The role of partisan conflict in forecasting the U.S. equity premium: A nonparametric approach

Rangan Gupta, John Weirstrass Muteba Mwamba and Mark Wohar ()

Finance Research Letters, 2018, vol. 25, issue C, 131-136

Abstract: Information on partisan conflict is shown to matter in forecasting the U.S. equity premium, especially when accounting for omitted nonlinearities in their relationship, via a nonparametric predictive regression approach over the monthly period 1981:01–2016:06. Unlike as suggested by a linear predictive model, the nonparametric functional coefficient regression that includes the partisan conflict index enhances significantly the out-of-sample excess stock returns predictability. This result is found to be robust when we use a quantile predictive regression framework to capture nonlinearity, especially when the market is found to be in its bullish mode (i.e., upper quantiles of the conditional distribution of the equity premium).

Keywords: Equity premium; Partisan conflict index; Linear and nonparametric predictive regressions (search for similar items in EconPapers)
JEL-codes: C14 C22 C53 G1 G18 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (13)

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Related works:
Working Paper: The Role of Partisan Conflict in Forecasting the U.S. Equity Premium: A Nonparametric Approach (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:25:y:2018:i:c:p:131-136

DOI: 10.1016/j.frl.2017.10.023

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