Spatial Panel Data Model Research Articles

Construction Strategy of Regional Investment and Financial Management System under Spatial Effect Modeling

Abstract In recent years, China’s regional economic differences have become more and more apparent, and the problem of unbalanced allocation of financial resources has become more and more prominent. In this context, it is of great practical significance to study the construction strategy of regional investment and financial management systems using the spatial effect model. This study adopts the spatial econometric method to establish the spatial effect model. Considering the limitations of the spatial panel data model, this paper also uses the general dynamic spatial panel model with fixed effects to analyze the relationship between regional investment and the financial management system. Finally, through the Granger causality test on the spatial effect analysis of regional investment and financial management, this paper verifies the study of the interaction between the financial management system and regional investment. The research data show that in the spatial panel model, the regression coefficient of the explanatory variable F financial management system is 0.2431, which is significant at the 1% level, indicating that with a little increase in the degree of financial management system, the regional economy grows by 0.2431. The fixed effect general dynamic spatial panel model’s superiority is confirmed by the higher goodness of fit of the general dynamic spatial panel model compared to the spatial panel model.

Housing Choices of Young Adults in Sweden

This study investigates why young adults live with their parents in Sweden. As young adults’ living arrangements affect decisions about marriage, education, childbirth, and participation in the workforce, more knowledge for policymakers is crucial to implementing effective policies to support young adults and promote financial independence and well-being. Using a data set from 1998 to 2021 at the municipal level in Sweden, we used a spatial autoregressive panel data model to examine the proportion of young adults living at home and the regional disparities. The study uncovered intraregional variations that illustrate how different municipalities in Sweden exhibit different patterns of young adults living at home. Our findings reveal that economic factors such as unemployment significantly impact this pattern. Housing market dynamics, demographic factors, cultural differences, and location-specific characteristics also play an essential role in explaining this pattern. These findings suggest that the key drivers are the lack of rental housing, high unemployment rates, a high degree of urbanisation, interregional migration, and social capital (such as social cohesion and inclusion).

Recognition and variable selection in sparse spatial panel data models with fixed effects

Recognition and variable selection in sparse spatial panel data models with fixed effects

Statistical Inference for Partially Linear Varying Coefficient Spatial Autoregressive Panel Data Model

This paper studies the estimation and inference of a partially linear varying coefficient spatial autoregressive panel data model with fixed effects. By means of the basis function approximations and the instrumental variable methods, we propose a two-stage least squares estimation procedure to estimate the unknown parametric and nonparametric components, and meanwhile study the asymptotic properties of the proposed estimators. Together with an empirical log-likelihood ratio function for the regression parameters, which follows an asymptotic chi-square distribution under some regularity conditions, we can further construct accurate confidence regions for the unknown parameters. Simulation studies show that the finite sample performance of the proposed methods are satisfactory in a wide range of settings. Lastly, when applied to the public capital data, our proposed model can also better reflect the changing characteristics of the US economy compared to the parametric panel data models.

Estimation and variable selection for high-dimensional spatial dynamic panel data models

Spatiotemporal modeling of networks is of great practical importance, with modern applications in epidemiology and social network analysis. Despite rapid methodological advances, how to effectively and efficiently estimate the parameters of spatial dynamic panel models remains a challenging problem. To tackle this problem, we construct consistent complex least-squares estimators by the eigendecomposition of a spatial weight matrix method originally proposed for undirected networks. We no longer require all eigenvalues and eigenvectors to be real, which is a remarkable achievement as it implies that the proposed method is now applicable to spatiotemporal data modeling of directed networks. Under mild, interpretable conditions, we show that the proposed parameter estimators are consistent and asymptotically normally distributed. We also present a complex orthogonal greedy algorithm for variable selection and rigorously investigate its convergence properties. Moreover, we incorporate fixed effects into the spatial dynamic panel models and provide a model transformation so that the proposed method can also be applied to the transformed model. Extensive simulation studies and data examples demonstrate the effectiveness of the proposed method.

