Spatial Autoregressive Panel Data Model Research Articles

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).

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.

The Spatial Autoregressive Panel Data Model with Spatial Moving Average Errors

This paper advocates the wider use of the spatial autoregressive (AR) panel data model with spatial moving average (MA) errors, individual and time effects, and different spatial weight matrices for each spatial lag. We demonstrate the practical relevance of this model, derive and investigate the asymptotic properties of a simple quasi maximum likelihood within estimator when is large and is finite, and provide an empirical example explaining military expenditures in 144 countries over the period 1993–2007.

Shrinkage estimation of semi-parametric spatial autoregressive panel data model with fixed effects

In this paper, we develop a shrinkage estimator for semi-parametric spatial autoregressive panel data model with fixed effects. Its asymptotic and oracle properties are proved and its finite-sample performance is investigated. The method is also verified with one real data.

Adaptive lasso variable selection method for semiparametric spatial autoregressive panel data model with random effects

This paper investigates variable selection in semiparametric spatial autoregressive panel data model with random effects. A penalized profile maximum-likelihood method is proposed with adaptive lasso penalty which achieves parameter estimation and variable selection at the same time. Under some regular conditions, we prove the theoretical properties of the estimators, including consistency and oracle property. In addition, we develop a feasible logarithm and carry out numerical simulations to examine the finite sample performance of this method. At last, a real data study about the investment influencing factors of the “Belt and Road” initiative is presented for illustration purpose.

Proximity and Police Departments: Spatial Dynamics of the Misdemeanor Arrest Decline

What makes police departments change their practices? Do they transform in isolation, or do they mimic their neighbors? Combining insights from organizational theory and urban sociology, the authors argue that organizational change diffuses, in part, through physically proximate institutions. They apply this theory to an underexamined trend: the decline of misdemeanor arrests in the United States between 1990 and 2018. The study first explores the spatial dynamics of low-level law enforcement by graphing and mapping trends across a range of metropolitan types, finding that suburbs made the fewest low-level arrests and central cities reduced their arrests the most during these years. A spatial autoregressive panel data model reveals that police departments decreased their misdemeanor arrests more when nearby departments did so, net of crime rates and other controls, evincing spatial mimicry. Police reform efforts need not target only state and federal governments but can diffuse outward from city-level changes.

Minimum distance quantile regression for spatial autoregressive panel data models with fixed effects.

This paper considers the quantile regression model with individual fixed effects for spatial panel data. Efficient minimum distance quantile regression estimators based on instrumental variable (IV) method are proposed for parameter estimation. The proposed estimator is computational fast compared with the IV-FEQR estimator proposed by Dai et al. (2020). Asymptotic properties of the proposed estimators are also established. Simulations are conducted to study the performance of the proposed method. Finally, we illustrate our methodologies using a cigarettes demand data set.

Semiparametric Spatial Autoregressive Panel Data Model with Fixed Effects and Time-Varying Coefficients

This article considers a semiparametric spatial autoregressive (SAR) panel data model with fixed effects and time-varying coefficients. The time-varying coefficients are allowed to follow unknown functions of time, while the other parameters are assumed to be unknown constants. We propose a local linear quasi-maximum likelihood estimation method to obtain consistent estimators for the SAR coefficient, the variance of the error term, and the nonparametric time-varying coefficients. The asymptotic properties of the proposed estimators are also established. Monte Carlo simulations are conducted to evaluate the finite sample performance of our proposed method. We apply the proposed model to study labor compensation in Chinese cities. The results show significant spatial dependence among cities and the impacts of capital, investment, and the economy’s structure on labor compensation change over time.

Spatial Diffusion of Civil Liberty

This paper studies the existence of spatial diffusion of civil liberty among neighboring countries. For that purpose, we first combine different exploratory space-time data analysis approaches to find that this phenomenon is spatially clustered and that a convergence process is at work among the world countries from 1985 to 2010, with a structural change by the end of the Twentieth century mainly due to the appearance of the Internet. Second, we specify a spatial autoregressive panel data model for a sample of 130 countries, for 1985–2000, and 172 countries, for 2000–2010. Results provide evidence for spatial diffusion of civil liberty, though it is not constant along this time span. The spreading rate is 0.34 in the first sub-period. After 2000, it reduces to 0.21; that is, countries only “catch” 21% of the average changes in their neighbors’ civil liberty levels. Additionally, religious culture, urban agglomeration and GDP explain the levels of civil liberties in the world.

