scholarly journals GMM Estimation of a Partially Linear Additive Spatial Error Model

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 622
Author(s):  
Jianbao Chen ◽  
Suli Cheng
Keyword(s):  
Monte Carlo ◽  
Method Of Moments ◽  
Arbitrary Number ◽  
Generalized Method Of Moments ◽  
Error Model ◽  
Spatial Error Model ◽  
Finite Sample ◽  
Spatial Error ◽  
Partially Linear ◽  
Consistency And Asymptotic Normality

This article presents a partially linear additive spatial error model (PLASEM) specification and its corresponding generalized method of moments (GMM). It also derives consistency and asymptotic normality of estimators for the case with a single nonparametric term and an arbitrary number of nonparametric additive terms under some regular conditions. In addition, the finite sample performance for our estimates is assessed by Monte Carlo simulations. Lastly, the proposed method is illustrated by analyzing Boston housing data.

Recursive Estimation of the Spatial Error Model

2022 ◽  
Author(s):  
Chiara Ghiringhelli ◽  
Gianfranco Piras ◽  
Giuseppe Arbia ◽  
Antonietta Mira
Keyword(s):  
Error Model ◽  
Recursive Estimation ◽  
Spatial Error Model ◽  
Spatial Error

Transportation and Urban Development

2018 ◽  
pp. 283-305
Author(s):  
Mehmet Akif Kara
Keyword(s):  
Spillover Effect ◽  
Error Model ◽  
Transportation Infrastructure ◽  
Spatial Error Model ◽  
Test Statistic ◽  
Spatial Econometric ◽  
Spatial Error ◽  
Infrastructure Investments ◽  
Regional Economic Performance ◽  
The Impact

It is noteworthy that there is a substantial literature review that examines the impact of transportation infrastructure on urban and regional economic performance. It is observed that such infrastructure investments are focused on the economic growth as well as the spillover effect in applied studies carried out in this respect. In this study, in which the effects of highway transportation infrastructure on urban output and the spillover effect of these investments are determined using the spatial econometric method, 81 cities in Turkey have been taken into consideration, and according to the results of the study, transportation infrastructure investments in Turkey have been found to contribute positively to urban output. Also, while the Moran's I test statistic reveals the spatial dependence of such investments, the Lagrange multiplier test results also determine the need to use the spatial error model. The spatial error model results reveal the existence of the positive spillover effect of transportation infrastructure investments.

Spatial Error Model

2008 ◽  
pp. 1101-1101
Author(s):  
Shashi Shekhar ◽  
Hui Xiong
Keyword(s):  
Error Model ◽  
Spatial Error Model ◽  
Spatial Error

scholarly journals Model Regresi Spasial dan Aplikasinya pada Kasus Tingkat Kemiskinan Kabupaten Soppeng

2018 ◽  
Vol 4 (2) ◽  
pp. 102
Author(s):  
Anggi Ananda Putri ◽  
Wahidah Sanusi ◽  
Sukarna Sukarna
Keyword(s):  
Social Issues ◽  
Well Being ◽  
Error Model ◽  
Poor Household ◽  
Spatial Dependency ◽  
Spatial Error Model ◽  
Spatial Error ◽  
Sar Model ◽  
Spatial Autoregressive ◽  
Independent Variable

Poverty is one of the major problem that frequently faced by human. Begin from poverty, consequently emerged several social issues, such as homeless, beggar, defendant, and prostitution. On this research were conducted modeling poverty degree in Soppeng with using number of poor household as the dependent variable. Modeling were done by using area approach which is a Spatial Autoregressive (SAR) model and Spatial Error Model (SEM). As for the independent variable used on this research is the number of health services, school facility, population density, social well being disable, and the distance on village and centre of Soppeng.  Regarding to the analysis of Spatial Autoregressive (SAR) and Spatial Error Model (SEM) shows that there is a spatial dependency lag and error on number of poor household variable. As for the independent variable which have the significancy account for 5% on Spatial Autoregressive (SAR) and Spatial Error Model (SEM) are every variables with a number R2= 90,9% on SAR and R2= 90,1% on SEM.

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