Faculty Digital Archive

Archive@NYU >
Stern School of Business >
IOMS: Statistics Working Papers >

Please use this identifier to cite or link to this item: http://hdl.handle.net/2451/26345

Title: Predictive Regressions: A Reduced-Bias Estimation Method
Authors: Amihud, Yakov
Hurvich, Clifford M.
Keywords: Stock Returns
Dividend Yields
Autoregressive Models
Issue Date: 26-Nov-2002
Publisher: Stern School of Business, New York University
Series/Report no.: SOR-2002-7
Abstract: We propose a direct and convenient reduced-bias estimator of predictive regression coefficients, assuming that the regressors are Gaussian first-order autoregressive with errors that are correlated with the error series of the dependent variable. For the single-regressor model, Stambaugh (1999) shows that the ordinary least squares estimator of the predictive regression coefficient is biased in small samples. Our estimation method employs an augmented regression which uses a proxy for the errors in the autoregressive model. We also develop a heuristic estimator of the standard error of the estimated predictive coefficient which performs well in simulations, and show that the estimated coefficient of the errors and its squared standard error are unbiased. We analyze the case of multiple predictors that are first-order autoregressive and derive bias expressions for both the ordinary least squares and our reduced-bias estimated coefficients. The effectiveness of our estimation method is demonstrated by simulations.
URI: http://hdl.handle.net/2451/26345
Appears in Collections:IOMS: Statistics Working Papers

Files in This Item:

File Description SizeFormat
02-07.pdf216.87 kBAdobe PDFView/Open

Items in Faculty Digital Archive are protected by copyright, with all rights reserved, unless otherwise indicated.

 

The contents of the FDA may be subject to copyright, be offered under a Creative Commons license, or be in the public domain.
Please check items for rights statements. For information about NYU’s copyright policy, see http://www.nyu.edu/footer/copyright-and-fair-use.html 
Valid XHTML 1.0 | CSS