A New Test for Multiple Predictive Regression
Ke-Li Xu () and
Junjie Guo ()
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Ke-Li Xu: Department of Economics, Indiana University
Junjie Guo: School of Finance, Central University of Finance and Economics, Beijing, China
CAEPR Working Papers from Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington
We consider inference for predictive regressions with multiple predictors. Extant tests for predictability may perform unsatisfactorily and tend to discover spurious predictability as the number of predictors increases. We propose a battery of new instrumental-variables based tests which involve enforcement or partial enforcement of the null hypothesis in variance estimation. A test based on the few-predictors-at-a-time parsimonious system approach is recommended. Empirical Monte Carlos demonstrate the remarkable finite-sample performance regardless of numerosity of predictors and their persistence properties. Empirical application to equity premium predictability is provided.
Keywords: Curse of dimensionality; Lagrange-multipliers test; persistence; predictive regression; return predictability (search for similar items in EconPapers)
Pages: 53 pages
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Persistent link: https://EconPapers.repec.org/RePEc:inu:caeprp:2022001
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