QML Estimation of Spatial Dynamic Panel Data Models with Time Varying Spatial Weights Matrices

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.

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.

Estimation of partially specified dynamic spatial panel data models with fixed-effects

Electricity sectors have experienced a transformation towards more liberalized and decentralized structure since the 1980s. This transformation process increases the importance of regional-level analysis. This study analyses the factors affecting regional electricity demand. As a case study, analysis is performed for Turkey by employing a dynamic spatial lag panel data model. The model allows for the inclusion of spatial interaction effects into the analysis which are important but mostly ignored in the literature. Findings show evidence of spatial spillover effects. Therefore, the paper concludes that one should consider spatial spillover effects of economic factors and regional energy policies.

This paper empirically investigates the spatial effects of trade openness on CO2 emissions in a panel of 54 middle‐income countries over the period 1996–2013. It adopts a static spatial panel data approach as well as a dynamic spatial panel data model with common factors to address the problems of spatial dependency and spillover effects and to explain the non‐stationary diffusion process of CO2 emissions across middle‐income countries. The empirical results indicate that: (i) there is a spatial dependence in CO2 emissions across the sample of middle‐income countries; (ii) Both global common factors and local spatial dependence are important drivers of the propagation patterns of CO2 emissions over the selected time‐period; (iii) trade openness, per capita real income, urbanization and energy intensity are the main determinants of CO2 emissions; (iv) the direct effect of trade openness on CO2 emissions is significantly positive; and (v) the indirect effect of trade openness on CO2 emissions is so significantly negative to overcome the positive direct effect, which implies a negative and significant total effect. This study provides robust policy implications for the sample of middle‐income countries to help them improve environmental quality.

Regional differences in spatial spillover and hysteresis effects: A theoretical and empirical study of environmental regulations on haze pollution in China

Inflation cost of strategic goods export restriction: evidence from dynamic spatial panel data model

Since the mid-1980s, Tunisia has conducted a structural adjustment program characterized by more privatization and economic opening. This transition has created unequal growth in the economic performance and the employment opportunities between coastal and interior regions (inland areas). The 14 January revolution has started as social demands against unequal employment opportunities and corruption. The identification of various factors explaining the employment growth in Tunisia's coastal area is necessary to understand regional disparities. In this study, we attempt to assess the impacts of institutional factors and industrial structures on manufacturing employment growth. We seek to answer the following key question: Why does employment grow in one region and not in other? Using panel data on five manufacturing sectors associated to 138 Tunisian coastal small localities (delegations) along six years (2002–2007), we will give some empirical evidence on regional employment growth. We use a dynamic spatial panel data model in order to consider the spatial and temporal effects in the analysis of the local employment growth. Our results show that spillovers have been found to be significantly effective only within a range of 15 km for high-tech industries and 50 km for low-tech. Our econometric result stand that agglomeration and diversity have a positive effect on the local employment manufacturing growth. However, competition has a negative effect. In addition, our results show that industrial zones do not increase employment.

Unified [formula omitted]-estimation of fixed-effects spatial dynamic models with short panels

Estimation of spatial autoregressive panel data models with fixed effects

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.