Generalized Maximum Entropy Estimator for the Linear Regression Model with a Spatial Autoregressive Disturbance

오차항이 SAR(1)을 따르는 공간선형회귀모형에서 일반화 최대엔트로피 추정량에 관한 연구

  • Cheon, Soo-Young (KU Industry-Academy Cooperation Group Team of Economics and Statistics, Korea Univ.) ;
  • Lim, Seong-Seop (Personal Risk Management Team, Hana Bank)
  • 전수영 (고려대학교 세종캠퍼스 경제통계 산학협력단) ;
  • 임성섭 (하나은행)
  • Published : 2009.03.30


This paper considers a linear regression model with a spatial autoregressive disturbance with ill-posed data and proposes the generalized maximum entropy(GME) estimator of regression coefficients. The performance of this estimator is investigated via Monte Carlo experiments. The results show that the GME estimator provides efficient and robust estimate for the unknown parameter.


  1. 송석헌, 전수영 (2006). 패널회귀모형에서 최대엔트로피 추정량에 관한 연구, <응용통계연구>, 19, 521-534
  2. 이재준 (2002). 오차항이 공간자기상관을 갖는 선형회귀모형에서 회귀계수 검정에 관한 연구, <고려대학교 석사학위 논문>
  3. Anselin, L. and Bera, A. (1998). Spatial Dependence in Linear Regression Model with an Introduction to Spatial Econometrics, Handbook of Applied Economic Statistics, New York
  4. Anselin, L. (2002). Under the hood: Issues in the specification and interpretation of spatial regression models, Agricultural Economics, 27, 247-267
  5. Belsley. D. (1991). Conditioning Diagnostics: Collinearity and Weak Data in Regression, John Wiley & Sons, New York
  6. Cliff, A. D. and Ord, J. K. (1973). Spatial Autocorrelation, Pion, London
  7. Dubin, R. A. (1998). Spatial autocorrelation: A primer, Journal of Housing Economics, 7, 304-327
  8. Golan, A. (1994). A multi-variable stochastic theory of size distribution of firms with empirical evidence, Advances in Econometrics, 10, 1-46
  9. Griffith, D. A. (1988). Advanced Spatial Statistics: Special Topics in the Exploration of Quantitative Spatial Data Series, Kluwer Academic Publishers
  10. Judge, G. G. and Golan, A. (1992). Recovering information in the case of ill-posed inverse problems with noise, Unpublished paper, University of California at Berkeley
  11. Kelejian, H. H. and Prucha, I. R. (1998). A generalized spatial two-stage least squares procedure for estimating a spatial autoregressive model with autoregressive disturbances, The Journal of Real Estate Finance and Economics, 17, 99-121
  12. 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
  13. Moulton, B. R. (1986). Random group effects and the precision of regression estimates, Journal of Econo-metrics, 32, 385-397
  14. Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit region, Economic Geography, 46, 230-240

Cited by

  1. A Comparative Study on Spatial Lattice Data Analysis - A Case Where Outlier Exists - vol.17, pp.2, 2010,