An Autoregressive Spectral Density Estimator at Frequency Zero for Nonstationarity Tests

Autor: Pierre Perron, Serena Ng
Přispěvatelé: Université de Montréal. Faculté des arts et des sciences. Département de sciences économiques
Rok vydání: 1996
Předmět:
Economics and Econometrics
[JEL:C10] Mathématiques et méthodes quantitatives - Économétrie et méthodes statistiques
généralités - Généralités
Autocorrelation
Estimator
Spectral density
jel:C13
[JEL:C10] Mathematical and Quantitative Methods - Econometric and Statistical Methods: General - General
jel:C10
[JEL:C13] Mathématiques et méthodes quantitatives - Économétrie et méthodes statistiques
généralités - Estimations
[JEL:C19] Mathématiques et méthodes quantitatives - Économétrie et méthodes statistiques
généralités - Divers
Regression
[JEL:C19] Mathematical and Quantitative Methods - Econometric and Statistical Methods: General - Other
Autoregressive model
jel:C19
[JEL:C13] Mathematical and Quantitative Methods - Econometric and Statistical Methods: General - Estimation
Kernel (statistics)
Statistics
Unit root
Truncation (statistics)
Social Sciences (miscellaneous)
Mathematics
Zdroj: Scopus-Elsevier
Popis: Many unit root and cointegration tests require an estimate of the spectral density function at frequency zero of some process. Commonly used are kernel estimators based on weighted sums of autocovariances constructed using estimated residuals from an AR(1) regression. 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 (MSE) of kernel-based estimators. We then consider a variant of the autoregressive spectral density estimator that does not share these shortcomings because it bypasses the use of the estimate from the AR(1) regression. Simulations and local asymptotic analyses show its bias and MSE to be much smaller than those of a kernel-based estimator when there is strong negative serial correlation. We also include a discussion about the appropriate choice of the truncation lag.
Databáze: OpenAIRE
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