<b>spsur</b>: An <i>R</i> Package for Dealing with Spatial Seemingly Unrelated Regression Models

Mı́nguez, Román; López, Fernando A.; Mur, Jesús

doi:10.18637/jss.v104.i11

Cited by 4 publications

(3 citation statements)

References 50 publications

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“…The estimation functions from spdep have been moved to the package spatialreg [6]. Other spatial econometric methods for cross-sectional data have been implemented in other packages: sphet [31] for estimating and testing spatial models with heteroskedastic innovations, spfilteR [20] for filtering out spatial dependence in linear models, spgwr [5] for estimating geographycally weighted regression models, and spsur [28] for estimating seemingly unrelated regression equations. Moreover, other R packages for spatial econometric analysis have been developed following several theoretical contributions to the literature on estimating static and dynamic spatial panel data models [see 17, for a review].…”

Section: Computing Marginal Impact Functionsmentioning

confidence: 99%

An R package for flexible modeling of spatio-temporal data

Mínguez,

Basile,

Durbán

2024

Preprint

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This article introduces the R package pspatreg, publicly available for download from the Comprehensive R Archive Network, for estimating semiparametric spatio-temporal econometric penalized spline (P-Spline) models. These models may include a nonparametric spatio-temporal trend, a spatial lag of the dependent variable, independent variables, or the noise, and a time series autoregressive noise. The essential functions in this package cover the estimation of P-Spline spatio-temporal econometric models using either the Restricted Maximum Likelihood (REML) method or the Maximum Likelihood (ML) method, as well as the computation of marginal impacts for both parametric and nonparametric terms. Moreover, this package provides methods for the graphical display of estimated nonlinear functions and spatial or spatio-temporal trend maps. Applications to cross-sectional and panel spatial data are used as examples of these functions’ usage. JEL Classification: C14 , C21 , C23 MSC Classification: 35A01 , 62G08

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Section: Computing Marginal Impact Functionsmentioning

confidence: 99%

An R package for flexible modeling of spatio-temporal data

Mínguez,

Basile,

Durbán

2024

Preprint

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“…16 To specify the SUR-SEM model, we constructed a spatial weighting matrix based on the queen criterion, which defines neighboring subdistricts based on common edges and vertices. The estimation is implemented with the spsur package in R (Mínguez et al, 2022) at subdistrict level, that is, all variables are collapsed by subdistrict and survey year.…”

Section: Appendix B: Robustness Checksmentioning

confidence: 99%

Extreme weather and agricultural management decisions among smallholder farmers in rural Thailand and Vietnam

Liebenehm,

Jaretzky,

Waibel

2023

Agricultural Economics

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In this article, we explore whether and to what extent smallholder farmers in Northeastern Thailand and Central Vietnam adjust their farm‐level management strategies in response to droughts. We hereby consider adjustments in flexible adaptive strategies including water management, fertilizer and pesticide application, labor, and machine use in response to a contemporaneous drought, and adjustments in crop diversification and investments in response to a previous year drought. To that end, we combine longitudinal household data from the two regions from 2007 to 2017 with monthly high‐resolution rainfall and temperature data to characterize droughts at the subdistrict level. We find that Thai farmers scale down input costs in terms of fertilizer and hired labor and outsource tasks to service providers with equipment such as a combine, especially when exposed to extreme droughts. Their diversification and investment response seems, however, muted. While Vietnamese farmers are also reducing fertilizer use, they are expanding both the number of hired laborers and rented machinery services. They are also diversifying their cropping portfolio and investing in agricultural equipment.

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“…As is the case with cross-sectional data, it is possible to test the residuals of Model(1) for spatial autocorrelation, and several tests have been developed to test the null hypothesis of spatial independence (López, Mur, and Angulo 2014). When the null hypothesis is rejected, several alternative specifications have been proposed to include spatial effects(Anselin., 1988).…”

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confidence: 99%

Estimating Climate Influence Of The Potential Covid-19 Pandemic Spreading In Algeria

Fadel

Rouaski

Zakane

et al. 2022

SEC

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This document aims to investigate the potential influence of climate on the spread of the COVID-19 pandemic, the direct and indirect effects of climate are felt all over the planet, although their magnitude and manifestations vary. According to estimates by the World Health Organization (WHO), climate change could be the cause of nearly 250,000 additional deaths per year worldwide between 2030 and 2050 (Word Health Organization, 2021). This study focused on examining the relationship between climate (Temperature, humidity, and wind speed) and hospitalizations due to COVID-19 in a well-selected sample of wilayas in Algeria. In this brief, we want to shed light on the likely course and geographic spread of the epidemic. The purpose of this article is to answer the main question of the study: We do this by examining the effect of climate (temperature, humidity, and wind speed) on hospitalizations due to COVID-19 in the wilayas of Algiers, Blida, Oran, Adrar, Setif and Tamanrasset. The choice of wilayas is based on the availability, quality, and consistency of the data required. Our analysis suggests that high temperature and humidity or high relative wind speed tend to hamper the spread of the virus and that a high population density tends to facilitate its transmission. This does not mean that higher temperatures are enough to contain the disease. The climate potentially plays a role in the spread of many respiratory viruses. It appeared important to know if this could also be the case for the new coronavirus, COVID-19. While the role of climate in the transmission is still difficult to quantify, it is clear that other factors are taken into account in the transmission of COVID-19, namely mainly compliance with the rules of physical distancing and barrier gestures. This study focused more particularly on the effects of absolute climate (Temperature, humidity, and wind speed). 90% of infections would have occurred in areas where the temperature is between 3 and 17 degrees and the absolute humidity is between 4 and 9 g / m3, 35 to 85% relative humidity (Bukhari Q., Jameel Y., 2020). We address the issue of the impact of climate on the spread of COVID-19, we use the SUR (Seemingly Unrelated Regression) model to estimate the relationship between climate and COVID-19 cases in Algeria during the period between April 18th, 2020, and April 17th, 2021 inclusive. The results of the SUR model estimate, also showed that there is no real climate that can damage the pandemic situation in Algeria during the period studied.

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spsur: An R Package for Dealing with Spatial Seemingly Unrelated Regression Models

Cited by 4 publications

References 50 publications

An R package for flexible modeling of spatio-temporal data

An R package for flexible modeling of spatio-temporal data

Extreme weather and agricultural management decisions among smallholder farmers in rural Thailand and Vietnam

Estimating Climate Influence Of The Potential Covid-19 Pandemic Spreading In Algeria

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