Testing for random effects and serial correlation in spatial autoregressive models

Estimation of spatial autoregressive panel data models with fixed effects

Estimation and testing of time-varying coefficients spatial autoregressive panel data model

Studying the impact of land urbanization on smog pollution has important guiding significance for the sustainable development of cities. This study adds the spatial effect between regions into the research framework of smog pollution control in China. On the basis of a panel dataset of 31 province-level administrative regions in China from 2000 to 2017, we investigate the impact of land urbanization on smog pollution. We construct a spatial weight matrix and use Moran’s I statistic and the spatial autoregressive panel data model. The research results show that land urbanization and smog pollution have an inverted U-shaped relationship. With the advancement of land urbanization, the area’s smog pollution first increases and then decreases. However, in general, China has not passed the inflection point and is still at a stage where increasing land urbanization rate aggravates smog pollution. Moreover, the country’s smog pollution has a significant spatial positive correlation that shows agglomeration. In that context, multiple environmental governance entities, including the government, enterprises, and the public, need to collaborate on measures to reduce smog pollution. Future urban construction in China will need to integrate solutions that address the current nexus between urbanization and smog pollution to achieve green and sustainable development.

Capturing household-level spatial influence in agricultural management using random effects regression

The Open Unemployment Level (OUL) is the percentage of the unemployed to the total labor force. One of the provinces with the highest OUL score in Indonesia is West Java Province. If an object of observation is affected by spatial effects, namely spatial dependence and spatial diversity, then the regression model used is the Spatial Autoregressive (SAR) model. Quantile regression minimizes absolute weighted residuals that are not symmetrical. It is perfect for use on data distribution that is not normally distributed, dense at the ends of the data distribution, or there are outliers. The Spatial Autoregressive Quantile Regression (SARQR) is a model that combines spatial autoregressive models with quantile regression. This research used the data regarding OUR in West Java in 2020 from the Central Bureau of Statistics. This study develops to modeling the Open Unemployment Level in all province in Indonesia using modified spatial autoregressive model with the quantile regression approach. This study compares the estimation results based on SAR and SARQR models to obtain an acceptable model. In this study, it was found that the SARQR model is better than SAR at dealing with the problems of dependency and diversity in spatial data modeling and is not easily affected by the presence of outlier data.

Background:Spatial panel data models have been widely studied and applied in both scientific and social science disciplines, especially in the analysis of spatial influence. In this paper, we consider the spatial dynamic nonparametric Durbin model (SDNDM) with fixed effects, which takes the nonlinear factors into account base on the spatial dynamic panel data models (SDPDM).Material and methods:Above all, we propose an iterative approach to estimate the spatial dynamic nonparametric Durbin model with corresponding hypothesis test and we find that convergence occurs since the second iteration. So we use a three stage iterative approach to improve the iterative approach.Results:The results indicate that the three stage iterative approach is more reliable when T or N is large and the accuracy of the nonparametric components estimation is very important.Conclusions:We believe that the three stage iterative approach can be applied to other spatial dynamic nonparametric panel data models as well.

Spatial autoregressive (SAR) models with varying coefficients are useful for capturing heterogeneous effects of the impacts of covariates as well as spatial interaction in empirical studies, and a wide range of popular models can be seen as its special cases, such as linear SAR models. In this study, we will propose a unified model selection method for the SAR model with varying coefficients to achieve two targets simultaneously: (1) variable selection (eliminate irrelevant covariates), and (2) identification of the covariates with constant effect among the relevant covariates. To do so, we follow the idea of group LASSO to incorporate two penalty functions to simultaneously do model selection and estimation. Monte Carlo experiments show that the proposed method performs well in finite samples. Finally, we illustrate the method with an application to the housing data of Chinese cities.

Stunting in children under 5 years of age is a condition where they have a length or height that is less than -2 standard deviations of the growth standard of Indonesian children. Stunting is caused by chronic malnutrition in the first 1000 days of life. The spatial panel data method was developed to solve problems related to spatial objects that are measured periodically by involving elements of area and time. The purpose of this study was to determine the best model and factors that influence stunting in children under 5 years of age in Indonesia using spatial panel data. The data used were from the website of the Central Statistics Agency and the publications of the Ministry of Health of the Republic of Indonesia in 2015-2019. Determination of the selected model is done by comparing the random effect spatial autoregressive model and spatial error model (SEM) random effect based on the value and Akaike information criterion (AIC). SEM random effect produces the largest value and the smallest AIC. The selected spatial panel data model in determining the factors that influence stunting in children under 5 years of age in Indonesia is the SEM random effect based on the largest and AIC compared to other models. Based on the selected model, children under five with malnutrition and poor nutrition, receiving Vitamin A, and the average monthly per capita expenditure on food have a significant effect on the percentage of stunting in children under five in Indonesia.

Is It a Curse or a Blessing to Live Near Rich Neighbors? Spatial Analysis and Spillover Effects of House Prices in Beijing

Spatial autoregressive (SAR) models are frequently applied in geographic research to account for spatial dependence. Although robust estimation methods (e.g. M-estimation) within SAR models have been investigated recently, key challenges remain: (1) How should the loss function be rationally designed in M-estimation for SAR models? (2) How can the scale parameter in M-estimation be evaluated? (3) How can the robust optimization problem be solved efficiently? In this research, we derived a closed-form bias-corrected least-squares estimator that outperforms the popular maximum likelihood estimator both numerically and statistically. We replaced the squared Euclidean norm with rational loss functions and then proposed an iteratively re-weighted least-squares scheme to attain a robust solution numerically. Simulation results demonstrate that our robust M-estimators, particularly redescending types, significantly improve estimation accuracy (reducing bias and root-mean-square error) and computational efficiency. In a real-world application with the Boston housing data, our method correctly identifies the underlying 22 outliers and increases the coefficient of determination from 0.78 to 0.90. Our future work will focus on enhancing computational performance through advanced optimization techniques.

