I thank Christopher Bowdler (editor), Casper G. de Vries, László Halpern, László Hunyadi, Gábor Kőrösi, Helmut Lütkepohl, Judit Neményi, Peter C. B. Phillips, András Simon, András Simonovits, János Vincze, two anonymous referees, and seminar participants at the Winter Symposium of the Econometric Society, the Spring Meeting of Young Economists, and the Econometric Institute of the Erasmus University, Rotterdam for comments and suggestions. The paper was finalized during my visit to the Tinbergen Institute, Rotterdam. I thank the Tinbergen Institute for its hospitality. I am alone responsible for any errors and for the views expressed in the paper.
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Estimation Bias and Inference in Overlapping Autoregressions: Implications for the Target-Zone Literature†
Article first published online: 19 SEP 2007
DOI: 10.1111/j.1468-0084.2007.00488.x
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How to Cite
Darvas, Z. (2008), Estimation Bias and Inference in Overlapping Autoregressions: Implications for the Target-Zone Literature. Oxford Bulletin of Economics and Statistics, 70: 1–22. doi: 10.1111/j.1468-0084.2007.00488.x
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Publication History
- Issue published online: 19 SEP 2007
- Article first published online: 19 SEP 2007
- Final Manuscript Received: April 2007
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Keywords:
- C22;
- F31
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 autoregressions, 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.