Improved Variance Estimation of Maximum Likelihood Estimators in Stable First-Order Dynamic Regression Models

Jan Kiviet and Garry Phillips

No 1206, Economic Growth Centre Working Paper Series from Nanyang Technological University, School of Social Sciences, Economic Growth Centre

Abstract: In dynamic regression models conditional maximum likelihood (least-squares) coefficient and variance estimators are biased. From expansions of the coefficient variance and its estimator we obtain an approximation to the bias in variance es- timation and a bias corrected variance estimator, for both the standard and a bias corrected coefficient estimator. These enable a comparison of their mean squared errors to second order. We formally derive sufficient conditions for admissibility of these approximations. Illustrative numerical and simulation results are presented on bias reduction of coefficient and variance estimation for three relevant classes of ?rst-order autoregressive models, supplemented by e¤ects on mean squared er- rors, test size and size corrected power. These indicate that substantial biases do occur in moderately large samples, but these can be mitigated substantially and may also yield mean squared error reduction. Crude asymptotic tests are cursed by huge size distortions. However, operational bias corrections of both the esti- mates of coefficients and their estimated variance are shown to curb type I errors reasonably well.

Keywords: higher-order asymptotic expansions; bias correction; efficiency gains; lagged dependent variables; finite sample moments; size improvement (search for similar items in EconPapers)
JEL-codes: C13 C22 (search for similar items in EconPapers)
Pages: 47 pages
Date: 2012-06
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (1)

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Journal Article: Improved variance estimation of maximum likelihood estimators in stable first-order dynamic regression models (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:nan:wpaper:1206

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