An Evaluation of the Three Pillars of Sustainability in Cities with High Airbnb Presence: A Case Study of the City of Madrid

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.

Effects of Land Urbanization on Smog Pollution in China: Estimation of Spatial Autoregressive Panel Data Models

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.

Measuring and analysing social efficiency in the production of maternal health services in Mexico, 2008-15.

'Social efficiency' (SE) denotes the capacity of health systems to ensure equitable access to quality health services at no financial risk to users. Mexico and other low- and middle-income countries have rarely studied the performance of their health systems from an SE perspective. We propose a metric for assessing SE in the production of maternal health services in the public sector among populations without social security, analysing contextual correlates of the demand for these services. Analysis was based on administrative data collected from the 243 health jurisdictions (HJs) in Mexico for the period 2008-15. We defined production inputs as the availability of physical and human resources and social product as the unweighted sum of social sub-products, including an equitable distribution of maternal health resources, the provision of quality maternal health care and financial protection for users. We described the SE scores, the main contextual characteristics as well as those related to the demand for maternal health services. We then performed a variance decomposition analysis of the SE score by component and estimated the SE territorial concentration patterns. Finally, we identified the structural characteristics modelling SE by means of a spatial autoregressive panel data model with fixed effects by year. The SE score rose from 57.7% in 2008 to 71.9% in 2015 (P < 0.01), with its quality component accounting for the largest proportion of variance (30%). SE peaked in HJs with low social marginalization and rurality, and with service demand characterized by low parity and older populations. Different SE levels demonstrated territorial concentration patterns. Analysing SE as a metric for health system performance offers elements that contribute to the achievement of UHC as well as to the design and implementation of effective maternal health interventions intended particularly for the most socially vulnerable sectors of the population.

Does direction of the transmission of bank risk matter? An application to the Chilean banking sector

This paper studies the transmission of bank risk in the Chilean banking sector by focusing on the direction of the transmission. By using a Spatial Autoregressive Panel data model, our main findings indicate the following. First, banks which maintain a high (low) proportion of interbank credits transmit risk to banks that maintain a low (high) proportion of interbank credits. Second, while small banks transmit risk to banks which are of similar size, large banks transmit risk to the whole banking sector. Finally, banks whose main clients belong to the financial establishments, insurance, real state, and services economic activity transmit risk to the rest of banks.

Time-Varying Coefficient Spatial Autoregressive Panel Data Model with Fixed Effects

This paper develops a time-varying coefficient spatial autoregressive panel data model with the individual fixed effects to capture the nonlinear effects of the regressors, which vary over the time. To effectively estimate the model, we propose a method that incorporates the nonparametric local linear method and the concentrated quasi-maximum likelihood estimation method to obtain consistent estimators for the spatial coefficient and the time-varying coefficient function. The asymptotic properties of these estimators are derived as well, showing the regular sqrt(NT)-rate of convergence for the parametric parameters and the common sqrt(NTh)-rate of convergence for the nonparametric component, respectively. Monte Carlo simulations are conducted to illustrate the finite sample performance of our proposed method. Meanwhile, we apply our method to study the Chinese labor productivity to identify the spatial influences and the time-varying spillover effects among 185 Chinese cities with comparison to the results on a subregion East China.