If the ordinary least squares (OLS) diagnostics discussed in the previous chapter indicate the existence of spatial lag or spatial error dependence, the researcher will wish to model the type of dependence indicated by these diagnostics. If the OLS diagnostics indicate the presence of a diffusion process, the researcher will wish to estimate a spatial lag model via maximum likelihood (ML) estimation or an instrumental variables specification incorporating instruments for the spatially lagged dependent variable. Alternatively, if the OLS diagnostics indicate the existence of spatial error dependence, the researcher may choose to estimate a more fully specified OLS model to model the spatial dependence or may choose to employ a ML or generalized method of moments (GMM) approach incorporating the spatial dependence in the errors. The spatial dependence diagnosed via the diagnostics discussed in Chapter 5 may alternatively be produced by spatial heterogeneity in the effects of covariates. If this is the only source of spatial dependence, modeling this heterogeneity will be sufficient to capture the spatial dependence. As a consequence, any specification search should also consider the possibility of spatial heterogeneity, which is the focus of Chapter 7. This chapter will first, however, examine alternative approaches for modeling spatial dependence if spatial heterogeneity is not present. This chapter begins by examining ML estimation of spatial lag models that derives from Ord (1975). Next, I explore alternative instrumental variables and GMM estimators for spatial lag dependence. Next, I turn to approaches for estimating spatial error models. I conclude by considering areas of concern in the estimation of spatial models. These include estimators for large sample sizes and diagnostics for continued spatial dependence. MAXIMUM LIKELIHOOD SPATIAL LAG ESTIMATION The mixed regressive, spatial autoregressive model, or spatial lag model, extends the pure spatial autoregressive model considered in Section 3.2 to include also the set of covariates and associated parameters: y = ρ W y +Xβ+e where X is again an N by K matrix of observations on the covariates, β is a K by 1 vector of parameters, and the remaining notation is as discussed in Section 3.2.

This paper studies the fact that 37 percent of the internal migrants in China do not sign a labor contract with their employers, as revealed in a nationwide survey. These contract-free jobs pay lower hourly wages, require longer weekly work hours, and provide less insurance or on-the-job training than regular jobs with contracts. We find that the co-villager networks play an important role in a migrant's decision on whether to accept such insecure and irregular jobs. By employing a comprehensive nationwide survey in 2011 in the spatial autoregressive logit model, we show that the common behavior of not signing contracts in the co-villager network increases the probability that a migrant accepts a contract-free job. We provide three possible explanations on how networks influence migrants' contract decisions: job referral mechanism, limited information on contract benefits, and the mini labor union formed among co-villagers, which substitutes for a formal contract. In the sub-sample analysis, we also find that the effects are larger for migrants whose jobs were introduced by their co-villagers, male migrants, migrants with rural Hukou, short-term migrants, and less educated migrants. The heterogeneous effects for migrants of different employer types, industries, and home provinces provide policy implications.

This paper analyzes the current situation of university English teaching and constructs a trinity of EGP+ESP+EIC multiple teaching innovation systems for university English based on the principles of English teaching innovation. In order to analyze this innovative teaching system of English, a stratified statistical model and a spatial autoregressive model are combined, and four ordinal stratified spatial autoregressive Probit models are innovatively constructed to model data where the dependent variable is ordinal, and there is a spatial dependency situation with a stratified structure. The analysis shows that in the process of continuous innovation of English teaching mode, the number of people who are dissatisfied with English teaching decreases by 30%, the number of people who are very satisfied increases by 35%, and the number of people who are very satisfied accounts for 42.5% of the total number of people and 90% of the total number of people who are satisfied in the final stage of innovation of English teaching in university. The English teaching innovation strategy for university students based on this study is feasible.

Mixed spatial autoregressive (SAR) models with numerical covariates have been well studied. However, as non-numerical data, such as functional data and compositional data, receive substantial amounts of attention and are applied to economics, medicine and meteorology, it becomes necessary to develop flexible SAR models with multiple data types. In this article, we integrate three types of covariates, functional, compositional and numerical, in an SAR model. The new model has the merits of classical functional linear models and compositional linear models with scalar responses. Moreover, we develop an estimation method for the proposed model, which is based on functional principal component analysis (FPCA), the isometric logratio (ilr) transformation and the maximum likelihood estimation (MLE) method. Monte Carlo experiments demonstrate the effectiveness of the estimators. A real dataset is also used to illustrate the utility of the proposed model.

SummaryThis paper analyzes the endogeneity bias problem caused by associations of members within a network when the spatial autoregressive (SAR) model is used to study social interactions. When there are unobserved factors that affect both friendship decisions and economic outcomes, the spatial weight matrix (sociomatrix; adjacency matrix) in the SAR model, which represents the structure of a friendship network, might correlate with the disturbance term of the model, and consequently result in an endogenous selection problem in the outcomes. We consider this problem of selection bias with a modeling approach. In this approach, a statistical network model is adopted to explain the endogenous network formation process. By specifying unobserved components in both the network model and the SAR model, we capture the correlation between the processes of network and outcome formation, and propose a proper estimation procedure for the system. We demonstrate that the estimation of this system can be effectively done by using the Bayesian method. We provide a Monte Carlo experiment and an empirical application of this modeling approach on the friendship networks of high school students and their interactions on academic performance in the Add Health data. Copyright © 2015 John Wiley & Sons, Ltd.