Relative risk of the COVID-19 pandemic and regional inflation convergence in Indonesia

This study investigates the relationship between the risk of a COVID-19 pandemic and the regional inflation convergence in Indonesia. It is important to examine the regional inflation convergences to evaluate the inflation rate and the impact of macroeconomic policy on inflation convergence, as the COVID-19 recession differs from the previous inflation recession. To calculate the impact of the relative risk of the COVID-19 pandemic on regional inflation convergence in Indonesia, the dynamic econometric spatial panel data model is used to calculate relative risk without spatial or SIRs. On the contrary, for calculating the risk relative to spatial elements, the CAR Leroux or BSCL Bayesian Spatial Model is used. Using BSCL, the calculation of the relative risk value for the COVID-19 pandemic concludes that Sumatera Island, Java Island, Kalimantan Island, Sulawesi Island, Maluku Island, and Papua Island have high risks, while Bali Island and Nusa Tenggara Island have low risks. In both static and dynamic models, the influence of currency circulation on inflation convergence is positive, and the relative risk of a COVID-19 pandemic on inflation convergence is negative. Studies show that the COVID-19 pandemic is a deterrent to inflation convergence, while the circulation of money drives inflation convergence.

Detrended seasonal relationships and impact of climatic factors combined with spatiotemporal effect on the prevalence of human brucellosis.

Human brucellosis (HB) is a seasonal and climate-affected infectious disease that is posing an increasing threat to public health and economy. However, most of the research on the seasonal relationships and impact of climatic factors on HB did not consider the secular trend and spatiotemporal effect related to the disease. We herein utilized long-term surveillance data on HB from 2008 to 2020 using sinusoidal models to explore detrended relationships between climatic factors and HB. In addition, we assessed the impact of such climatic factors on HB using a spatial panel data model combined with the spatiotemporal effect. HB peaked around mid-May. HB was significantly correlated with climatic factors with 1-5-month lag when the respective correlations reached the maximum across the different lag periods. Each 0.1 °C increase in temperature led to 0.5% decrease in the 5-month lag incidence of HB. We also observed a positive spatiotemporal effect on the disease. Our study provides a detailed and in-depth overview of seasonal relationships and impact of climatic factors on HB. In addition, it proposes a novel approach for exploring the seasonal relationships and quantifying the impacts of climatic factors on various infectious diseases.

Intergovernmental Transfers and Economic Development: Spatial Panel Data Analysis in Brazilian States

Brazilian Fiscal Federalism makes intensive use of mechanisms of interjurisdictional transfers as strategies to combat poverty and regional differences, even while recognizing that, if poorly conceived, they can generate or aggravate existing regional demands and create externalities that affect the behavior of economic development of the recipients´ governments. In this sense, this paper aims to assess the effect of intergovernmental transfers from the State Participation Fund (FPE) on the economic development of Brazilian states. To this end, we employed spatial panel data models for the 27 Brazilian federative units from 1997 to 2016. The results show that when spatial dependence is controlled, the spatially lagged FPE has a negative effect on the GDP per capita of the states. This indicates that the criteria for determining the fund transfers must be expanded, considering other factors besides the sole equalization of horizontal imbalances between states.

Associations of (un)observed factors and gender inequality in European countries: Evidence from spatial panel data model

This study aims to explore gender inequality using spatial panel data models and take a step forward in adopting such a comprehensive approach to identify and estimate the factors driving gender inequality in European countries. The analysis utilizes panel data covers 41 European countries spanning the period from 1990 to 2020. By employing spatial panel data models, the study accounts gender inequality by mainly considering unobserved effects and various other factors, including social, economic, human rights, and environmental degradation. The findings reveal that spatial effects play a significant role in gender inequality, as evidenced by the Gender Inequality Index heatmap and the Moran's I spatial autocorrelation test. Among the panel data regression models estimate to spatial effects, the FE-SAC model is determined to be the most efficient and consistent based on various model selection criteria. The main results show that life expectancy at birth, expected years of schooling, and democracy index have decreasing effects on gender inequality, whereas CO2 emissions per capita have increasing effects. Ultimately, the study concludes that gender equality is linked to unobserved effects such as the same culture, beliefs, and values which are main reasons of spatial effects.

