Spatial Dynamic Panel Data Model Research Articles

Ecological footprint spillover effects in Europe: New insights from dynamic spatial panel data model with common shocks

This comprehensive study addresses the urgent global challenges of climate change and environmental degradation by focusing on the Ecological Footprint (EF). Unlike previous studies, it introduces a novel approach incorporating spatial spillover, temporal effects, and common shocks in panel data analysis. The spatial spillover effect highlights the influence of trade, pollution havens, and competition between neighboring countries on EF. The temporal effects emphasize the significance of historical production patterns and export strategies in shaping the current EF. The study also considers the impact of exogenous common shocks, such as international agreements and global events, on EF. Utilizing a dynamic spatial panel data model with common shocks, the research examines 40 European countries from 1992 to 2020, revealing the significant impact of biocapacity, energy consumption, industrialization, and globalization on EF. Findings indicate that spatial spillover effects contribute to EF transfer, emphasizing the need for collaborative global efforts. The study sheds light on the interconnectedness of environmental impacts and underscores the importance of considering both weak and strong forms of cross-sectional dependence in achieving accurate estimations. The research enriches our understanding of EF determinants and provides nuanced insights for policymakers striving to develop effective strategies for sustainable resource management and environmental conservation.

Inference of Dynamic Spatial Panel Data Model and Its Application in Carbon Emission Analysis

The Spatial Dynamic Panel Data (SDPD) model is a widely used statistical model in the fields of economics and social sciences and has been the subject of extensive research by many scholars in recent years. Existing methods for parameter estimation primarily focus on improvements to the Generalized Method of Moments (GMM) and Quasi-Maximum Likelihood (QML). In this paper, we employ the method of Empirical Likelihood (EL) for statistical inference of the dynamic spatial panel data model and obtain confidence regions for the parameters. Through numerical simulations, we present the performance of the confidence regions obtained using the Empirical Likelihood (EL) and the Asymptotically Normal (NA) methods under finite samples and compare the two approaches. Finally, we analyze carbon using the suggested model and techniques.

Nonlinear Spatial Dynamic Panel Data Models with Endogenous Dominant Units: An Application to Share Data

This article develops a nonlinear spatial dynamic panel data model with one particularly interesting application to a structural interaction model for share data. To account for effects from dominant (popular) units, the spatial weights matrix in our model can allow for unbounded column sums. To account for heterogeneity, our model includes two-way fixed effects and heteroscedastic errors. We further consider the potential endogeneity of the spatial weight matrix constructed from socioeconomic distance. We investigate the quasi-maximum likelihood estimator (QMLE), generalized methods of moments estimator (GMME), and root estimator (RTE), and establish their consistency and asymptotic normality based on the near epoch dependence (NED) framework. The RTE can derive a relatively computationally simple and closed-form solution without evaluating the QMLE’s Jacobian matrix as well as the iterations by GMME. We consider both n , T → ∞ , and the strength of the dominant units is equal to 1 when T → ∞ . For the purpose of empirical analysis, we derive the marginal effects and their limiting distributions based on the proposed estimators. In an empirical application, we apply our model to China’s prefecture city-level data, revealing significant spillover effects of the tertiary industry share. These findings suggest that the development of the tertiary sector in large cities can foster its growth in small cities.

Testing Spatial Dynamic Panel Data Models with Heterogeneous Spatial and Regression Coefficients

Spatio‐temporal data are often analysed by means of spatial dynamic panel data (SDPD) models. In the last decade, several versions of these models have been proposed, generally based on specific assumptions and estimator properties. We focus on an SDPD model with heterogeneous coefficients both in the spatial and exogeneous regression components. We propose a strategy to identify the specific structure of the SDPD model through a multiple testing procedure that allows to choose between a general version of the model and a nested version derived from the general one by imposing restrictions on the parameters. Our proposal can be used to test the homogeneity of the model parameters as well as the absence of specific components, such as spatial effects, dynamic effects or exogenous regressors. It is also possible to use the proposed testing procedure for the identification of relevant locations. The theoretical results highlight the consistency of the testing procedure in the high‐dimensional setup, when the number of spatial units goes to infinity and exceeds the number of time‐observations per spatial unit. Further, we also conduct a Monte Carlo simulation study, which gives empirical evidence of the good performance of the testing procedure in finite samples.

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.

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.

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.

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.

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.

The diffusion of COVID-19 across Italian provinces: a spatial dynamic panel data approach with common factors

ABSTRACT This study adopts a spatial dynamic panel data model with common factors and a connectivity matrix based on cross-province population flows to help explain the spread of COVID-19 infections across Italian provinces during the period 2020–21. We find that an increase in the infections in a province has a positive and statistically significant effect on neighbours’ infections, which highlights the relevance of spatial spillover effects. This finding is robust to several robustness checks. Furthermore, we investigate cross-provincial transmission heterogeneity using a heterogeneous spatial dynamic panel, which provides novel insights into the diffusion patterns of the disease.

History, neighborhood, and proximity as factors of land-use change: A dynamic spatial regression model

Spatio-temporal land-use change (LUC) modeling provides vital information about land development dynamics. However, accounting for such dynamics faces methodological challenges. This research introduces a Dynamic Spatial Panel Data (DSPD) modeling framework for LUC, incorporating spatial and temporal dependencies. A continuous response variable is introduced to take advantage of traditional spatial regression models. The DSPD model is applied to balanced spatial panel data at the block-group level covering Florida between 2010 and 2019 and incorporating both new and previously used proxy variables. The urban growth impacts of site-specific, proximity, neighborhood, socio-economic, and transportation factors are investigated. This study contributes to the literature by providing extensive insights into spatial autocorrelation, spillover, heterogeneity, and temporal lag effects in urban growth. Also, the study reveals the importance of mobility and mortgage financing in land development. The proposed modeling framework achieves high accuracy. The dynamic structure of this model provides an opportunity to predict future urban growth without the need for a land development scenario. Such predictions provide insights about future land development to practitioners and policymakers.