Cultural Differences and the Consumption Structure in the European Union Countries

The aim of the paper is to study the cultural differentiation in the European Union countries and compare it with the differentiation in their consumption spending. The question is whether similar countries in terms of culture have similar final consumption expenditures and consumption structures. Culture in this research is characterised by six Hofstede dimensions – power distance, individualism vs. collectivism, masculinity vs. femininity, uncertainty avoidance, long term orientation and indulgence vs. restraint. The consumption structure is characterised by share of durable goods, semi‑durable goods, non‑durable goods and services in households’ final consumption. In the analysis, the influence of culture on the share of non‑durable goods and services in final consumption expenditures is considered. Countries’ similarities in these two aspects are evaluated with the use of the cluster analysis approach – the k‑means algorithm and the Ward clustering method. The dependence between the structure of final consumption expenditures and culture is investigated using spatial autoregressive (SAR) and spatial error (SE) panel data models.&#x0D;

A heterogeneous coefficient approach to the knowledge production function

ABSTRACTPast literature has used conventional spatial autoregressive panel data models to relate patent production output to knowledge production inputs. However, research conducted on regional innovation systems points to regional disparities in both regions’ ability to turn their knowledge inputs into innovation and to access external knowledge. Applying a heterogeneous coefficients spatial autoregressive panel model, we estimate region-specific knowledge production functions (KPFs) for 94 NUTS-3 regions in France using a panel covering 21 years from 1988 to 2008 and four high-technology industries. A great deal of regional heterogeneity in the KPF relationship exists across regions, providing new insights regarding spatial spillin and spillout effects between regions.

The Spatial Structure of Farmland Values: A Semiparametric Approach

Controlling for spatial heterogeneity and spatial dependence in farmland valuation models has gained substantial attention in recent literature. This paper proposes to derive the spatial structure of farmland values endogenously and semiparametrically based on the spatial competition theory. The paper assembles panel data of Pennsylvania county level farmland values between 1982 and 2012. A spatial autoregressive panel data model with spatial weights matrix endogenously incorporated is estimated. Out of sample predictions and non-nested statistical tests for model selection suggest that the fit and the predictability of hedonic farmland valuation models can be greatly improved.

Estimation and inference in functional-coefficient spatial autoregressive panel data models with fixed effects

This paper develops an innovative way of estimating a functional-coefficient spatial autoregressive panel data model with unobserved individual effects which can accommodate (multiple) time-invariant regressors with a large number of cross-sectional units and a finite time periods. Our proposed methodology removes unobserved fixed effects from the model by transforming the latter into a semiparametric additive model, however avoids using backfitting technique. We derive the limiting results for the proposed estimators and construct a consistent nonparametric test to test for spatial endogeneity. A small Monte Carlo study shows that our proposed estimators and test statistic exhibit good finite-sample performance.

Estimation and Inference in Functional-Coefficient Spatial Autoregressive Panel Data Models with Fixed Effects

This paper develops an innovative way of estimating a functional-coefficient spatial autoregressive panel data model with unobserved individual effects which can accommodate (multiple) time-invariant regressors in the model with a large number of cross-sectional units and a fixed number of time periods. The methodology we propose removes unobserved fixed effects from the model by transforming the latter into a semiparametric additive model, the estimation of which however does not require the use of backfitting or marginal integration techniques. We derive the consistency and asymptotic normality results for the proposed kernel and sieve estimators. We also construct a consistent nonparametric test to test for spatial endogeneity in the data. A small Monte Carlo study shows that our proposed estimators and the test statistic exhibit good finite-sample performance.

Measurement and Convergence in Development Performance of China’s High-tech Industry

In China’s high-tech industry development, there exist regional scale and regional performance differences, of which the latter will influence resource allocation in regional high-tech industry and upgrading process of industrial structure.1 Hence, the article conducts a systematic research of measurement and convergence analysis in development performance of China’s high-tech industry. The article contains two main research parts. First, in terms of measurement, by adopting the Malmquist index methods, the article measures the comprehensive performance of China’s high-tech industry development. Second, to conduct convergence analysis, it studies agglomeration patterns characteristics, absolute β convergence and its converging mechanism of China’s provincial high-tech industry development performance from 2001 to 2013 by adopting a spatial autoregressive (SAR) panel data model. The results indicate distinct absolute β convergence in China’s high-tech industry development performance is due to technology diffusion. In addition, convergence rates of different regions vary.