Board of Governors of the Federal Reserve System (US)
Finance and Economics Discussion Series
Forecasting with Sufficient Dimension Reductions
Factor models have been successfully employed in summarizing large datasets with few underlying latent factors and in building time series forecasting models for economic variables. When the objective is to forecast a target variable y with a large set of predictors x, the construction of the summary of the xs should be driven by how informative on y it is. Most existing methods first reduce the predictors and then forecast y in independent phases of the modeling process. In this paper we present an alternative and potentially more attractive alternative: summarizing x as it relates to y, so that all the information in the conditional distribution of y|x is preserved. These y-targeted reductions of the predictors are obtained using Sufficient Dimension Reduction techniques. We show in simulations and real data analysis that forecasting models based on sufficient reductions have the potential of significantly improved performance.
Cite this item
Alessandro Barbarino & Efstathia Bura, Forecasting with Sufficient Dimension Reductions, Board of Governors of the Federal Reserve System (US), Finance and Economics Discussion Series 2015-74, 14 Sep 2015.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
- E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
Keywords: Diffusion Index; Dimension Reduction; Factor Models; Forecasting; Partial Least Squares; Principal Components
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