Exploring the effects of market-oriented reforms on industrial land use eco-efficiency in China: Evidence from a spatial and non-linear analysis

The abundant supply of industrial land has played an important role in China's large-scale urbanization and industrialization. A large amount of industrial land is characterized by low-efficiency use and high industrial-pollution emissions. To improve industrial land use eco-efficiency (ILUE), the Chinese government is continuously strengthening the market-oriented reforms of industrial land transactions. Different from previous studies that assess the effectiveness of such an effort based on non-spatial and linear perspectives, this study explores the spatial and non-linear effects of the market-oriented reforms on ILUE, using data of 99 cities in China collected from 2009 to 2020. We evaluate the market-oriented reforms with two indicators of industrial land premium rate (ILPR) and the ratio of bidding, auction, and listing (RBAL) in industrial land transactions, then measure the spatial and non-linear effects using the spatial panel data model. The results show that the rising ILPR in adjacent cities negatively affected local ILUE, but the RBAL had no obvious spatial effects. The relationship between ILPR and ILUE showed a U-shaped curve; that between RBAL and ILUE was an inverted U-shape. The effects were heterogeneous across regions. The rising ILPR of adjacent cities positively affected local ILUE in the east, while negatively in the central region. The increasing RBAL had no noticeable spatial effects except in the central region. The western region did not show significant spatial effects. The relationships between ILUE and the two indicators showed U-shaped curves of different shapes in the three regions. The findings help to improve future ILUE by managing the industrial land markets in better ways.

Profile Maximum Likelihood Estimation of Single-Index Spatial Dynamic Panel Data Model

In this paper, the spatial dynamic panel data (SDPD) model is extended to the single-index spatial dynamic panel data (Si-SDPD) model by introducing a nonlinear connection function to reflect the interaction between explanatory variables. The Si-SDPD model not only retains the advantages of the parametric SDPD model in dealing with spatial and temporal interaction effects and spatio-temporal dependencies, but also solves the limitations of the parametric SDPD model that may lead to missed bias. It reduces the data dimension of non-parametric models and enhances the practicability and explanatory power of parametric models. Since the parts of the model to be estimated contain unknown functions, we propose a new estimation method, a profile maximum likelihood (PML) method, to solve the problem of incidental parameters in the estimation. Under the assumption that the spatial coefficients are known, we preliminarily estimate the unknown function by carrying out local polynomial estimation, so as to transform the model into the parametric form for solving purposes. We then solve the dynamic panel parametric model via quasi-maximum likelihood (QML) estimation. We derive the asymptotic properties of profile maximum likelihood estimators (PMLEs) and find that, under certain regularity conditions, both parametric and non-parametric estimators are consistent. Monte Carlo results show that PMLEs have good finite sample performance.

Economic Determinants of Birth Rate in Romania. A Spatial Analysis

Abstract The purpose of this article is to determine the factors influencing the birth rate in Romania, by incorporating explicitly the spatial factor in the proposed models. The study is justified by the dramatic fall of the birth rate over the past three decades. With a negative natural population growth and an increasing number of emigrants, the population will become older and there will be a few million less in the next decades. To achieve the objective, various spatial modelling methods were used, such as Spatial AutoRegressive Model (SAR), Spatial Error Model (SEM), Geographically Weighted Regression (GWR) and a spatial panel data model. The data granularity is at the county level for the year 2020. Results show that GDP per capita and the amount of financial support received by families for raising a child have a significant effect on the birth rate. Using a spatial approach for modelling the birth rate, we reveal demographic problems that may exist in certain areas and identify the regions that would require a policy to stimulate birth rates.

