糖心原创

School of Economics

Fabrizio Iacone (University of York)

Date(s)
Thursday 19th March 2015 (16:00-17:30)
Description

Autocorrelation robust inference using the Daniell kernel with fixed bandwidth (joint with J. Hualde)

Abstract

We consider alternative asymptotics for frequency domain based estimates of the long run variance, in which the bandwidth is kept fixed. For a weakly dependent process, this does not yield a consistent estimate of the long run variance, but the standardized mean has t limit distribution. For given bandwidth, we find that this limit is more precise than the standard normal one. In presence of fractionally integrated data, the limit distribution of the estimate is not standard, and we derive critical values for the standardized mean for various bandwidths. We find that this asymptotics provided a better approximation than with Memory Auto-correlation Consistent (MAC) estimate. In multivariate set up, fixed bandwidth asymptotics may be used to characterize the limit distribution in alternative to standard Narrow Band asymptotics.

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