Predictive Regressions: A Reduced-Bias Estimation Method

By Y. Amihud, Clifford M. Hurvich. Journal of Financial and Quantitative Analysis, 2004

309 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/91c8fa3984dd8bea190980ea94a9adc0d3c1301e

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Citations in the corpus

Catering Through Nominal Share Prices (2009, unknown): “Moreever, the reduced-bias estimator of Amihud and Hurvich (2004) indicates virtually no bias for either predictor in our setting.”
The Myth of Long-Horizon Predictability (2005, unknown): “…at other small sample issues such as persistence and spurious regression, as well as this bias, include Foster, Sarkissian and Simin (2003), Jansson and Moreira (2006), Amihud and Hurvich (2004), Torous, Valkanov and Yan (2004), Campbell and Yogo (2006), and Powell, Shi, Smith and Whaley (2005).”
Investor sentiment aligned: : A powerful predictor of stock returns (2015, methodology): “Second, we calculate the Stambaugh (1999) bias-adjusted regression coefficients following Amihud and Hurvich (2004) and Amihud, Hurvich, and Wang (2009).”
Time-Varying Liquidity and Momentum Profits (2016, methodology): “Our results are robust to augmentation of the regression estimates for serial correlations in the explanatory variables prescribed in Amihud and Hurvich (2004).”
Testing predictive regression models with nonstationary regressors (2014, unknown): “To deal with this problem, by assuming, commonly done in the literature (see Amihud and Hurvich(2004)andAmihudetal.”
THE INTERTEMPORAL RELATION BETWEEN EXPECTED RETURNS AND RISK (2008, methodology): “Stambaugh (1999), Amihud and Hurvich (2004), and Lewellen (2004) point out that a small sample bias exists in time-series predictive regressions when the regression disturbances are correlated with the regressors’ innovations.”
Manager sentiment and stock returns (2019, background): “Amihud and Hurvich (2004), Lewellen (2004), Campbell and Yogo (2006), and Amihud, Hurvich, and Wang (2009) develop predictive regression tests that explicitly account for the Stambaugh small-sample bias.”
Predictive regression under various degrees of persistence and robust long-horizon regression (2013, methodology): “…regressors — see Kostakis et al. (2010) — and this is the primary case of interest in practice. subsequent researchers, e.g., Kothari and Shanken (1997), β̂adj = β̂ + Σ̂0xΣxx 1 + 3ρ̂ n , (2.5) and Amihud and Hurvich (2004) refined this estimator by using a second order bias correction.”
Can investor sentiment be a momentum time-series predictor? Evidence from China (2017, methodology): “Of course, the small-sample bias has made itself a hot topic in the econometric literature, as multiple bias-reduction techniques have been proposed in the literature (Amihud & Hurvich 2004; Amihud et al. 2009; Chiquoine & Hjalmarsson 2009; Zhu 2013).”
Multiple-Predictor Regressions: Hypothesis Testing (2009, methodology): “, p), v c t = xt − Θ̂ − Φ̂xt−1, Φ̂ is the reduced-bias VAR(1)”
Robust econometric inference for stock return predictability (2011, methodology): “The notable exception is the iterative procedure of Amihud, Hurvich, and Wang (2009), which is based on the augmented regression method of Amihud and Hurvich (2004) and accommodates multiple regressors in a single-horizon predictive setup under the restriction that the predictors are stationary.”
Measuring Investor Sentiment with Mutual Fund Flows (2012, methodology): “Due to the persistence of the explanatory variables, the table also presents the Amihud and Hurvich (2004) correction for a single persistent regressor (A–”