Estimation Bias and Inference in Overlapping Autoregressions: Implications for the Target-Zone Literature
AbstractSamples 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. Copyright 2008 Blackwell Publishing Ltd and the Department of Economics, University of Oxford.
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Bibliographic InfoArticle provided by Department of Economics, University of Oxford in its journal Oxford Bulletin of Economics and Statistics.
Volume (Year): 70 (2008)
Issue (Month): 1 (02)
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Other versions of this item:
- Zsolt Darvas, 2007. "Estimation Bias and Inference in Overlapping Autoregressions: Implications for the Target Zone Literature," Working Papers 0701, Department of Mathematical Economics and Economic Analysis, Corvinus University of Budapest.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- F31 - International Economics - - International Finance - - - Foreign Exchange
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