Optimal Time Interval Selection in Long-Run Correlation Estimation

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Optimal Time Interval Selection in Long-Run Correlation Estimation

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Pedro H. Albuquerque (Texas A&M International University)

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Pedro H. Albuquerque

Abstract

This paper presents an asymptotically optimal time interval selection criterion for the long-run correlation block estimator (Bartlett kernel estimator) based on the Newey-West and Andrews-Monahan approaches. An alignment criterion that enhances finite-sample performance is also proposed. The procedure offers an optimal yet unobtrusive alternative to the common practice in finance and economics of arbitrarily choosing time intervals or lags in correlation studies. A Monte Carlo experiment using parameters derived from Dow Jones returns data confirms that the procedures are MSE-superior to typical alternatives such as aggregation over arbitrary time intervals, parametric VAR estimation, and Newey-West covariance matrix estimation with automatic lag selection.

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Paper provided by EconWPA in its series Econometrics with number 0511017.

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Length: 23 pages
Date of creation: 23 Nov 2005
Date of revision: 27 Nov 2005
Handle: RePEc:wpa:wuwpem:0511017

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Find related papers by JEL classification:
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods

This paper has been announced in the following NEP Reports:

NEP-ALL-2005-12-09 (All new papers)
NEP-ECM-2005-12-09 (Econometrics)
NEP-ETS-2005-12-09 (Econometric Time Series)

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Other versions:
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Other versions:
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