Thesis (M.Sc.) - Cairo University - Faculty of Graduate Studies for Statistical Research - Department Statistics and Econometrics

The spatial analysis aims to understand and explore the nature of entanglements and interactions between spatial units{u2019} locations. The analysis of models involving spatial dependence has received great attention in recent decades. Because ignoring the presence of spatial dependence in the data is very likely to lead to biased or inefficient estimates if we use traditional estimation methods. When spatial dependence exists in the data, then this may be an additional source of variation. As we know, ignoring the source of variation can lead to biased estimates, and the traditional estimators are no longer efficient due to changes in asymptotic variance-covariance matrices (VCMs).Therefore, alternative estimation methods had to be developed to take into account spatial dependence to obtain more accurate results. Recently, researchers go to introducing this approach in panel data models to take the advantages provided by these models.Therefore, this thesis is an attempt to assess the risks involved in ignoring the spatial dependence in panel data modeling by using a Monte Carlo simulation (MCS) study to compare the performance of two estimators; i.e., spatial maximum likelihood estimator (MLE) and non-spatial ordinary least squares (OLS) within-group estimator for two spatial panel data (SPD) models; Spatial lag model (SLM) and spatial error model (SEM), by using three spatial weights matrices; inverse distance, Gaussian transformation, and inverse exponential distance matrices. Then, we provide a general framework that shows how to define the appropriate model from among several candidate models through application to data of per capita personal income (PCPI) in U.S. states from 2009 to 2019, concerning three main aspects; educational attainment, economy size, and labor force type.The results of our simulation study show that the non-spatial estimator gives us biased and inefficient estimates for the parameters of covariates, and has a negative effect on the goodness of fit criteria, for both models, especially if the spatial dependence degree is large. In addition, our empirical study confirms that PCPI is spatially dependent lagged correlated

Issued also as CD