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Interpreting long-horizon estimates in predictive regressions

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This paper analyzes the asymptotic properties of long-horizon estimators under both the null hypothesis and an alternative of predictability. Asymptotically, under the null of no predictability, the long-run estimator is an increasing deterministic function of the short-run estimate and the forecasting horizon. Under the alternative of predictability, the conditional distribution of the long-run estimator, given the short-run estimate, is no longer degenerate and the expected pattern of coefficient estimates across horizons differs from that under the null. Importantly, however, under the alternative, highly endogenous regressors, such as the dividend-price ratio, tend to deviate much less than exogenous regressors, such as the short interest rate, from the pattern expected under the null, making it more difficult to distinguish between the null and the alternative.

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  • Erik Hjalmarsson, 2008. "Interpreting long-horizon estimates in predictive regressions," International Finance Discussion Papers 928, Board of Governors of the Federal Reserve System (U.S.).
  • Handle: RePEc:fip:fedgif:928
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    Cited by:

    1. Maynard, Alex & Ren, Dongmeng, 2019. "The finite sample power of long-horizon predictive tests in models with financial bubbles," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 418-430.
    2. Hjalmarsson, Erik, 2018. "Maximal predictability under long-term mean reversion," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 269-282.
    3. Hjalmarsson, Erik, 2012. "Some curious power properties of long-horizon tests," Finance Research Letters, Elsevier, vol. 9(2), pages 81-91.

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