scholarly journals Spatial Regression Models: A Systematic Comparison of Different Model Specifications Using Monte Carlo Experiments

2019 ◽  
pp. 004912411988246 ◽  
Author(s):  
Tobias Rüttenauer
Keyword(s):  
Monte Carlo ◽  
Spatial Data ◽  
Regression Models ◽  
Spatial Regression ◽  
Spillover Effects ◽  
Spatial Error ◽  
Monte Carlo Experiments ◽  
Spatial Autoregressive ◽  
Spatial Regression Models ◽  
Variable Bias

Spatial regression models provide the opportunity to analyze spatial data and spatial processes. Yet, several model specifications can be used, all assuming different types of spatial dependence. This study summarizes the most commonly used spatial regression models and offers a comparison of their performance by using Monte Carlo experiments. In contrast to previous simulations, this study evaluates the bias of the impacts rather than the regression coefficients and additionally provides results for situations with a nonspatial omitted variable bias. Results reveal that the most commonly used spatial autoregressive and spatial error specifications yield severe drawbacks. In contrast, spatial Durbin specifications (SDM and SDEM) and the simple spatial lag of X (SLX) provide accurate estimates of direct impacts even in the case of misspecification. Regarding the indirect “spillover” effects, several—quite realistic—situations exist in which the SLX outperforms the more complex SDM and SDEM specifications.

SPATIAL AUTOREGRESSIVE (SAR) MODEL WITH ENSEMBLE LEARNING-MULTIPLICATIVE NOISE WITH LOGNORMAL DISTRIBUTION (CASE ON POVERTY DATA IN EAST JAVA)

2021 ◽  
Vol 14 (1) ◽  
pp. 89-97
Author(s):  
Dewi Retno Sari Saputro ◽  
Sulistyaningsih Sulistyaningsih ◽  
Purnami Widyaningsih
Keyword(s):  
Regression Model ◽  
Ensemble Learning ◽  
Spatial Data ◽  
Lognormal Distribution ◽  
Multiplicative Noise ◽  
Spatial Regression ◽  
Spatial Error ◽  
Spatial Regression Model ◽  
Sar Model ◽  
Spatial Autoregressive

The regression model that can be used to model spatial data is Spatial Autoregressive (SAR) model. The level of accuracy of the estimated parameters of the SAR model can be improved, especially to provide better results and can reduce the error rate by resampling method. Resampling is done by adding noise (noise) to the data using Ensemble Learning (EL) with multiplicative noise. The research objective is to estimate the parameters of the SAR model using EL with multiplicative noise. In this research was also applied a spatial regression model of the ensemble non-hybrid multiplicative noise which has a lognormal distribution of cases on poverty data in East Java in 2016. The results showed that the estimated value of the non-hybrid spatial ensemble spatial regression model with multiplicative noise with a lognormal distribution was obtained from the average parameter estimation of 10 Spatial Error Model (SEM) resulting from resampling. The multiplicative noise used is generated from lognormal distributions with an average of one and a standard deviation of 0.433. The Root Mean Squared Error (RMSE) value generated by the non-hybrid spatial ensemble regression model with multiplicative noise with a lognormal distribution is 22.99.

Irrigation Water Valuation Using Spatial Hedonic Models in GIS Environment

2010 ◽  
Vol 1 (4) ◽  
pp. 1-13 ◽  
Author(s):  
Zisis Mallios
Keyword(s):  
Irrigation Water ◽  
Regression Models ◽  
Spatial Regression ◽  
Software Tool ◽  
Hedonic Pricing ◽  
Spatial Error ◽  
Spatial Lag Model ◽  
Spatial Weights Matrix ◽  
Lag Model ◽  
Spatial Regression Models

Hedonic pricing is an indirect valuation method that applies to heterogeneous goods investigating the relationship between the prices of tradable goods and their attributes. It can be used to measure the value of irrigation water through the estimation of the model that describes the relation between the market value of the land parcels and its characteristics. Because many of the land parcels included in a hedonic pricing model are spatial in nature, the conventional regression analysis fails to incorporate all the available information. Spatial regression models can achieve more efficient estimates because they are designed to deal with the spatial dependence of the data. In this paper, the authors present the results of an application of the hedonic pricing method on irrigation water valuation obtained using a software tool that is developed for the ArcGIS environment. This tool incorporates, in the GIS application, the estimation of two different spatial regression models, the spatial lag model and the spatial error model. It also has the option for different specifications of the spatial weights matrix, giving the researcher the opportunity to examine how it affects the overall performance of the model.

The Effect of Socioeconomic and Environmental Factors on Obesity

2021 ◽  
Vol 12 (4) ◽  
pp. 58-74
Author(s):  
Ortis Yankey ◽  
Prince M. Amegbor ◽  
Marcellinus Essah
Keyword(s):  
Environmental Factors ◽  
Regression Models ◽  
Spatial Regression ◽  
Positive Association ◽  
Error Model ◽  
Spatial Error Model ◽  
Significant Positive Association ◽  
Spatial Error ◽  
Obesity Rate ◽  
Spatial Regression Models

This paper examined the effect of socio-economic and environmental factors on obesity in Cleveland (Ohio) using an OLS model and three spatial regression models: spatial error model, spatial lag model, and a spatial error model with a spatially lagged response (SEMSLR). Comparative assessment of the models showed that the SEMSLR and the spatial error models were the best models. The spatial effect from the various spatial regression models was statistically significant, indicating an essential spatial interaction among neighboring geographic units and the need to account for spatial dependency in obesity research. The authors also found a statistically significant positive association between the percentage of families below poverty, Black population, and SNAP recipient with obesity rate. The percentage of college-educated had a statistically significant negative association with the obesity rate. The study shows that health outcomes such as obesity are not randomly distributed but are more clustered in deprived and marginalized neighborhoods.

