaterina Smetanina is Assistant of Econometrics and Statistics at the University of Chicago Booth School of Business where she is also Asness Junior Faculty Fellow and Biehler Junior Faculty Fellow. Ekaterina works in the fields of econometrics, financial economics, empirical finance, and forecasting. Her main research looks to develop new econometric models to analyse different aspects of financial markets, in particular with focus on the topic of forecasting. She also works on the development of robust forecast evaluation methodologies, which are designed to be applicable in a wide variety of real-world situations.
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First published: 8 December 2021