Efficiency of least-squares-estimation of polynomial trend when residuals are autocorrelated

DOI10.1016/0165-1765(94)90022-1zbMath0797.62103OpenAlexW2088643674MaRDI QIDQ1331869

Roland Jeske, Ralf Busse, Walter Kramer

Publication date: 30 October 1994

Published in: Economics Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0165-1765(94)90022-1

zbMATH Keywords

lower boundsgeneralized least squaresordinary least squarespolynomial regressionspolynomial trend modelstationary AR(1) disturbances

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) General nonlinear regression (62J02)

Related Items (4)

Relative efficiency of OLSE and COTE for seasonal autoregressive disturbances ⋮ Autocorrelation- and heteroskedasticity-consistent \(t\)-values with trending data ⋮ A general condition for an optimal limiting efficiency of OLS in the general linear regression model ⋮ The efficiency of the sample mean in a linear regression model when errors follow a first-order moving average process

Cites Work

• Unnamed Item
• Finite Sample Efficiency of Ordinary Least Squares in the Linear Regression Model with Autocorrelated Errors
• Efficiency of Least-Squares Estimation of Linear Trend when Residuals Are Autocorrelated
• Note on Estimating Linear Trend when Residuals are Autocorrelated
• On the Estimation of Regression Coefficients in the Case of an Autocorrelated Disturbance

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