ROBUST COVARIANCE MATRIX ESTIMATION: HAC ESTIMATES WITH LONG MEMORY/ANTIPERSISTENCE CORRECTION

Peter M. Robinson

53 citations0/0 files storedEconometric Theory

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Financial Risk and Volatility Modeling Advanced Statistical Methods and Models Spatial and Panel Data Analysis

Abstract

Smoothed nonparametric estimates of the spectral density matrix at zero frequency have been widely used in econometric inference, because they can consistently estimate the covariance matrix of a partial sum of a possibly dependent vector process. When elements of the vector process exhibit long memory or antipersistence such estimates are inconsistent. We propose estimates which are still consistent in such circumstances, adapting automatically to memory parameters that can vary across the vector and be unknown.

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References

1991Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation 1995Gaussian Semiparametric Estimation of Long Range Dependence 1994Semiparametric Analysis of Long-Memory Time Series 1998The mean squared error of Geweke and Porter‐Hudak's estimator of the memory parameter of a long‐memory time series 2002Determination of cointegrating rank in fractional systems 1996Bandwidth Choice in Gaussian Semiparametric Estimation of Long Range Dependence

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2014HAC Corrections for Strongly Autocorrelated Time Series 2011Asymptotic theory for nonparametric regression with spatial data 2008Multiple local whittle estimation in stationary systems 2009Two estimators of the long-run variance: Beyond short memory 2011FIXED-B ASYMPTOTICS FOR THE STUDENTIZED MEAN FROM TIME SERIES WITH SHORT, LONG, OR NEGATIVE MEMORY

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