Consistency of kernel variance estimators for sums of semiparametric linear processes

DOI10.1111/1368-423X.t01-1-00079zbMath0992.62079OpenAlexW3123921568MaRDI QIDQ4551778

No author found.

Publication date: 23 September 2002

Published in: The Econometrics Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/1368-423x.t01-1-00079

zbMATH Keywords

near epoch dependent

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Markov processes: estimation; hidden Markov models (62M05)

Related Items (3)

ASYMPTOTICS OF SPECTRAL DENSITY ESTIMATES ⋮ A new consistency proof for HAC variance estimators ⋮ A test for fractional cointegration using the sieve bootstrap

Cites Work

• Testing the null hypothesis of stationarity against the alternative of a unit root. How sure are we that economic time series have a unit root?
• Establishing conditions for the functional central limit theorem in nonlinear and semiparametric time series processes.
• THE FUNCTIONAL CENTRAL LIMIT THEOREM AND WEAK CONVERGENCE TO STOCHASTIC INTEGRALS I
• THE FUNCTIONAL CENTRAL LIMIT THEOREM AND WEAK CONVERGENCE TO STOCHASTIC INTEGRALS II
• Stochastic Limit Theory
• A STRONG CONSISTENCY PROOF FOR HETEROSKEDASTICITY AND AUTOCORRELATION CONSISTENT COVARIANCE MATRIX ESTIMATORS
• Consistency of Kernel Estimators of Heteroscedastic and Autocorrelated Covariance Matrices

This page was built for publication: Consistency of kernel variance estimators for sums of semiparametric linear processes