Higher-order asymptotic expansions of the least-squares estimation bias in first-order dynamic regression models

DOI10.1016/j.csda.2010.07.013zbMath1255.62258OpenAlexW2103773647MaRDI QIDQ1927149

Jan F. Kiviet, Garry D. A. Phillips

Publication date: 30 December 2012

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://pure.uva.nl/ws/files/4230959/1751_OecologiaLipsDuivenvoorden.pdf

zbMATH Keywords

Monte Carlo simulationbias approximationlagged dependent variablesARX-model

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Monte Carlo methods (65C05)

Related Items (6)

Improved variance estimation of maximum likelihood estimators in stable first-order dynamic regression models ⋮ The ability to correct the bias in the stable AD(1,1) model with a feedback effect ⋮ Improving the estimation and predictions of small time series models ⋮ Bias and covariance of the least squares estimate in a structured errors-in-variables problem ⋮ Higher order mean squared error of generalized method of moments estimators for nonlinear models ⋮ The accuracy of the higher order bias approximation for the 2SLS estimator

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