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The Role of Partisan Conflict in Forecasting the U.S. Equity Premium: A Nonparametric Approach

Author

Listed:
  • Rangan Gupta

    () (Department of Economics, University of Pretoria, South Africa)

  • John W. Muteba Mwamba

    () (Faculty of Economic and Financial Sciences, University of Johannesburg, South Africa)

  • Mark E. Wohar

    () (College of Business Administration, University of Nebraska at Omaha USA, and School of Business and Economics, Loughborough University, UK)

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 1981:1-2016:6. 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.

Suggested Citation

  • Rangan Gupta & John W. Muteba Mwamba & Mark E. Wohar, 2016. "The Role of Partisan Conflict in Forecasting the U.S. Equity Premium: A Nonparametric Approach," Working Papers 201686, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201686
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    References listed on IDEAS

    as
    1. Cheng, Chak Hung Jack & Hankins, William B. & Chiu, Ching-Wai (Jeremy), 2016. "Does US partisan conflict matter for the Euro area?," Economics Letters, Elsevier, vol. 138(C), pages 64-67.
    2. Azzimonti, Marina, 2018. "Partisan conflict and private investment," Journal of Monetary Economics, Elsevier, vol. 93(C), pages 114-131.
    3. John Y. Campbell, 2008. "Viewpoint: Estimating the equity premium," Canadian Journal of Economics, Canadian Economics Association, vol. 41(1), pages 1-21, February.
    4. Giancarlo Bruno, 2014. "Consumer confidence and consumption forecast: a non-parametric approach," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 41(1), pages 37-52, February.
    5. Bekiros, Stelios & Gupta, Rangan & Majumdar, Anandamayee, 2016. "Incorporating economic policy uncertainty in US equity premium models: A nonlinear predictability analysis," Finance Research Letters, Elsevier, vol. 18(C), pages 291-296.
    6. Manela, Asaf & Moreira, Alan, 2017. "News implied volatility and disaster concerns," Journal of Financial Economics, Elsevier, vol. 123(1), pages 137-162.
    7. Rapach, David E. & Wohar, Mark E. & Rangvid, Jesper, 2005. "Macro variables and international stock return predictability," International Journal of Forecasting, Elsevier, vol. 21(1), pages 137-166.
    8. Cai, Zongwu & Fan, Jianqing & Yao, Qiwei, 2000. "Functional-coefficient regression models for nonlinear time series," LSE Research Online Documents on Economics 6314, London School of Economics and Political Science, LSE Library.
    9. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, Elsevier.
    10. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
    11. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
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    Cited by:

    1. Rangan Gupta & Patrick Kanda & Mark E. Wohar, 2018. "Predicting Stock Market Movements in the United States: The Role of Presidential Approval Ratings," Working Papers 201830, University of Pretoria, Department of Economics.
    2. Rangan Gupta & Christian Pierdzioch & Refk Selmi & Mark E. Wohar, 2017. "Does Partisan Conflict Predict a Reduction in US Stock Market (Realized) Volatility? Evidence from a Quantile-on-Quantile Regression Model," Working Papers 201744, University of Pretoria, Department of Economics.

    More about this item

    Keywords

    : Equity Premium; Partisan Conflict Index; Linear and Nonparametric Predictive Regressions;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G1 - Financial Economics - - General Financial Markets
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation

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