Estimation of the Non-Parametric Spatial Dynamic Panel Data Model with Fixed Effects

In this paper, the spatial dynamic panel data (SDPD) model with fixed effects is extended to a non-parametric form by relaxing the linear or nonlinear parameter structure of explanatory variables. The non-parametric spatial dynamic panel data (NSDPD) model with fixed effects not only retains the advantages of the SDPD model which can deal with spatial and/or temporal individual characteristics and spatio-temporal dependencies but also solves the limitation that may lead to specification errors. It also enhances the flexibility and practicability of the spatial econometric model. Since the model to be estimated contains unknown functions, we propose this profile maximum likelihood (PML) method to solve the problem of the incidental parameters in the estimation. Under the assumption that the spatial coefficients are known, we first eliminate the influence of the time effect by substitution and then use the local polynomial estimation to preliminarily estimate the unknown function so as to transform the model into the parametric form for solving. We derive the asymptotic properties of PMLEs and find that under certain regularity conditions, both parametric and non-parametric estimators are consistent. The Monte Carlo results show that the estimators have good finite sample performance. We illustrate the empirical relevance of the model by applying it to examine the impact of tourism dynamics on economic development in the Yangtze River Delta region of China.

The Spatiotemporal Elasticity of Age Structure in China’s Interprovincial Migration System

The supply and demand of labour in the market can often experience profound transformations as a result of an ageing population. This can substantially impact the sustainable development of human society. Since the 1970s, China’s internal migration has continued to increase, but there has been a shift toward an ageing trend since the year 2000. How does the change of age structure interact with socioeconomic development to produce changes in the supply and demand of labour over space and time? This study constructs a spatial dynamic panel data model of interprovincial migration flows in China from 1985 to 2015 in order to quantify the spatiotemporal impacts of age structure on migration. The preliminary results indicate that age structure plays the most important role among regional socioeconomic characteristics of migration, dominated by the large supply, demand, and cross elasticities of labour population. Labour demand and the cross elasticities of total dependency ratio rank second. Comparatively, the total elasticities of regional GDP and wage levels on migration flows are not as significant as expected. This study lays the groundwork for identifying the interaction mechanisms of migration systems and provides important insights on regional sustainable development from the perspective of ageing.

The spatial spillover effects of energy transition and trade openness on CO2 emissions

This study explores the spatial effects of energy transition and trade openness on CO2 emissions from 1990 to 2019 in 75 BRI countries. We use the spatial panel data models to address spatial dependency and the spillover impact between adjacent countries. First, the estimated findings support the occurrence of spatial autocorrelations in CO2 emissions across BRI countries. Second, we find that trade openness, natural gas consumption, and these variables' spatial effects positively affect CO2 emissions. Hence, renewable energy consumption and its spatial effect are negatively significant on CO2 emissions, while the GDP and its spatial effect are positive and negative, respectively. This study provides robust policy implications for promoting a green economy, CO2 reduction legislation, renewable energy, and the eventual replacement of natural gas.

Forecasting regional GDPs: a comparison with spatial dynamic panel data models

ABSTRACT The monitoring of the regional (provincial) economic situation is of particular importance due to the high level of heterogeneity and interdependences among different territories. Although econometric models allow for spatial and serial correlation of various kinds, the limited availability of territorial data restricts the set of relevant predictors at a more disaggregated level, especially for gross domestic product (GDP). Combining data from different sources at NUTS-3 level, this paper evaluates the predictive performance of a spatial dynamic panel data model with individual fixed effects and some relevant exogenous regressors, by using data on total gross value added (GVA) for 103 Italian provinces over the period 2000–2016. A comparison with nested panel sub-specifications as well as pure temporal autoregressive specifications has also been included. The main finding is that the spatial dynamic specification increases forecast accuracy more than its competitors throughout the out-of-sample, recognising an important role played by both space and time. However, when temporal cointegration is detected, the random-walk specification is still to be preferred in some cases even in the presence of short panels.

