Forschungsgruppe Angewandte Statistik
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2020-11-24 [ ]

Project Announcement: Sufficient Dimension Reduction Methodology in Forecasting

Sufficient Dimension Reduction Methodology in Forecasting.

Principal Investigator: Efstathia Bura
Project Assistants: Karl Oskar Ekvall, Manfred Deistler, Lukas Fertl, Barbara Brune, Daniel Kapla

Starting Date: December 2017

Funded by FWF - Der Wissenschaftsfonds 

Economists and policy makers have more data at their disposal than ever before. Extracting the most relevant information prevents reacting to idiosyncratic movements and can lead to more precise forecasts and macro/microeconomic analyses. However, how to use these data effectively is an open problem.

Dynamic Factor Models are pervasive in macro-econometrics and financial econometrics for both measuring co-movement and forecasting time series. However, the data reduction in DFMs comprises of summarizing the information in large data sets with a few components that capture a large proportion of their total variability without considering the forecasting ability of the reduced data.

Sufficient Dimension Reduction (SDR) is a collection of tools for reducing the dimension of multivariate data in regression problems without losing inferential information for modeling the response. SDR uses many noisy signals in the observable data to extract information about the underlying structural sources of comovement that can be used to inform the building of forecasting models.

This project will extend existing and develop new SDR methodology in econometric modeling and forecasting. SDR methods and data analysis tools will be developed to identify and estimate exhaustive reductions, including nonlinear data reductions, which have been marginally investigated in the DFM context.

Furthermore, SDR methods for (a) targeted PCA and (b) for large p-small T (many predictors, few observations) time series regressions based on Krylov subspaces will be developed.

Envelope models for multivariate response forecasting, such as central banks' macro forecasts, will be also developed and applied.

The proposed research intends to make a significant contribution to the development of statistical tools that reduce data complexity in order to understand and model the underlying relationships and structures that drive the economy and obtain more accurate forecasts.

Forschungsportal TU Wien