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Long-Run Regressions: Theory and Application to US Asset Markets

Author

Listed:
  • Charlotte S. Hansen

    (Zicklin School of Business,Baruch College/CUNY)

  • Bjorn E. Tuypens

    (Oak Hill Platinum Partners, L.L.C.)

Abstract

The question of long-run predictability in the aggregate US stock market is still unsettled. This is due to the lack of a robust method to judge the statistical significance of long-run regressions under the maintained hypothesis. By developing a spectral theory of long-run regressions with both long-run dependent and independent variables, we demonstrate a version of Engle's (1974) conjecture that asymptotically correct standard errors can be computed by multiplying the ordinary least squares standard errors by the square root of 2/3 times the length of the forecast horizon. We generalize Stambaugh's (1999) bias formula to the long-run regression model proposed in this paper. In addition, we find, that for persistent predictive variables, the OLS estimator in our regression model is more efficient than the estimator in the predictive regressions suggested by Campbell and Shiller (1988) and Hodrick (1992). Application of our method shows thatthe long-run earnings yield significantly predicts up to 69% of the variation in the 10-year S&P 500 real return, and up to 49% of long-run bond returns.

Suggested Citation

  • Charlotte S. Hansen & Bjorn E. Tuypens, 2004. "Long-Run Regressions: Theory and Application to US Asset Markets," Finance 0410018, EconWPA.
  • Handle: RePEc:wpa:wuwpfi:0410018
    Note: Type of Document - pdf; pages: 85
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    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/fin/papers/0410/0410018.pdf
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. David McMillan & Alan Speight, 2006. "Non-linear long horizon returns predictability: evidence from six south-east Asian markets," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 13(2), pages 95-111, June.
    2. Yu-chin Chen & Kwok Ping Tsang, 2013. "What Does the Yield Curve Tell Us about Exchange Rate Predictability?," The Review of Economics and Statistics, MIT Press, vol. 95(1), pages 185-205, March.
    3. Charlotte S. Hansen & Bjorn E. Tuypens, 2004. "Proxying for Expected Returns with Price Earnings Ratios," Finance 0410019, EconWPA.
    4. David C. Ling & Andy Naranjo & Benjamin Scheick, 2014. "Investor Sentiment, Limits to Arbitrage and Private Market Returns," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 42(3), pages 531-577, September.
    5. Qiu, Mei & Pinfold, John F. & Rose, Lawrence C., 2011. "Predicting foreign exchange movements using historic deviations from PPP," International Review of Economics & Finance, Elsevier, vol. 20(4), pages 485-497, October.

    More about this item

    Keywords

    Forecasting; stock returns; spectral analysis; Hansen-Hodrick standard errors;

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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