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Estimation in Dynamic Linear Regression Models with Infinite Variance Errors

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  • Knight, Keith

Abstract

This paper considers the asymptotic behavior of M-estimates in a dynamic linear regression model where the errors have infinite second moments but the exogenous regressors satisfy the standard assumptions. It is shown that under certain conditions, the estimates of the parameters corresponding to the exogenous regressors are asymptotically normal and converge to the true values at the standard n−½ rate.

Suggested Citation

  • Knight, Keith, 1993. "Estimation in Dynamic Linear Regression Models with Infinite Variance Errors," Econometric Theory, Cambridge University Press, vol. 9(4), pages 570-588, August.
    • Handle: RePEc:cup:etheor:v:9:y:1993:i:04:p:570-588_00
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    Cited by:

    1. Dong Wan Shin & Oesook Lee, 2004. "M‐Estimation for regressions with integrated regressors and arma errors," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(2), pages 283-299, March.
    2. Chen, Mei-Yuan & Kuan, Chung-Ming, 2001. "Testing parameter constancy in models with infinite variance errors," Economics Letters, Elsevier, vol. 72(1), pages 11-18, July.
    3. Koul, Hira L. & Surgailis, Donatas, 2001. "Asymptotics of empirical processes of long memory moving averages with infinite variance," Stochastic Processes and their Applications, Elsevier, vol. 91(2), pages 309-336, February.
    4. Ngai Chan & Rongmao Zhang, 2009. "M-estimation in nonparametric regression under strong dependence and infinite variance," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(2), pages 391-411, June.

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