Spatio-Temporal Analysis of Game Harvests in Sweden

The benefits and costs of wildlife are contingent on the spatial overlap of animal populations with economic and recreational human activities. By using a production function approach with dynamic spatial panel data models, we analyze the effects of human hunting and carnivore predation pressure on the value of ungulate game harvests. The results show evidence of dynamic spatial dependence in the harvests of roe deer and wild boar, but not in those of moose, which is likely explained by the presence of harvesting quotas for the latter. Results suggest the impact of lynx on roe deer harvesting values is reduced by 75% when spatial effects are taken into account. The spatial analysis confirms that policymakers’ aim to reduce wild boar populations through increased hunting has been successful, an effect that was only visible when considering spatial effects.

Identifying latent group structures in spatial dynamic panels

This paper considers the identification of latent group structures in spatial dynamic panels. We follow Lee and Yu (2010) and consider a rich spatial dynamic panel data (SDPD) model with two-way fixed effects. In addition, we also allow for latent panel structures where individuals can be classified into a few groups such that individuals within the same group share the common slope parameters and do not otherwise. Both the number of groups and the individuals’ group membership are unknown. To identify the latent group structures, we first adopt the GMM to obtain the preliminary unconstrained estimates of the slope coefficients. Then we apply the sequential binary segmentation algorithm (SBSA) of Wang and Su (2021) to these estimates and obtain the clusters. A BIC-type information criterion is proposed to choose the number of latent groups consistently. The asymptotic analysis shows that this method can identify the true group structure consistently, and the post-classification estimators enjoy the oracle property. Monte Carlo simulations demonstrate that our method has good finite sample performance. Finally, we apply our approach to the US housing market and identify two latent Metropolitan Statistical Areas groups.

Spatial Dynamic Panel Data Models with High-Order Time-Varying Endogenous Weights Matrices

Spatial Dynamic Panel Data Models with High-Order Time-Varying Endogenous Weights Matrices

Spatial dynamic panel models with missing data

Missing data are a common problem that researchers face in practice. In this article, we focus on the missing response problem for a spatial dynamic panel data (SDPD) model, which allows for both spatial and temporal dependencies. A logistic regression with a set of prespecified covariates is used to model the missingness mechanism, which is assumed to be missing at random (MAR). A weighted maximum likelihood estimator (WMLE) is proposed for parameter estimation in the presence of incomplete data. The associated asymptotic properties are investigated. Thereafter, we develop a novel imputation method, which makes use of the information from spatial dependence, temporal dependence and exogenous regression covariates. Lastly, the performance of WMLE and the proposed imputation method are demonstrated by both simulation studies and a real data example.

Cities in a pandemic: Evidence from China.

This paper studies the impact of urban density, city government efficiency, and medical resources on COVID-19 infection and death outcomes in China. We adopt a simultaneous spatial dynamic panel data model to account for (i) the simultaneity of infection and death outcomes, (ii) the spatial pattern of the transmission, (iii) the intertemporal dynamics of the disease, and (iv) the unobserved city-specific and time-specific effects. We find that, while population density increases the level of infections, government efficiency significantly mitigates the negative impact of urban density. We also find that the availability of medical resources improves public health outcomes conditional on lagged infections. Moreover, there exists significant heterogeneity at different phases of the epidemiological cycle.

Estimating dynamic spatial panel data models with endogenous regressors using synthetic instruments

The paper applies synthetic instruments, initially developed for cross-sectional regression, to estimate dynamic spatial panel data models. These have two main advantages. First, instruments correlated with endogenous variables and yet independent of the errors are difficult to find. Not only are synthetic instruments normally exogenous, but they are usually strongly correlated with endogenous variables, and thus help to avoid the problem of weak instruments. Secondly, they help to reduce instrumental variables proliferation, which is a common result of standard methods of avoiding endogeneity bias. As demonstrated by Monte Carlo simulation, instrument proliferation causes bias in the Sargan–Hansen J test statistic, which is an important indicator of instrument validity and hence estimation consistency. It is also associated with a downward bias in parameter standard error estimates. The paper shows the results of applying synthetic instruments across a variety of different specifications and data generating processes, and it illustrates the method with real data leading to more reliable inference of causal impacts on the level of employment across London districts.

Spatial and temporal correlations of crime in Detroit: Evidence from spatial dynamic panel data models

Since the infamous riot of 1967, high crime rates and negative media reports have labeled the city of Detroit as one of the most dangerous cities in the United States. Using a Spatial Dynamic Panel Data model with both individual and time fixed effects to capture the unobserved heterogeneity as well as the time varying common factors, we investigate the spatial and temporal interactions of criminal activities among the block groups in Detroit. The results indicate that the crime incidents in a block is correlated with the average crime incidents in neighboring block groups contemporaneously, with an estimated coefficient of 0.4758, and the block crime incidents is also correlated with the average crime incidents in neighboring blocks from the previous year, with an estimated coefficient of 0.1572. And crime incidents in a block are positively correlated with its own crime incidents in the previous year. The findings are robust against different model specifications based on alternative spatial weights matrices. The results for both violent crimes and property crimes also suggest strong spatial and temporal correlations among neighboring blocks, providing suggestive and preliminary evidence for policy implementation.