Semiparametric Estimation in Time‐Series Regression with Long‐Range Dependence

DOI10.1111/j.1467-9892.2005.00401.xzbMath1097.62085OpenAlexW3123858928MaRDI QIDQ5467604

Morten Ørregaard Nielsen

Publication date: 24 May 2006

Published in: Journal of Time Series Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1467-9892.2005.00401.x

zbMATH Keywords

asymptotic normalitygeneralized least squaresWhittle likelihoodcross-periodogram

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20)

Related Items (8)

Finite Sample Performance in Cointegration Analysis of Nonlinear Time Series with Long Memory ⋮ Multiple local Whittle estimation in stationary systems ⋮ Fully modified narrow‐band least squares estimation of weak fractional cointegration ⋮ Consistent inference for predictive regressions in persistent economic systems ⋮ A Generalised Fractional Differencing Bootstrap for Long Memory Processes ⋮ Fixed Bandwidth Inference for Fractional Cointegration ⋮ A comparison of semiparametric tests for fractional cointegration ⋮ Semiparametric Sieve-Type Generalized Least Squares Inference

Uses Software

• Ox
• ARFIMA
• longmemo

Cites Work

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• A semiparametric two-step estimator in a multivariate long memory model
• Understanding spurious regressions in econometrics
• The central limit theorem for time series regression
• On large-sample estimation for the mean of a stationary random sequence
• Time series regression with long-range dependence
• Large-sample inference for nonparametric regression with dependent errors
• Averaged periodogram estimation of long memory
• The spurious regression of fractionally integrated processes
• CONSISTENCY OF THE AVERAGED CROSS‐PERIODOGRAM IN LONG MEMORY SERIES
• Gaussian Semi‐parametric Estimation of Fractional Cointegration
• Adapting to Unknown Disturbance Autocorrelation in Regression with Long Memory

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