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Some strong consistency results in stochastic regression

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  • Lita da Silva, João

Abstract

Strong consistency of the least-squares estimates in stochastic regression models is established assuming errors with variance not necessarily defined. The errors will be considered identically distributed having absolute moment of order r, 0

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  • Lita da Silva, João, 2014. "Some strong consistency results in stochastic regression," Journal of Multivariate Analysis, Elsevier, vol. 129(C), pages 220-226.
    • Handle: RePEc:eee:jmvana:v:129:y:2014:i:c:p:220-226
    DOI: 10.1016/j.jmva.2014.04.022
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    References listed on IDEAS

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    1. Lai, T. L. & Robbins, Herbert & Wei, C. Z., 1979. "Strong consistency of least squares estimates in multiple regression II," Journal of Multivariate Analysis, Elsevier, vol. 9(3), pages 343-361, September.
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    3. Pliskin, Jeffrey, 1988. "A further theoretical result for generalized ridge regression estimators," Economics Letters, Elsevier, vol. 26(2), pages 133-135.
    4. DRYGAS, Hilmar, 1976. "Weak and strong consistency of the least squares estimators in regression models," LIDAM Reprints CORE 236, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. Gui-Jing, Chen & Lai, T. L. & Wei, C. Z., 1981. "Convergence systems and strong consistency of least squares estimates in regression models," Journal of Multivariate Analysis, Elsevier, vol. 11(3), pages 319-333, September.
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    Cited by:

    1. Cubadda, Gianluca & Guardabascio, Barbara, 2019. "Representation, estimation and forecasting of the multivariate index-augmented autoregressive model," International Journal of Forecasting, Elsevier, vol. 35(1), pages 67-79.

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