 Complete subset regressions with large-dimensional sets of predictorsGraham Elliott (), Antonio Gargano and Allan Timmermann Journal of Economic Dynamics and Control, 2015, vol. 54, issue C, 86-110 Abstract: We analyze the complete subset regression (CSR) approach of Elliott et al. (2013) in situations with many possible predictor variables. The CSR approach has the computational advantage that it can be applied even when the number of predictors exceeds the sample size. Theoretical results establish that the CSR approach achieves variance reduction and Monte Carlo simulations show that it offers a favorable bias–variance trade-off in the presence of many weak predictor variables. Empirical applications to out-of-sample predictability of U.S. unemployment, GDP growth and inflation show that CSR combinations produce more accurate point forecasts than a dynamic factor approach or univariate regressions that do not exploit the information in the cross-section of predictors. Keywords: Complete subset regression; Macroeconomic forecasts; Forecast combination; Factor models (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:54:y:2015:i:c:p:86-110 DOI: 10.1016/j.jedc.2015.03.004 Access Statistics for this article Journal of Economic Dynamics and Control is currently edited by J. Bullard, C. Chiarella, H. Dawid, C. H. Hommes, P. Klein and C. Otrok More articles in Journal of Economic Dynamics and Control from Elsevier |
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