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Financial Variables and the Simulated Out-of-Sample Forecastability of U.S. Output Growth Since 1985: An Encompassing Approach

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  • David E. Rapach
  • Christian E. Weber

Abstract

We reconsider the out-of-sample forecasting ability of a large number of financial variables with respect to real output growth over the 1985:1--1999:4 period. We show that models including financial variables display almost no forecasting ability relative to an autoregressive benchmark model over this period according to a mean squared forecast error metric. However, tests based on forecast encompassing indicate that many financial variables do, in fact, contain information that is useful for forecasting real output growth over the 1985:1--1999:4 out-of-sample period. Our results suggest that the extant literature exaggerates the demise of the forecasting power of financial variables with respect to real activity since the mid-1980s. (JEL C22, C53, E44, E32) Copyright 2004, Oxford University Press.

Suggested Citation

  • David E. Rapach & Christian E. Weber, 2004. "Financial Variables and the Simulated Out-of-Sample Forecastability of U.S. Output Growth Since 1985: An Encompassing Approach," Economic Inquiry, Western Economic Association International, vol. 42(4), pages 717-738, October.
    • Handle: RePEc:oup:ecinqu:v:42:y:2004:i:4:p:717-738
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    Cited by:

    1. Balcilar, Mehmet & Gupta, Rangan & Wohar, Mark E., 2017. "Common cycles and common trends in the stock and oil markets: Evidence from more than 150years of data," Energy Economics, Elsevier, vol. 61(C), pages 72-86.
    2. Nikolaos Antonakakis & Rangan Gupta & Aviral K. Tiwari, 2016. "Time-Varying Correlations between Inflation and Stock Prices in the United States over the Last Two Centuries," Working Papers 201605, University of Pretoria, Department of Economics.
    3. Kirsten Thompson & Renee Van Eyden & Rangan Gupta, 2015. "Identifying an index of financial conditions for South Africa," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 32(2), pages 256-274, June.
    4. Ming-Chih Lee & Chien-Liang Chiu & Wan-Hsiu Cheng, 2007. "Enhancing Forecast Accuracy By Using Long Estimation Periods," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 1(2), pages 1-9.
    5. Wei, Xiaoqiao & Yang, Yuhong, 2012. "Robust forecast combinations," Journal of Econometrics, Elsevier, vol. 166(2), pages 224-236.
    6. Mark E. Wohar & David E. Rapach, 2007. "Forecasting the recent behavior of US business fixed investment spending: an analysis of competing models This is a significantly revised version of our previous paper, 'Forecasting US Business Fixed ," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(1), pages 33-51.
    7. Jordan, Steven J. & Vivian, Andrew & Wohar, Mark E., 2017. "Forecasting market returns: bagging or combining?," International Journal of Forecasting, Elsevier, vol. 33(1), pages 102-120.
    8. Makin, Anthony J. & Narayan, Paresh Kumar & Narayan, Seema, 2014. "What expenditure does Anglosphere foreign borrowing fund?," Journal of International Money and Finance, Elsevier, vol. 40(C), pages 63-78.
    9. Rangan Gupta & Faaiqa Hartley, 2013. "The Role of Asset Prices in Forecasting Inflation and Output in South Africa," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 12(3), pages 239-291, December.
    10. Gupta, Rangan & Wohar, Mark, 2017. "Forecasting oil and stock returns with a Qual VAR using over 150years off data," Energy Economics, Elsevier, vol. 62(C), pages 181-186.
    11. Sousa, Ricardo M. & Vivian, Andrew & Wohar, Mark E., 2016. "Predicting asset returns in the BRICS: The role of macroeconomic and fundamental predictors," International Review of Economics & Finance, Elsevier, vol. 41(C), pages 122-143.
    12. Kirsten Thompson & Reneé van Eyden & Rangan Gupta, 2015. "Testing the Out-of-Sample Forecasting Ability of a Financial Conditions Index for South Africa," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 51(3), pages 486-501, May.
    13. Shiu-Sheng Chen, 2005. "A note on in-sample and out-of-sample tests for Granger causality," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(6), pages 453-464.
    14. Akhter Faroque & William Veloce & Jean-Francois Lamarche, 2012. "Have structural changes eliminated the out-of-sample ability of financial variables to forecast real activity after the mid-1980s? Evidence from the Canadian economy," Applied Economics, Taylor & Francis Journals, vol. 44(30), pages 3965-3985, October.
    15. Kirsten Thompson & Renee van Eyden & Rangan Gupta, 2013. "Identifying a financial conditions index for South Africa," Working Papers 201333, University of Pretoria, Department of Economics.
    16. Antonakakis, Nikolaos & Gupta, Rangan & Tiwari, Aviral K., 2017. "Has the correlation of inflation and stock prices changed in the United States over the last two centuries?," Research in International Business and Finance, Elsevier, vol. 42(C), pages 1-8.
    17. Furkan Emirmahmutoglu & Mehmet Bacilar & Nicholas Apergis & Beatrice D. Simo-Kengne & Tsangyao Chang & Rangan Gupta, 2016. "Causal Relationship between Asset Prices and Output in the United States: Evidence from the State-Level Panel Granger Causality Test," Regional Studies, Taylor & Francis Journals, vol. 50(10), pages 1728-1741, October.
    18. Abdullah Gulcu & Dilem Yildirim, 2018. "Smooth Breaks And Nonlinear Mean Reversion In Real Interest Parity: Evidence From East Asian Countries," ERC Working Papers 1804, ERC - Economic Research Center, Middle East Technical University, revised Feb 2018.
    19. Westerlund, Joakim & Narayan, Paresh Kumar, 2012. "Does the choice of estimator matter when forecasting returns?," Journal of Banking & Finance, Elsevier, vol. 36(9), pages 2632-2640.
    20. Panopoulou, Ekaterini, 2007. "Predictive financial models of the euro area: A new evaluation test," International Journal of Forecasting, Elsevier, vol. 23(4), pages 695-705.
    21. Ekaterini Panopoulou & Sotiria Plastira, 2014. "Fama French factors and US stock return predictability," Journal of Asset Management, Palgrave Macmillan, vol. 15(2), pages 110-128, April.

    More about this item

    JEL classification:

    • 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
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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