An Autoregressive Spectral Density Estimator at Frequency Zero for Nonstationarity Tests

Pierre Perron and Serena Ng ()

Cahiers de recherche from Universite de Montreal, Departement de sciences economiques

Abstract: Many unit root and cointegration tests require an estimate of the spectral density function at frequency zero at some process. Kernel estimators based on weighted sums of autocovariances constructed using estimated residuals from an AR(1) regression are commonly used. However, it is known that with substantially correlated errors, the OLS estimate of the AR(1) parameter is severely biased. in this paper, we first show that this least squares bias induces a significant increase in the bias and mean-squared error of kernel-based estimators.

JEL-codes: C10 C13 C19 (search for similar items in EconPapers)
Pages: 42 pages
Date: 1996
References: Add references at CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://hdl.handle.net/1866/2112 (application/pdf)

Related works:
Journal Article: AN AUTOREGRESSIVE SPECTRAL DENSITY ESTIMATOR AT FREQUENCY ZERO FOR NONSTATIONARITY TESTS (1998)
Working Paper: An Autoregressive Spectral Density Estimator at Frequency Zero for Nonstationarity Tests (1996)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:mtl:montde:9611

Access Statistics for this paper

More papers in Cahiers de recherche from Universite de Montreal, Departement de sciences economiques Contact information at EDIRC.
Bibliographic data for series maintained by Sharon BREWER ().