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

Testing for random effects and serial correlation in spatial autoregressive models

Estimation of spatial autoregressive panel data models with fixed effects

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.

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 dynamic panel data (SDPD) models have received great attention in economics in recent 10 years. Existing approaches for the estimation and test of SDPD models are quasi-maximum likelihood (QML) approach and generalized method of moments (GMM). In this article, we introduce the empirical likelihood (EL) method to the statistical inference for SDPD models. The EL ratio statistics are constructed for the parameters of spatial dynamic panel data models. It is shown that the limiting distributions of the empirical likelihood ratio statistics are chi-squared distributions, which are used to construct confidence regions for the parameters of the models. Simulation results show that the EL based confidence regions outperform the normal approximation based confidence regions.

Bayesian analysis of spatial dynamic panel data model with convex combinations of different spatial weight matrices: A reparameterized approach

Weak ties in large-scale networks facilitate the diffusion of information and resources, generating spillover effects. While geographic proximity may generate straightforward networks, a non predetermined spillover framework better accommodates socioeconomic interactions. In such cases, it is important to test the endogeneity of a spatial weight matrix before full estimation to ensure valid and efficient model selection. I propose a robust score test to assess this endogeneity in the weak-tied spatial dynamic panel data model, emphasizing its asymptotic optimality and computational efficiency. First, I correct score function biases to address the incidental parameters problem. Second, I adjust the score functions to enhance robustness against local parametric misspecifications. Monte Carlo simulations demonstrate favorable finite sample properties. In my application, I illustrate how this test helps model selection for the Solow-Swan growth model, considering spillovers from geographic and knowledge proximities. The estimation results reveal endogenous spillover effects with weakly positive impacts on real income per worker, while savings show positive internal effects but negative spillover effects, possibly due to competition for investment. Notably, the labor growth rate positively influences knowledge accumulation and innovation, leading to beneficial effects on real income per worker through both internal and spillover channels.

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.

This article examines the effects of the Covid-19 crisis on the banking sector of Romania using a spatial panel dataset of 21 months, from April 2020 to December 2021, among 41 counties. The empirical setup exploits the spatial and time-series variations of the banking sector during the pandemic via means of exploratory data analysis, simple linear models for each month with spatial diagnosis, and spatial panel data models with fixed effects. County-level differences and particular disturbances in the trend of banking services are highlighted against the turning points of the pandemic. We show that increasing Covid-19 infections result in higher loans and savings per capita, with significant spatial interactions given by the neighboring counties.

Objective: To explore the spatiotemporal distribution and macro-related factors of congenital syphilis in Guangdong province and provide suggestions and recommendations for prevention. Methods: Yearly reported cases of syphilis and some influencing factor data of Guangdong province were collected from 2005 to 2017. The spatiotemporal distribution of congenital syphilis was described. Meanwhile, the spatial panel data model was constructed to analyze the relationship between the incidence rates of congenital syphilis and related factors. Results: From 2005 to 2017, 13 361 cases of congenital syphilis were reported in Guangdong province. The number of congenital syphilis cases rose to its highest point during 2005-2011. A slow downward trend followed. The peaks of incidence were observed from August to December. The incidence of the non-Pearl River Delta region has experienced a process of rising first and then decreasing. The spatial panel data model results showed that congenital syphilis had significant positive spatial autocorrelation (P<0.001). The incidence of primary and secondary syphilis in women (β=0.822,P<0.001), gross domestic product per capita (β=3.511,P<0.001), net migrate rate (β=0.215,P=0.047) and maternal system management rate(β=0.017,P=0.021) were all positively correlated with the incidence rates of congenital syphilis. Registered population density (β=-1.167,P<0.001) and prenatal examination rate (β=-0.038,P=0.031) was negatively correlated with congenital syphilis. Conclusions: The incidence of congenital syphilis was spatially aggregated in Guangdong province from 2005 to 2017. The intensity of prevention might be strengthened in cities with developed economies and high net migration rates, which have high risks of congenital syphilis. Controlling the incidence of primary and secondary syphilis in women and increasing the prenatal examination rate for pregnant women appears effective prevention measures of congenital syphilis.