Analysis of the effect of heterogeneous human capital on new urbanization in China

The impact of human capital on the process of urbanization is very important. This research aims to examine the effect of heterogeneous human capital on the process of new urbanization and divides human capital into basic human capital, skill-based human capital, and knowledge-based human capital. The 2005–2019 provincial spatial panel data model analyzes the impact of heterogeneous human capital and spatial spillover effects on the process of new urbanization based on distinct spatial weight matrices. The results indicate that the three types of human capital have obvious spatial correlations with the evolution of new urbanization. Basic and skill-based human capital have a significant positive influence on boosting the process of new urbanization, while the influence of knowledge-based human capital is not obvious, and less effect on surrounding cities. In terms of regional heterogeneity, the promotion effect of human capital in the east is higher than that in the middle and west. According to the research, this paper suggests that the government should guarantee the social welfare of basic human capital and skilled human capital, because they are an important force in the construction of urbanization, and weaken the impact of academic qualifications on the introduction of urban talents.

Wild Bootstrap-Based Bias Correction for Spatial Quantile Panel Data Models with Varying Coefficients

This paper studies quantile regression for spatial panel data models with varying coefficients, taking the time and location effects of the impacts of the covariates into account, i.e., the implications of covariates may change over time and location. Smoothing methods are employed for approximating varying coefficients, including B-spline and local polynomial approximation. A fixed-effects quantile regression (FEQR) estimator is typically biased in the presence of the spatial lag variable. The wild bootstrap method is employed to attenuate the estimation bias. Simulations are conducted to study the performance of the proposed method and show that the proposed methods are stable and efficient. Further, the estimators based on the B-spline method perform much better than those of the local polynomial approximation method, especially for location-varying coefficients. Real data about economic development in China are also analyzed to illustrate application of the proposed procedure.

Effects of transboundary PM2.5 transported from China on the regional PM2.5 concentrations in South Korea: A spatial panel-data analysis.

This study identifies causal links between a high-PM2.5 episode in Korea and air pollutants originating from China during a high-PM2.5 episode that occurred in Korea between February 23 and March 12, 2019. Datasets on ground-based PM2.5 levels in Korea and China, airflows from the back-trajectory models, and satellite images were investigated, and long-range transboundary transport (LRTT) effects were statistically analyzed using spatial panel-data models. The findings are: 1) visual presentations of the observed PM2.5 concentration in China and Korea, back-trajectory air flows, and satellite images from the Moderate Resolution Imaging Spectroradiometer Aerosol Optical Depth and the Copernicus Atmosphere Monitoring Service clearly show that transboundary air pollutants from China affect PM2.5 concentration in Korea; 2) the effect of LRTT from China is likely to intensify under certain meteorological conditions, such as westerly winds from China to Korea, the formation of high pressure in China and low pressure in Korea, relatively high temperature, and stagnant air flow in Korea; 3) the results from the spatial panel-data models provide statistical evidence of the positive effect of LRTT from China on increasing local PM2.5 concentration in Korea. The nationwide average LRTT contributions to PM2.5 concentration in Korea are 38.4%, while regional contributions are 41.3% for the Seoul Metropolitan Area, 38.6% for the northwest region, and 27.5% for the southeast regions in Korea, indicating the greatest impact on the Seoul Metropolitan Area.

A spatial origin‐destination approach for the analysis of local tourism demand in Italy

AbstractThe article assesses the competitiveness of tourist destinations, while accounting for spatial features of tourism and information on both the origin and the destination of tourists. Using a dynamic spatial panel data model with common factors within the origin‐destination framework, it explores unilateral inbound tourism flows in 110 Italian regions from 23 European origin countries over the period 2004–2017. The estimation and test results confirm the need to consider this advanced spatial econometric model. In an empirical application measuring tourism resilience of regions based on this model, the results demonstrate the importance of promoting coordinated actions between neighbouring destinations to support the attractiveness of local clusters.