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A note on S2 in a linear regression model based on two-stage sampling data

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  • Song, Seuck Heun

Abstract

The ordinary least-squares-based estimator of the disturbance variance is shown to be asymptotically unbiased and weakly consistent irrespective of restrictions on the nonstochastic regressor matrix, when a regression model uses the data collected by a two-stage sampling.

Suggested Citation

  • Song, Seuck Heun, 1999. "A note on S2 in a linear regression model based on two-stage sampling data," Statistics & Probability Letters, Elsevier, vol. 43(2), pages 131-135, June.
    • Handle: RePEc:eee:stapro:v:43:y:1999:i:2:p:131-135
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    References listed on IDEAS

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    1. Kloek, T, 1972. "Note on Consistent Estimation of the Variance of the Disturbances in the Linear Model," Econometrica, Econometric Society, vol. 40(5), pages 911-912, September.
    2. Neudecker, Heinz, 1977. "Bounds for the Bias of the Least Squares Estimator of s2 in the Case of a First-order Autoregressive Process (Positive Autocorrelation)," Econometrica, Econometric Society, vol. 45(5), pages 1257-1262, July.
    3. Riddell, William Craig, 1979. "The Empirical Foundations of the Phillips Curve: Evidence from Canadian Wage Contract Data," Econometrica, Econometric Society, vol. 47(1), pages 1-24, January.
    4. Kloek, T, 1981. "OLS Estimation in a Model Where a Microvariable Is Explained by Aggregates and Contemporaneous Disturbances Are Equicorrelated," Econometrica, Econometric Society, vol. 49(1), pages 205-207, January.
    5. Drygas, Hilmar, 1975. "A Note on a Paper by T. Kloek Concerning the Consistency of Variance Estimation in the Linear Model," Econometrica, Econometric Society, vol. 43(1), pages 175-175, January.
    6. Sathe, S T & Vinod, H D, 1974. "Bounds on the Variance of Regression Coefficients Due to Heteroscedastic or Autoregressive Errors," Econometrica, Econometric Society, vol. 42(2), pages 333-340, March.
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    Cited by:

    1. Song, Seuck Heun & Lee, Jaejun, 2008. "A note on S2 in a spatially correlated error components regression model for panel data," Economics Letters, Elsevier, vol. 101(1), pages 41-43, October.

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