A Unified Framework for Dimension Reduction in Forecasting

Alessandro Barbarino () and Efstathia Bura

No 2017-004, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)

Abstract: Factor models are widely used in summarizing large datasets with few underlying latent factors and in building time series forecasting models for economic variables. In these models, the reduction of the predictors and the modeling and forecasting of the response y are carried out in two separate and independent phases. We introduce a potentially more attractive alternative, Sufficient Dimension Reduction (SDR), that summarizes x as it relates to y, so that all the information in the conditional distribution of y|x is preserved. We study the relationship between SDR and popular estimation methods, such as ordinary least squares (OLS), dynamic factor models (DFM), partial least squares (PLS) and RIDGE regression, and establish the connection and fundamental differences between the DFM and SDR frameworks. We show that SDR significantly reduces the dimension of widely used macroeconomic series data with one or two sufficient reductions delivering similar forecasting performance to that of competing methods in macro-forecasting.

Keywords: Diffusion Index; Dimension Reduction; Factor Models; Forecasting; Partial Least Squares; Principal Components (search for similar items in EconPapers)
JEL-codes: C32 C53 C55 E17 (search for similar items in EconPapers)
Pages: 68 pages
Date: 2017-01-12
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedgfe:2017-04

DOI: 10.17016/FEDS.2017.004

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