USE OF SPATIAL AUTOCORRELATION TO BUILD REGRESSION MODELS OF TRANSACTION PRICES

2013 ◽  
Vol 21 (4) ◽  
pp. 65-74 ◽  
Author(s):  
Radosław Cellmer
Keyword(s):  
Spatial Autocorrelation ◽  
Regression Models ◽  
Spatial Regression ◽  
Research Work ◽  
Property Market ◽  
Spatial Error ◽  
Spatial Lag Model ◽  
Spatial Weights Matrix ◽  
Lag Model ◽  
Spatial Regression Models

Abstract This paper presents the principles of studying global spatial autocorrelation in the land property market, as well as the possibilities of using these regularities for the construction of spatial regression models. Research work consisted primarily of testing the structure of the spatial weights matrix using different criteria and conducting diagnostic tests of two types of models: the spatial error model and the spatial lag model. The paper formulates the hypothesis that the application of spatial regression models greatly increases the accuracy of transaction price prediction while forming the basis for the creation of cartographic documents including, among others, maps of land value.

scholarly journals Measurement Error and the Specification of the Weights Matrix in Spatial Regression Models

2019 ◽  
Vol 28 (2) ◽  
pp. 284-292 ◽  
Author(s):  
Garrett N. Vande Kamp
Keyword(s):  
Monte Carlo ◽  
Measurement Error ◽  
Panel Data ◽  
Regression Models ◽  
Spatial Regression ◽  
Spatial Weights ◽  
The Core ◽  
Spatial Weights Matrix ◽  
Spatial Regression Models ◽  
And Control

While the spatial weights matrix $\boldsymbol{W}$ is at the core of spatial regression models, there is a scarcity of techniques for validating a given specification of $\boldsymbol{W}$. I approach this problem from a measurement error perspective. When $\boldsymbol{W}$ is inflated by a constant, a predictable form of endogeneity occurs that is not problematic in other regression contexts. I use this insight to construct a theoretically appealing test and control for the validity of $\boldsymbol{W}$ that is tractable in panel data, which I call the K test. I demonstrate the utility of the test using Monte Carlo simulations.

scholarly journals PEMODELAN INDEKS PEMBANGUNAN MANUSIA DI PROVINSI JAWA TENGAH TAHUN 2017 MENGGUNAKAN ANALISIS REGRESI SPASIAL

2020 ◽  
Vol 11 (1) ◽  
pp. 45
Author(s):  
Wahyuni Alwi ◽  
Jajang Jajang ◽  
Nunung Nurhayati
Keyword(s):  
Regression Models ◽  
Health Workers ◽  
Moving Average ◽  
Spatial Regression ◽  
Influence Factors ◽  
P Value ◽  
Linear Regression Models ◽  
Spatial Error ◽  
Central Java ◽  
Spatial Autoregressive

This research discussed about model of Human Development Index (HDI) in Central Java with spatial regression analysis. and identify  variables that give significant influence. First, analyze the influence factors based on result of p-value from t test in multiple linear regression models. Then, made spatial weight matrix with queen continguity method. After that, estimate spatial regression models, namely spatial autoregressive (SAR), Spatial error models (SEM), and spatial autoregive moving average (SARMA) and  choose the best model based on minimum AIC value. The results showed that SAR was the best spatial regression model and the significant variables was the gross enrollment rates at senior high schools, the health workers, and the district minimum wages. All of them that give positive influences. The variable that give biggest influence for HDI was the health workers. Full Article

Spatial regression modeling of tree height–diameter relationships

2009 ◽  
Vol 39 (12) ◽  
pp. 2283-2293 ◽  
Author(s):  
Qingmin Meng ◽  
Chris J. Cieszewski ◽  
Mike R. Strub ◽  
Bruce E. Borders
Keyword(s):  
Regression Models ◽  
Spatial Regression ◽  
Tree Height ◽  
Height Growth ◽  
Endogenous Variable ◽  
Exogenous Variables ◽  
Spatial Error ◽  
Spatial Lag Model ◽  
Lag Model ◽  
Spatial Regression Models

Tree height–diameter relationships are usually studied using linear or nonlinear models, but exogenous variables, especially spatially autocorrelated and dependent variables of tree diameter or height, are not often considered in height–diameter modeling. Three types of spatial regression models — spatial lag model, spatial error model, and spatial Durbin process model — are explored in this study. The height–diameter relationships are modeled using the spatial regression models to investigate the effects of spatial dependence and spatial autocorrelation and the roles of the exogenous variables generated by neighboring trees. Case study 1 shows that the spatial lag model should be used to analyze height–diameter relationships, in which heights of neighboring trees, which are exogenous variables, and the endogenous variable DBH significantly affect height growth. Case study 2 shows that the spatial error model performs better than other models, and that height growth is not only affected by its endogenous variable diameter but also by unobserved variables that vary spatially and result in residual spatial autocorrelation. Spatial regression models are an approach to height–diameter modeling that provide insight into how the endogenous variable diameter, the exogenous variables height and (or) diameter of neighboring trees, and locally varied but unobserved environmental or ecological variables contribute to height growth.

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