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Оценка Пространственных Моделей С Переменными Коэффициентами Пространственной Чувствительности Методом Максимального Правдоподобия И Обобщенным Методом Наименьших Квадратов
[Maximum likelihood and generalized least squares estimation of spatial lag models with endogenous spatial coefficients: a Monte Carlo simulation]


  • Myasnikov, Alexander


The traditional spatial lag model assumes that all regions in the sample exhibit the same degree of sensitivity to spatial external effects. This may not always be the case, however, especially with highly heterogeneous regions like those in Russia. Therefore, a model has been suggested that views spatial coefficients as being endogenously defined by regions’ intrinsic characteristics. We generalize this model, describe approaches to its estimation based on maximum likelihood and generalized least squares, and perform a Monte Carlo simulation of these two estimation methods in small samples. We find that the maximum likelihood estimator is preferable due to the lower biases and variances of the estimates it yields, although the generalized least squares estimator can also be useful in small samples for robustness checks and as a first approximation tool. In larger samples, results of the generalized least squares estimator are very close to those of the maximum likelihood estimator, so the former may be preferred because of its simplicity and less strict computational power requirements.

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References listed on IDEAS

  1. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
  2. Kelejian, Harry H & Prucha, Ingmar 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, Springer, vol. 17(1), pages 99-121, July.
  3. Kathleen P. Bell & Nancy E. Bockstael, 2000. "Applying the Generalized-Moments Estimation Approach to Spatial Problems Involving Microlevel Data," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 72-82, February.
  4. Luisa Corrado & Bernard Fingleton, 2012. "Where Is The Economics In Spatial Econometrics?," Journal of Regional Science, Wiley Blackwell, vol. 52(2), pages 210-239, May.
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spatial lag model; endogenous spatial coefficients; Monte Carlo simulation; small sample estimation;

JEL classification:

  • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
  • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods

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