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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

Suggested Citation

  • 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.
    2. Pliskin, Jeffrey, 1988. "A further theoretical result for generalized ridge regression estimators," Economics Letters, Elsevier, vol. 26(2), pages 133-135.
    3. 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.
    4. William G. Brown & Bruce R. Beattie, 1975. "Improving Estimates of Economic Parameters by Use of Ridge Regression with Production Function Applications," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 57(1), pages 21-32.
    5. 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).
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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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