Generalized Bayes Estimation of Spatial Autoregressive Models

• Autorzy: Anoop Chaturvedi , Sandeep Mishra
• Języki publikacji: EN

Abstrakty

EN

The spatial autoregressive (SAR) models are widely used in spatial econometrics for analyzing spatial data involving spatial autocorrelation structure. The present paper derives a Generalized Bayes estimator for estimating the parameters of a SAR model. The admissibility and minimaxity properties of the estimator have been discussed. For investigating the finite sample behaviour of the estimator, the results of a simulation study have been presented. The results of the paper are applied to demographic data on total fertility rate for selected Indian states. (original abstract)

Słowa kluczowe

EN

Bayesian estimation; Bayesian inference; Autoregression models; Fertility model; Spatial econometrics

PL

Estymacja bayesowska; Wnioskowanie bayesowskie; Modele autoregresji; Model płodności; Ekonometria przestrzenna

• Czasopismo: Statistics in Transition
• Rocznik: 2019
• Tom: 20
• Numer: nr 2
• Strony: 15--32

Twórcy

autor

Anoop Chaturvedi

• University of Allahabad, India

autor

Sandeep Mishra

• University of Allahabad, India

Bibliografia

• ANSELIN, L., (1988). Spatial econometrics, methods, and models, Dordrecht: Kluwer Academic.
• ANSELIN, L., REY, S., (2010). Perspectives on spatial data analysis, Berlin: Springer, DOI:10.1007/978-3-642-01976-0.
• BERGER, J., (1976). Admissible minimax estimation of a multivariate normal mean with arbitrary quadratic loss. Ann. Statist., 4, pp. 223-226.
• BERGER, J. O., (1980): Statistical Decision Theory: Foundations, Concepts and Methods, Springer, N.Y.
• BROWN, L. D., (1971). Admissible estimators, recurrent diffusions, and insoluble boundary value problems, Ann. Math. Statist., 42, pp. 855-903.
• EFRON, B., MORRIS, C., (1976). Families of minimax estimators of the mean of a multivariate normal distribution. Ann. Statist., 4, pp. 11-21.
• FOURDRINIER, D., STRAWDERMAN. W. E., WELLS, T., (1998). On the construction of Bayes minimax estimators, Ann. Statist., 26, pp. 660-671.
• KUBOKAWA, T., (1991). An approach to improving the James-Stein estimator, J. Multivariate Anal., 36, pp. 121-126.
• KUBOKAWA, T., (1994). An unified approach to improving equivariant estimators, Ann. Statist., 22, pp. 290-299.
• LESAGE, J. P., (1998): Spatial Econometrics, www.spatial-econometrics.com.
• LESAGE, J. P, PACE, R. K., (2009). Introduction to spatial econometrics, Boca Raton, FL: CRC, DOI:10.1201/9781420064254.
• MARUYAMA, Y., (1998). A unified and broadened class of admissible minimax estimators of a multivariate normal mean, J. Multivariate Anal., 64, 196-205.
• MARUYAMA, Y., (1999). Improving on the James-Stein Estimator, Statistics & Decisions, 17, pp. 137-140.
• MARUYAMA, Y., (2000). Minimax admissible estimation of a multivariate normal mean and improvement upon the James-Stein estimator, Ph.D. dissertation, Graduate School of Economics, University of Tokyo.
• PAL, A., CHATURVEDI, A., DUBEY, A., (2016). Shrinkage estimation in spatial autoregressive model, Journal of Multivariate Analysis, 143, pp. 362-373.
• RUBIN, H. (1977). Robust Bayesian estimation, In Statistical Decision Theov and Related Topics II. (S. S. Gupta and D. S. Moore, eds.), Academic Press.
• SCHABENBERGER, O., GOTWAY, C. A., (2005): Statistical Methods for Spatial Data Analysis, Chapman and Hall/CRC: Boca Raton, FL.
• STEIN, C., (1973). Estimation of the mean of a multivariate normal distribution, In Proc. Prague Symp. Asymptotic Statist., pp. 345-381.
• ZELLNER, A., (1986). On Assessing Prior Distributions and Bayesian Regression Analysis with g-Prior Distributions, In: Goel, P. and Zellner, A., Eds., Bayesian Inference and Decision Techniques: Essays in Honor of Bruno de Finetti, Elsevier Science Publishers, Inc., New York, pp. 233-243.

Identyfikatory

DOI

10.21307/stattrans-2019-012