Some recent developments in spatial panel data models

Lee, Lung‐fei; Yu, Jihai

doi:10.1016/j.regsciurbeco.2009.09.002

Cited by 344 publications

(193 citation statements)

References 47 publications

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“…This study extends the SDM through a dynamic panel approach specifying a SDM-based spatiotemporal model. This is also an extension from the SAR-based spatiotemporal model proposed by Lee and Yu (2010) and Elhorst et al (2013) by incorporating spatial lags of explanatory variables. The motivations are threefold.…”

Section: Spatiotemporal Autoregressive Modelmentioning

confidence: 96%

Tourism and regional income inequality: Evidence from China

Chen

et al. 2016

Annals of Tourism Research

182

120

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Section: Spatiotemporal Autoregressive Modelmentioning

confidence: 96%

Tourism and regional income inequality: Evidence from China

Chen

et al. 2016

Annals of Tourism Research

182

120

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“…In the same way, Elhorst (2012) examines a collection of spatial dynamic panel data (SDPD) models that include one or more of the following variables and/or error terms: "a dependent variable lagged in time, a dependent variable lagged in space, a dependent variable lagged in both space and time, independent variables lagged in time, independent variables lagged in space, serial error autocorrelation, spatial error autocorrelation, spatial-specific and time-period-specific effects." Lee and Yu (2010) examine recent developments in spatial panel data models for both static and dynamic cases that consider fixed effects, spatial lags and spatial disturbance specifications [for other surveys, see also Elhorst (2010Elhorst ( , 2012]. Specifically, these spatial dynamic panel data models can be applied to investigate the economic growth and convergence processes of regions that employ income per capita growth rates versus lagged levels of the explanatory variables.…”

Section: Spatial Panel Data Modelsmentioning

confidence: 99%

Evaluating multiple spatial dimensions of economic growth in Brazil using spatial panel data models

et al. 2015

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The goal of this paper is to evaluate the results of regional economic growth model estimations at multiple spatial scales using spatial panel data models. The spatial scales examined are minimum comparable areas, microregions, mesoregions and states between 1970 and 2000. Alternative spatial panel data models with fixed effects were systematically estimated across those spatial scales to demonstrate that the estimated coefficients change with the scale level. The results show that the conclusions obtained from growth regressions depend on the choice of spatial scale. First, the values of spatial spillover coefficients vary according to the spatial scale under analysis. In general, such coefficients are statistically significant at the MCA, microregional and mesoregional levels, however, at state level those coefficients are no longer statistically significant, suggesting that spatial spillovers are bounded in space. Moreover, the positive average-years-of-schooling direct effect coefficient increases as more aggregate spatial scales are used. Population density coefficients show that higher populated areas are harmful to economic growth, indicating that congestion effects are operating in all spatial scales, but their magnitudes vary across geographic scales. Finally, the club convergence hypothesis cannot be rejected suggesting that there are differences in the convergence processes between the north and south in Brazil. Furthermore, the paper discusses the potential theoretical reasons for different results found across estimations at different spatial scales. JEL Classification C23 · O18 · R11 MotivationThe goal of this paper was to evaluate the results of regional economic growth estimates at multiple spatial scales using alternative spatio-temporal models recently proposed in the spatial econometrics literature. During the last two decades, an increasing dissemination of spatial econometrics techniques has been observed among regional scientists, economists, and researchers in several fields (Anselin 1988;Lesage 1999;Conley 1999). The vast research of applied spatial econometrics on the interdependencies among spatial units and their effects on, among others, regional economic growth, trade flows, knowledge spillovers, migration, housing prices, tax interactions, and city's growth controls 1 is well known. However, this literature still lacks a better understanding of the potential reasons why models estimated at different geographic scales yield different results in the context of regional economic growth empirics. 2 Resende (2011) engages in an initial discussion on the determinants of Brazil's regional economic growth at a variety of geographic scales using a cross-sectional data set over the 1990s period. Resende (2013) improves this analysis by using standard panel data models across several spatial scales, but the process of economic growth in Brazil is only examined using non-spatial panel data models. This investigation refers back to the modifiable areal unit problem (MAUP), 3 but it s...

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“…, m N ) T is a vector with spatial fixed effects; and α t is the coefficient of a time-period fixed effect, one for every year (except one to avoid perfect multicollinearity); while ι N is an N × 1 vector of ones. The control for time-specific effects is crucial since most variables tend to increase and decrease together in different regions over time; if not accounted for, h might be overestimated (Lee & Yu, 2010).…”

Section: Basic Modelmentioning

confidence: 99%

Regional labour force participation across the European Union: a time–space recursive modelling approach with endogenous regressors

Vega

Elhorst

2016

Spatial Economic Analysis

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Regional labour force participation across the European Union: a time-space recursive modelling approach with endogenous regressors. Spatial Economic Analysis. Although there is an abundant regional labour market literature taking a spatial perspective, only a few studies have explored extending the analysis of labour force participation with spatial effects. This paper revisits this important issue, proposing a timespace recursive modelling approach that builds on and appraises Fogli and Veldkamp's methodology from 2011 and finding for the United States that participation rates vary with past values in nearby regions. Major shortcomings in their study are corrected for, including stationarity and the control for endogenous regressors other than the time and space-time-lagged dependent variable using system generalized method of moments (GMM). The paper also highlights interaction effects among explanatory variables for the first time in this context. Using a panel of 108 regions across the European Union over 1986-2010, the results for total, male and female participation rates throw a new light on the socio-economic relevance of different determinants. Importantly, characteristics in neighbouring regions play a significant role, and neglecting endogeneity is found to have serious consequences, underlining increased attention on the specification and estimation of spatial econometric models with endogenous regressors. MOTS CLÉS participation de la population active régions de l'Union européenne, panneaux spatiaux dynamiques, régresseurs endogènes RESUMEN Participación regional de la población activa en la Unión Europea: un enfoque de modelo recursivo tiempoespacio con regresores endógenos. Spatial Economic Analysis. Aunque existen abundantes publicaciones sobre el mercado laboral regional con una perspectiva espacial, solo en unos pocos estudios se ha intentado ampliar con efectos espaciales el análisis de la participación de la población activa. En este artículo revisamos esta cuestión importante proponiendo un enfoque de modelo recursivo tiempo-espacio que valora y se basa en la metodología de Fogli y Veldkamp de 2011, donde se observó que en los Estados Unidos los niveles de participación varían con valores previos en regiones cercanas. Corregimos importantes deficiencias en su estudio, entre ellas la estacionalidad y el control de regresores endógenos que no sean la variable que depende del tiempo y el espacio-tiempo retrasado mediante el método generalizado de momentos (GMM). También se destacan los efectos de interacción entre las variables explicativas por primera vez en este contexto. A partir de un panel de 108 regiones de toda la Unión Europea para el periodo entre 1986 y 2010, los resultados para el nivel de participación total, de mujeres y de hombres, aportan un enfoque nuevo sobre la relevancia socioeconómica de los diferentes determinantes. Pero lo más importante es que las características en las regiones vecinas desempeñan un importante papel y observamos que ignorar la endogeneidad tie...

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Some recent developments in spatial panel data models

Cited by 344 publications

References 47 publications

Tourism and regional income inequality: Evidence from China

Tourism and regional income inequality: Evidence from China

Evaluating multiple spatial dimensions of economic growth in Brazil using spatial panel data models

Regional labour force participation across the European Union: a time–space recursive modelling approach with endogenous regressors

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