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Supervision in Factor Models Improving Economic Forecasts

"Decision makers at central banks, government institutions, financial institutions, and academic researchers today are inundated by economic data. Time series from different sources, at different levels of accuracy and aggregation... ver más

30/04/2017

100K€

Presupuesto del proyecto: 100K€

Líder del proyecto

AARHUS UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2017-04-30 No tenemos la información de la convocatoria

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1 Participantes

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Líder del proyecto

AARHUS UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 100K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto "Decision makers at central banks, government institutions, financial institutions, and academic researchers today are inundated by economic data. Time series from different sources, at different levels of accuracy and aggregation are available. This proposal aims at a class of models that extracts a small number of driving factors out of very large data sets in order to enable decision makers to compute their forecasts from this tractable set of factors (Stock and Watson, 2002b). These factor models start from a simple linear regression and identify the factors that contribute most to the variation of the predictors on the right-hand side of the equation. They do not consider the variation of the forecast target on the left-hand side of the equation. In earlier work (Hillebrand, Lee, Li, and Huang, 2012), we have proposed a method that informs the selection of the factors about the variation in the forecast target and thereby select factors that have more forecast power for the target. This connection between factor selection and target is called supervision. We have applied this method to forecasting economic output and inflation from yield curve data, that is, the interest charged for public and corporate debt at different maturities, an issue at the forefront of the current Euro crisis. In this proposal, we aim to (1) find the analytic reasons for the forecast improvements that result from our method, (2) extend this method to a dynamic factor model that is supervised for the forecast target, (3) apply the extended dynamic method to yield curve and output/inflation data, and (4) establish the link between supervised estimation and shrinkage estimation. This is achieved by conducting research, hosting visitors for seminars and workshops, and in particular by hosting an ""Advances in Econometrics"" conference in Aarhus. Funding of the proposal will significantly contribute to the establishment and retainment of the investigator, in line with the People work program."

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