Degrees of freedom adjustment for disturbance variance estimators in dynamic regression models
AbstractIn the classical regression model with fixed regressors the statistic S 2 , i.e. the sum of squared residuals (SSR) divided by the number of degrees of freedom, is an unbiased estimator of the variance of the disturbances. If the model is dynamic and contains lagged-dependent explanatory variables, then the least-squares coefficient estimators are biased in finite samples, and so is S 2 . By deriving the expectation of the initial terms in an expansion of the expression for SSR in the case of an autoregressive regression model, we prove that the bias in the degrees of freedom adjusted estimator is of smaller order in T , the sample size, than the bias of the unadjusted maximum-likelihood estimator. We also indicate how a further decrease in the bias can be achieved, and what the consequences are for estimating s. Insight is provided into the relative numerical magnitude of the bias for various estimators of s 2 in some relevant particular cases of this class of model by Monte Carlo simulation.
Download InfoTo our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
Bibliographic InfoArticle provided by Royal Economic Society in its journal The Econometrics Journal.
Volume (Year): 1 (1998)
Issue (Month): RegularPapers ()
Contact details of provider:
Postal: Office of the Secretary-General, School of Economics and Finance, University of St. Andrews, St. Andrews, Fife, KY16 9AL, UK
Phone: +44 1334 462479
Web page: http://www.res.org.uk/
More information through EDIRC
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Javier Alvarez & Martin Browning & Mette Ejrnæs, 2002.
"Modelling income processes with lots of heterogeneity,"
10th International Conference on Panel Data, Berlin, July 5-6, 2002
D2-3, International Conferences on Panel Data.
- Martin Browning & Mette Ejrn�s & Javier Alvarez, 2010. "Modelling Income Processes with Lots of Heterogeneity," Review of Economic Studies, Oxford University Press, vol. 77(4), pages 1353-1381.
- Martin Browning & Mette Ejrnaes, 2006. "Modelling income processes with lots of heterogeneity," Economics Series Working Papers 285, University of Oxford, Department of Economics.
- Javier Alvarez & Martin Browning & Mette Ejrnæs, 2001. "Modelling Income Processes with lots of heterogeneity," CAM Working Papers 2002-01, University of Copenhagen. Department of Economics. Centre for Applied Microeconometrics.
- Kiviet, Jan F. & Phillips, Garry D.A., 2014. "Improved variance estimation of maximum likelihood estimators in stable first-order dynamic regression models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 424-448.
- Jan F. KIVIET & Garry D.A. PHILLIPS, 2012. "Improved Variance Estimation of Maximum Likelihood Estimators in Stable First-Order Dynamic Regression Models," Economic Growth centre Working Paper Series 1206, Nanyang Technolgical University, School of Humanities and Social Sciences, Economic Growth centre.
- Kiviet, Jan F. & Phillips, Garry D.A., 2012.
"Higher-order asymptotic expansions of the least-squares estimation bias in first-order dynamic regression models,"
Computational Statistics & Data Analysis,
Elsevier, vol. 56(11), pages 3705-3729.
- Kiviet, J.F. & Phillips, G.D.A., 1999. "Higher-Order Asymptotic Expansions of the Least-Squares Estimation Bias in First-Order Dynamic Regression Models," Discussion Papers 9903, Exeter University, Department of Economics.
- Jan F. Kiviet & Garry D. A. Phillips, 2000. "Improved Coefficient and Variance Estimation in Stable First-Order Dynamic Regression Models," Econometric Society World Congress 2000 Contributed Papers 0631, Econometric Society.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley-Blackwell Digital Licensing) or (Christopher F. Baum).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.