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Journal of Time Series Econometrics

Editor-in-Chief: Hidalgo, Javier


CiteScore 2018: 0.20

SCImago Journal Rank (SJR) 2018: 0.323
Source Normalized Impact per Paper (SNIP) 2018: 0.291

Mathematical Citation Quotient (MCQ) 2018: 0.03

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ISSN
1941-1928
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Penalized Averaging of Parametric and Non-Parametric Quantile Forecasts

Jan G. De Gooijer / Dawit Zerom
Published Online: 2019-12-18 | DOI: https://doi.org/10.1515/jtse-2019-0021

Abstract

We propose a hybrid penalized averaging for combining parametric and non-parametric quantile forecasts when faced with a large number of predictors. This approach goes beyond the usual practice of combining conditional mean forecasts from parametric time series models with only a few predictors. The hybrid methodology adopts the adaptive LASSO regularization to simultaneously reduce predictor dimension and obtain quantile forecasts. Several recent empirical studies have considered a large set of macroeconomic predictors and technical indicators with the goal of forecasting the S&P 500 equity risk premium. To illustrate the merit of the proposed approach, we extend the mean-based equity premium forecasting into the conditional quantile context. The application offers three main findings. First, combining parametric and non-parametric approaches adds quantile forecast accuracy over and above the constituent methods. Second, a handful of macroeconomic predictors are found to have systematic forecasting power. Third, different predictors are identified as important when considering lower, central and upper quantiles of the equity premium distribution.

Keywords: large database; non-parametric; parametric; penalized averaging; quantile forecasting

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About the article

Published Online: 2019-12-18


Citation Information: Journal of Time Series Econometrics, 20190021, ISSN (Online) 1941-1928, DOI: https://doi.org/10.1515/jtse-2019-0021.

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