Communications for Statistical Applications and Methods

  • 제17권3호
  • /
  • Pages.349-356
  • /
  • 2010
  • /
  • 2287-7843(pISSN)
  • /
  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
권호 표지
DOI QR코드

DOI QR Code

Asymptotic Properties of the Disturbance Variance Estimator in a Spatial Panel Data Regression Model with a Measurement Error Component

  • Lee, Jae-Jun (Department of Statistics, The University of Georgia)
  • 투고 : 20100100
  • 심사 : 20100500
  • 발행 : 2010.05.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

The ordinary least squares based estimator of the disturbance variance in a regression model for spatial panel data is shown to be asymptotically unbiased and weakly consistent in the context of SAR(1), SMA(1) and SARMA(1,1)-disturbances when there is measurement error in the regressor matrix.

키워드

참고문헌

  1. Anselin, L. (1988). Spatial Econometrics: Methods and Models, Kluwer Academic Publishers, Dordrecht.
  2. Anselin, L. (2003). Spatial externalities, spatial multipliers and spatial econometrics, International Regional Science Review, 26, 153-166. https://doi.org/10.1177/0160017602250972
  3. Anselin, L. and Bera, A. (1998). Spatial dependence in linear regression models with an introduction to spatial econometrics. In Ullah, Amman and Giles, David E.A., editors, Handbook of Applied Economic Statistics, 237-289.
  4. Baltagi, B. H. (2001). Econometric Analysis of Panel Data, John Wiley, New York.
  5. Baltagi, B. H. and Kramer, W. (1994). Consistency, asymptotic unbiasedness and bounds on the bias of $S^2$ in the linear regression model with error component disturbances, Statistical Papers, 35, 323-328. https://doi.org/10.1007/BF02926424
  6. Case, A. (1992). Neighborhood influence and technological change, Regional Science and Urban Economics, 22, 491-508. https://doi.org/10.1016/0166-0462(92)90041-X
  7. Cliff, A. and Ord, J. K. (1981). Spatial Processes: Models and Applications, Pion, London.
  8. Dufour, J. M. (1986). Bias of $S^2$ in linear regression with dependent errors, The American Statistician, 40, 284-285. https://doi.org/10.2307/2684604
  9. Dufour, J. M. (1988). Estimator of the disturbance variance in econometric models, Journal of Econometrics, 37, 277-292. https://doi.org/10.1016/0304-4076(88)90007-3
  10. Griliches, Z. and Hausman, J. A. (1986). Errors in variables in panel data, Journal of Econometrics, 31, 93-118. https://doi.org/10.1016/0304-4076(86)90058-8
  11. Horn, R. A. and Johnson, C. R. (1985). Matrix Analysis, Cambridge University Press, Cambridge.
  12. Hsiao, C. (1986). Analysis of Panel Data, Cambridge University Press, Cambridge.
  13. Kapoor, M., Kelejian, H. H. and Prucha, I. R. (2007). Panel data models with spatially correlated error components, Journal of Econometrics, 140, 97-130. https://doi.org/10.1016/j.jeconom.2006.09.004
  14. Kelejian, H. H. and Prucha, I. R. (1999). A generalized moments estimator for the autoregressive parameter in a spatial model, International Economic Review, 40, 509-533. https://doi.org/10.1111/1468-2354.00027
  15. Kelejian, H. H. and Prucha, I. R. (2002). 2SLS and OLS in a spatial autoregressive model with equal spatial weights, Regional Science and Urban Economics, 32, 691-707. https://doi.org/10.1016/S0166-0462(02)00003-0
  16. Kelejian, H. H., Prucha, I. R. and Yuzefovich, Y. (2006). Estimation problems in models with spatial weighting matrices which have blocks of equal elements, Journal of Regional Science, 46, 507-515. https://doi.org/10.1111/j.1467-9787.2006.00449.x
  17. Kiviet, J. F. and Kramer, W. (1992). Bias of $S^2$ in the linear regression model with correlated errors, The Review of Economics and Statistics, 74, 362-365. https://doi.org/10.2307/2109673
  18. Kramer, W. (1991). The asymptotic unbiasedness of $S^2$ in the linear regression model with AR(1)-disturbances, Statistical Papers, 32, 71–72.
  19. Kr¨amer, W. and Berghoff, S. (1991). Consistency of S^2$ in the linear regression model with correlated errors, Empirical Economics, 16, 375–377.
  20. Kramer,W. and Donninger, C. (1987). Spatial autocorrelation among errors and the relative efficiency of OLS in the linear regression model, Journal of the American Statistical Association, 82, 577-579. https://doi.org/10.2307/2289467
  21. Lee, L. (2004). Asymptotic distributions of quasi-maximum likelihood estimators for spatial autoregressive models, Econometrica, 72, 1899-1925. https://doi.org/10.1111/j.1468-0262.2004.00558.x
  22. Neudecker, M. (1977). Bounds for the bias of the least squares estimator of ${\sigma}^2$ in case of a first-order autoregressive process (positive autocorrelation). Econometrica, 45, 1258-1262.
  23. Neudecker, M. (1978). Bounds for the bias of the LS estimator of ${\sigma}^2$ in case of a first-order (positive) autoregressive process where the regression contains a constant term, Econometrica, 46, 1223-1226. https://doi.org/10.2307/1911447
  24. Sathe, S. T. and Vinod, H. D. (1974). Bounds on the variance of regression coefficients due to heteroscedastic or autoregressive errors, Econometrica, 42, 333-340. https://doi.org/10.2307/1911982
  25. Song, S. H. (1996). Consistency and asymptotic unbiasedness of $S^2$ in the serially correlated error components regression model for panel data, Statistical Papers, 37, 267-275. https://doi.org/10.1007/BF02926588
  26. Song, S. H. and Kim, M. (2006). Consistency and asymptotic unbiasedness of $S^2$ in a panel data regression model with measurement error, Journal of the Korean Data Analysis Society, 8, 597-605.
  27. Song, S. H. and Lee, J. (2008). A note on $S^2$ in a spatially correlated error components regression model for panel data, Economics Letters, 101, 41-43. https://doi.org/10.1016/j.econlet.2008.04.003
  28. Theil, H. (1971). Principles of Econometrics, Wiley, New York.
  29. Watson, G. S. (1955). Serial correlation in regression analysis I, Biometrika, 42, 333-340.