A Review of Panel Data on Spatial Econometrics Models

Astuti, Alfira Mulya; Setiawan, Setiawan; Zain, Ismaini; Purnomo, Jerry Dwi Trijoyo

doi:10.1088/1742-6596/1490/1/012032

Cited by 10 publications

(12 citation statements)

References 38 publications

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“…Regarding the spatial effects, an overview of the panel data spatial econometric models can be found in Astuti et al (2020). Briefly, the Spatial Autoregressive Model (SAR) specifies the existence of spatial effects in the dependent variable, while the Spatial Error Model (SEM) specifies their existence in the error term.…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, the Spatial Durbin Model (SDM) allows identifying spatial effects in the dependent variable as well as in the regressors. Thus, to ascertain the role of geographical location or spatial proximity throughout the whole production technology (i.e., output and inputs), the SDM (which nests both the SAR and SEM models) is the appropriate model (Astuti et al 2020;Haini 2020), with…”

Section: Methodsmentioning

confidence: 99%

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Transient and Persistent Efficiency and Spatial Spillovers: Evidence from the Portuguese Wine Industry

et al. 2021

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This paper investigates the presence of spatial spillovers in firms’ productive (in)efficiency. For this purpose, a spatial stochastic frontier model is specified and estimated, accounting for spatial dependence and persistent and transient (in)efficiency. This approach is applied to a panel dataset from 2014 to 2019 of Portuguese wineries. Apart from the traditional input and output quantities used in the estimation of a production function, the novelty of this study is the inclusion of information on the firms’ exact location, which allows incorporating the neighboring dependence in the productive efficiency analysis. Empirical findings show that despite the Portuguese wineries’ technological positive dependence on spatial closeness for both inputs and outputs, the geographic closeness is not strong enough to provide overall productive efficiency gains.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Transient and Persistent Efficiency and Spatial Spillovers: Evidence from the Portuguese Wine Industry

et al. 2021

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“…resulted from statistical observations of the variables characteristic of a group of n regions periodically, for a defined time interval, T [31].…”

Section: Methodsmentioning

confidence: 99%

Factors Influencing Physicians Migration—A Case Study from Romania

et al. 2022

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Brain drain is a phenomenon that, over time, has followed an upward trend. It is an important component represented by physicians’ migration. For the country of destination, the migration of physicians offers several advantages, whereas the country of origin loses skilled and sometimes highly trained individuals. This process will be reflected both in the efficiency of the health system (severe employment shortage) and in the quality of the health system services. After Romania’s accession to the EU, the migration of doctors intensified, significantly increasing the shortage of physicians. The purpose of this article is to identify the push factors that influence the physicians’ decision to migrate from Romania. For this, a panel regression analysis was applied, highlighting that physicians’ migration is influenced by several factors, such as the number of beds in hospitals, the number of emigrants, unemployment rate, and income. At the same time, we analyzed the extent to which public policy measures addressed to the remuneration of medical staff influenced the propensity towards external mobility of the practicing doctors, already employed and/or graduates. The results confirm that public policies can be a tool for redistributing the labor force allocation on the labor market. Moreover, the results of our analysis highlight that specific measures do not solve the system crises facing the health sector. Systemic, multidimensional changes are needed, adapted to the needs of medical services specific to the geographical area and adequate to the health status of the population.

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“…This cohort study used state-level data on monthly aggregate cigarette sales volumes released for Massachusetts and its bordering states to obtain unbiased and consistent estimates. 3 The study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. Because the study did not directly involve human participants, it did not require institutional review board approval or informed consent in accordance with the Common Rule.…”

Section: Methodsmentioning

confidence: 99%

“…We used a spatial lag regression model specification (eMethods in the Supplement) to compare temporal changes in cigarette sales in Massachusetts and its bordering states before (January 1, 2017, to May 31, 2020) and after (June 1, 2020, to June 30, 2021) the comprehensive flavor ban with changes in 40 states and the District of Columbia (DC) that did not implement local or statewide menthol flavor bans. The model allowed for spatial dependence in cigarette sales between Massachusetts and its bordering states to obtain unbiased and consistent estimates . The study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.…”

Section: Methodsmentioning

confidence: 99%