Estimator’s Property of Spatially Corrected Blundell-Bond on Dynamic of Spatial Durbin Panel Model Using Monte Carlo Simulation

Authors

  • widya reza Brawijaya
  • Henny Pramoedyo Brawijaya
  • Rahma Fitriani Brawijaya

DOI:

https://doi.org/10.21512/comtech.v10i2.5665

Keywords:

estimator’s property, Spatially Corrected Blundell-Bond, panel dynamics, Monte Carlo simulation

Abstract

This research discussed the properties of Spatially Corrected Blundell-Bond (SCBB) in overcoming the problem of endogeneity and spatial dependence that occur in dynamic Spatial Durbin Model (SDM) panels. The properties of the estimator tested were unbiased and normality. The properties test of the estimator was carried out using the Monte Carlo simulation approach. From the results of this research, it finds that the SCBB estimator has unbiased properties and follows a normal distribution. Based on the property of the estimator obtained, the SCBB parameter estimation method in the dynamic SDM panel model works well in overcoming endogeneity and spatial dependence problems.

Dimensions

Plum Analytics

Author Biographies

widya reza, Brawijaya

Department of Statistic , Mathematic and Natural Science Faculty, Brawijaya University, Malang

Henny Pramoedyo, Brawijaya

Department of Statistic , Mathematic and Natural Science Faculty, Brawijaya University, Malang

Rahma Fitriani, Brawijaya

Department of Statistic , Mathematic and Natural Science Faculty, Brawijaya University, Malang

References

Bai, Y., Zhou, S., & Fan, Z. (2018). A Monte Carlo comparison of GMM and QMLE estimators for short dynamic panel data models with spatial errors. Journal of Statistical Computation and Simulation, 88(2), 376-409. https://doi.org/10.1080/00949655.2017.1392522

Baltagi, B. H., Fingleton, B., & Pirotte, A. (2014). Estimating and forecasting with a dynamic spatial panel data model. Oxford Bulletin of Economics and Statistics, 76(1), 112-138. https://doi.org/10.1111/obes.12011

Baltagi, B. H., & Li, D. (2006). Prediction in the panel data model with spatial correlation: The case of liquor. Spatial Economic Analysis, 1(2), 175-185.

Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of econometrics, 87(1), 115-143. https://doi.org/10.1016/S0304-4076(98)00009-8

Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical analysis, 28(4), 281-298. https://doi.org/10.1111/j.1538-4632.1996.tb00936.x

Cizek, P., Jacobs, J. P., Ligthart, J. E., & Vrijburg, H. (2015). GMM estimation of fixed effects dynamic panel data models with spatial lag and spatial errors. In CentER Discussion Paper (No. 2015-003).

De Pedro, C. (2014). Macroeconomics. Rudiger Dornbusch, Stanley Fischer y Richard Startz McGraw-Hill Higher Education, 2011. Papeles De Europa, 27(1), 165-168. https://doi.org/10.5209/rev_PADE.2014.v27.n1.47067

Elhorst, J. P. (2014). Spatial econometrics: from crosssectional data to spatial panels. In Springerbriefs in regional science (Vol. 479, pp. 480). Heidelberg: Springer.

Greene, W. H. (2003). Econometric analysis. Upper Saddle River: Prentice Hall.

Haining, R. (1990). Book reviews: Anselin, L. 1988: Spatial econometrics: methods and models. London: Kluwer. xvi + 284 pp. £39.00 cloth. Progress in Human Geography, 14(3), 448-449. https://doi.org/10.1177/030913259001400309

Jacobs, J. P., Ligthart, J. E., & Vrijburg, H. (2009). Dynamic panel data models featuring endogenous interaction and spatially correlated errors. In CentER Discussion Paper Series (No. 2009-92).

Kelejian, H. H., & Prucha, I. R. (2010). Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances. Journal of econometrics, 157(1), 53-67. https://doi.org/10.1016/j.jeconom.2009.10.025

Kukenova, M., & Monteiro, J. A. (2009). Spatial dynamic panel model and system GMM: a Monte Carlo investigation. In IRENE Working Papers 09-01.

Lee, L. F., & Yu, J. (2010). Estimation of spatial autoregressive panel data models with fixed effects. Journal of Econometrics, 154(2), 165-185. https://doi.org/10.1016/j.jeconom.2009.08.001

Lee, L. F., & Yu, J. (2014). Efficient GMM estimation of spatial dynamic panel data models with fixed effects. Journal of Econometrics, 180(2), 174-197. https://doi.org/10.1016/j.jeconom.2014.03.003

Rubinstein, R. Y., & Kroese, D. P. (2016). Simulation and the Monte Carlo method. Wiley.

Shi, W., & Lee, L. F. (2017). Spatial dynamic panel data models with interactive fixed effects. Journal of Econometrics, 197(2), 323-347. https://doi.org/10.1016/j.jeconom.2016.12.001

Su, L., & Yang, Z. (2015). QML estimation of dynamic panel data models with spatial errors. Journal of Econometrics, 185(1), 230-258. https://doi.org/10.1016/j.jeconom.2014.11.002

Yu, J., De Jong, R., & Lee, L. F. (2012). Estimation for spatial dynamic panel data with fixed effects: The case of spatial cointegration. Journal of Econometrics, 167(1), 16-37. https://doi.org/10.1016/j.jeconom.2011.05.014

Downloads

Published

2019-12-31

Issue

Section

Articles
Abstract 797  .
PDF downloaded 280  .