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Bias of SDE 2 in the Linear Regression Model with Correlated Errors

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  • Kiviet, Jan F
  • Kramer, Walter

Abstract

The authors consider the relative bias of the OLS-based estimate s(squared) of the disturbance variance in the linear regression model when disturbances are stationary AR(1). They improve upon previous bounds for the bias and show that E(s[squared]/["sigma" squared]) tends to zero as autocorrelation increases whenever there is an intercept in the regression. Copyright 1992 by MIT Press.

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Bibliographic Info

Article provided by MIT Press in its journal Review of Economics & Statistics.

Volume (Year): 74 (1992)
Issue (Month): 2 (May)
Pages: 362-65

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Handle: RePEc:tpr:restat:v:74:y:1992:i:2:p:362-65

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Web page: http://mitpress.mit.edu/journals/

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Cited by:
  1. Gotu, Butte, 1999. "The consistency of s2 in the linear regression model when the disturbances are spatially correlated," Technical Reports 1999,07, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
  2. Richard W. Kopcke, 1993. "The determinants of business investment: has capital spending been surprisingly low?," New England Economic Review, Federal Reserve Bank of Boston, issue Jan, pages 3-31.
  3. Krämer, Walter & Schich, Sebastian T., 2005. "Large-scale disasters and the insurance industry," Technical Reports 2005,11, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
  4. 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.
  5. Krämer, Walter & Hanck, Christoph, 2006. "OLS-based estimation of the disturbance variance under spatial autocorrelation," Technical Reports 2006,42, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

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