Estimation Bias and Inference in Overlapping Auto-regressions: Implications for the Target-Zone Literature

22 Pages Posted: 18 Jan 2008

See all articles by Zsolt Darvas

Zsolt Darvas

Budapest University of Economic Sciences and Public Administration

Date Written: 0000

Abstract

Samples with overlapping observations are used for the study of uncovered interest rate parity, the predictability of long-run stock returns and the credibility of exchange rate target zones. This paper quantifies the biases in parameter estimation and size distortions of hypothesis tests of overlapping linear and polynomial auto-regressions, which have been used in target-zone applications. We show that both estimation bias and size distortions of hypothesis tests are generally larger, if the amount of overlap is larger, the sample size is smaller, and autoregressive root of the data-generating process is closer to unity. In particular, the estimates are biased in a way that makes it more likely that the predictions of the Bertola Svensson model will be supported. Size distortions of various tests also turn out to be substantial even when using a heteroskedasticity and autocorrelation-consistent covariance matrix.

Suggested Citation

Darvas, Zsolt, Estimation Bias and Inference in Overlapping Auto-regressions: Implications for the Target-Zone Literature (0000). Oxford Bulletin of Economics and Statistics, Vol. 70, Issue 1, pp. 1-22, February 2008, Available at SSRN: https://ssrn.com/abstract=1084225 or http://dx.doi.org/10.1111/j.1468-0084.2007.00488.x

Zsolt Darvas (Contact Author)

Budapest University of Economic Sciences and Public Administration ( email )

Budapest H-1093
Hungary

HOME PAGE: http://www.uni-corvinus.hu/darvas

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