The recent proliferation of private tourist accommodations on what has been known as sharing economy has induced new models on urban tourism and on the use of traditional housing. Urban tourism pressure has caused many transformation processes with important impacts in neighborhoods with high tourist interest, which are shown in the evolution of certain urban sustainability indicators, such as those proposed by the UN-Habitat Agenda for Sustainable Cities in line with the principles of the Leipzig Charter on Sustainable European Cities. Due to the above, the objective of this work is to analyze the spatial distribution of Airbnb accommodations, and explore the factors associated with the situation of Airbnb rentals in relation to the indicators of urban sustainability of neighborhoods and variables related to the gentrification processes of neighborhoods in which there is crowding or overtourism, measured through the Global Tourist Stress Index. For this, a first-order spatial autoregressive panel (SAR) data model with fixed effects has been specified, the results of which provide us with information to understand how sustainability indicators in the neighborhoods of the city of Madrid in the time period 2015–2018, they would explain the location and number of Airbnb accommodations found there. Additionally, it allows us to observe the existence of a spillover effect from the central neighborhoods, with a high per capita income, to nearby neighborhoods with lower income due to the level of tourist crowding, which is a contribution to the scarce existing literature.

The paper examines the impact of R&amp;amp;D expenditures on gross domestic product (GDP) at the NUTS 1 level in Germany. Using the spatial panel data analysis method, the existence of spatial dependencies among regions was investigated, and the findings revealed positive spatial effects. Based on the tests conducted, the Random Effects Spatial Error Panel Data Model was identified and accepted as the appropriate model for this paper. It was determined that a one-unit increase in R&amp;amp;D expenditures leads to a 7% increase in GDP at a 1% significance level. Furthermore, the model was found to have an explanatory power of 63%, demonstrating that it is both statistically significant and reliable. The paper is significant as it emphasizes the multiplier effect that highlights the spatial impacts of R&amp;amp;D expenditures. Additionally, the heterogeneity among regions was found to have a meaningful spatial relationship with economic performance, leading to the development of an econometric model proposal in this context.

In this paper, we analyse the ability of a dynamic spatial panel data model without explanatory variables to explain a variable of interest, in this case employment in the fifty Spanish provinces. The best model is a dynamic fixed effect with a spatial lag structure in an equation estimated through the unconditional ML procedure. Predictions derived from this selected model are compared with those derived from fifty seasonal ARIMA models that also treat outlier observations. The results indicate that forecasts derived from a single estimated spatial panel data model are as accurate as those derived from the estimation of fifty seasonal ARIMA models. This shows that spatial panel data models play an important role in forecasting.

From the perspective of spatial geography, this paper verifies the spatial dependence of China’s provincial carbon emissions. The contribution of impact factors with different fields of view to carbon emissions’ growth is estimated based on the spatial panel data model, t. The study found that during 2000–2015, China’s energy-related carbon emissions in the provinces were dependent on the spatial, and the spatial spillover effect of carbon emissions and its influencing factors in the neighboring provinces are obvious. It was also found that economic growth, industrial structure, financial development, and urbanization rates are positive, and the effect of the population and technological progress on reducing carbon emissions is significant. The effect of source price, export dependence, and fiscal decentralization on carbon emissions’ growth did not pass a significance test. In the formulation of carbon emission-related policies and development plans, the government must consider the effect of the influencing factors affecting the carbon emissions in the adjacent area and combine the carbon emissions and spatial spillover effect of the related factors in order to reduce carbon emissions in the time dimension and the spatial dimension of China as a whole.

Spatial Panel Data Model with Error Dependence: A Bayesian Separable Covariance Approach