Fixed-effects dynamic spatial panel data models and impulse response analysis

Li, Kunpeng

doi:10.1016/j.jeconom.2017.02.001

Cited by 24 publications

(13 citation statements)

References 34 publications

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“…Existing studies have focused on the spatial spillover effects of public infrastructure on regional productivity, of fiscal investment on public provision [22,23], and of health investment on regional healthcare costs [24]. Spatial spillover analysis techniques have included the use of cross-sectional and spatial panel data [25,26,27], and static and dynamic models [28,29,30,31]. New approaches have been implemented following empirical studies of public services.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Distribution, Spillover Effects and Influences of China’s Two Levels of Public Healthcare Resources

Song

Wei

Deng

et al. 2019

IJERPH

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In China, upper-level healthcare (ULHC) and lower-level healthcare (LLHC) provide different public medical and health services. Only when these two levels of healthcare resources are distributed equally and synergistically can the public’s demands for healthcare be met fairly. Despite a number of previous studies having analysed the spatial distribution of healthcare and its determinants, few have evaluated the differences in spatial equity between ULHC and LLHC and investigated their institutional, geographical and socioeconomic influences and spillover effects. This study aims to bridge this gap by analysing panel data on the two levels of healthcare resources in 31 Chinese provinces covering the period 2003–2015 using Moran’s I models and dynamic spatial Durbin panel models (DSDMs). The results indicate that, over the study period, although both levels of healthcare resources improved considerably in all regions, spatial disparities were large. The spatio-temporal characteristics of ULHC and LLHC differed, although both levels were relatively low to the north-west of the Hu Huanyong Line. DSDM analysis revealed direct and indirect effects at both short-and long-term scales for both levels of healthcare resources. Meanwhile, the influencing factors had different impacts on the different levels of healthcare resources. In general, long-term effects were greater for ULHC and short-term effects were greater for LLHC. The spillover effects of ULHC were more significant than those of LLHC. More specifically, industrial structure, traffic accessibility, government expenditure and family healthcare expenditure were the main determinants of ULHC, while industrial structure, urbanisation, topography, traffic accessibility, government expenditure and family healthcare expenditure were the main determinants of LLHC. These findings have important implications for policymakers seeking to optimize the availability of the two levels of healthcare resources.

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Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Distribution, Spillover Effects and Influences of China’s Two Levels of Public Healthcare Resources

Song

Wei

Deng

et al. 2019

IJERPH

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“…Now if we combine the models considered in Anselin et al (1996) and Bera et al (2001), we have the spatial panel data model, potentially with four sources of departures (from the classical regression model) coming from the possible presence of the spatial lag, spatial error, random effect and (time series) serial correlation. The spatial panel data models have been studied extensively in terms of estimation issues, and have gained much popularity over time given the wide availability of the longitudinal data [see, for instance, Aquaro et al (2019), Baltagi et al (2014), Elhorst et al (2014), Kapoor et al (2007), Lee and Yu (2010), LeSage (2014), Li (2017), Olivier and LeSage (2011), Olivier and LeSage (2012), Hashem and Elisa (2011), Zhenlin (2018) and Yu et al (2008)]. Many researchers have conducted conditional and marginal specification tests in spatial panel data models.…”

Section: A Brief Survey Of the Literaturementioning

confidence: 99%

Specification tests for spatial panel data models

Bera

Doğan

Taşpınar

et al. 2020

J Spat Econometrics

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Specification of a model is one of the most fundamental problems in econometrics. In practice, specification tests are generally carried out in a piecemeal fashion, for example, testing the presence of one-effect at a time ignoring the potential presence of other forms of misspecification. Many of the suggested tests in the literature require estimation of complex models and even then those tests cannot account for multiple forms of departures from the model under the null hypothesis. Using Bera and Yoon (Econom Theory 9(04):649-658, 1993) general test principle and a spatial panel model framework, we first propose an overall test for ''all'' possible misspecification. Then, we derive adjusted Rao's score tests for random effect, serial correlation, spatial lag and spatial error, which can identify the definite cause(s) of rejection of the basic model and thus aiding in the steps for model revision. For empirical researchers, our suggested procedures provide simple strategies for model specification search employing only the ordinary least squares residuals from a standard linear panel regression. Through an extensive simulation study, we evaluate the finite sample performance of our suggested tests and some of the existing procedures. We find that our proposed tests have good finite sample properties both in terms of size and power. Finally, to illustrate the usefulness of our procedures, we provide an empirical application of our test strategy in the context of the convergence theory of incomes of different economies, which is a widely studied empirical problem in macroeconomic growth theory. Our empirical illustration reveals the problems in using and interpreting unadjusted tests, and demonstrates how these problems are rectified in using our proposed adjusted tests. Keywords Rao's score (RS) test Á Robust RS test Á Specification test Á Spatial models Á Spatial panel data models JEL Classification C13 Á C21 Á C31

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“…In recent empirical researches on regional science and economics, spatial factors are receiving widespread attention as the unobservable heterogeneity and mutual effects of one region with its neighbors cannot be ignored. [49][50][51][52][53][54] Focusing on regional carbon emissions, many researches fully considered the influence of spatial dependence and spillover effects. Meng et al 55 decomposed carbon emission growth from a spatial perspective, by applying a spatial structural decomposition analysis method based on the 2007 and 2010 China's inter-regional input-output tables.…”

Section: Literatures On Spatial Effects Of Carbon Emissionsmentioning

confidence: 99%

Convergence of per capita carbon emissions in the Yangtze River Economic Belt, China

Zhang

et al. 2018

Energy & Environment

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The convergence of carbon emissions in important regions is a key prerequisite for reaching China's carbon emissions peak target. The Yangtze River Economic Belt (YREB) is the latest strategic economic area in China. In this study, a method of investigating the convergence of per capita carbon emissions in the YREB is proposed, and it includes the following models: r convergence, stochastic convergence and b convergence. Besides, the five control variables per capita Gross Domestic Product (GDP), energy consumption structure, industrial structure, population density and urbanization rate are considered in the test models. In addition to testing the whole sample of 74 cities belonging to the Economic Belt, the convergence of three subgroups (municipalities, capital and sub-provincial cities and prefecture-level cities) is also tested by considering spatial effects. The following results are obtained: (1) although r convergence and b conditional convergence are observed, stochastic convergence is not observed for the 74 cities; (2) the per capita GDP and energy structure are the key factors influencing the cities' convergence, whereas the urbanization rate has a limited impact; and (3) improving the industrial structure and reducing the population density are effective methods of promoting the convergence of per capita carbon emissions among the capital and subprovincial cities and municipalities, respectively.

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Fixed-effects dynamic spatial panel data models and impulse response analysis

Cited by 24 publications

References 34 publications

Spatio-Temporal Distribution, Spillover Effects and Influences of China’s Two Levels of Public Healthcare Resources

Spatio-Temporal Distribution, Spillover Effects and Influences of China’s Two Levels of Public Healthcare Resources

Specification tests for spatial panel data models

Convergence of per capita carbon emissions in the Yangtze River Economic Belt, China

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