Institutions Versus Geography: Subnational Evidence from the United States

Clifton, Eric V.; Romero-Barrutieta, Alma

doi:10.2139/ssrn.926224

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…This work is empirically based not only on the specifications based on the determinants of export diversification developed by (Imbs and Wacziarg, 2003;Agosin et al, 2012) but also on the specifications linking governance, geography and export diversification developed by Acemoglu et al (2001), Easterly and Levine (2002) and used by Andrea and Presbitero (2006) and Clifton and Romero-Barrutieta (2006). The model specification is as follows:…”

Section: Ii1 Model Specificationmentioning

confidence: 99%

Export diversification in Africa's landlocked countries: governance vs geography

Stephane,

NKOA

2024

Preprint

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The objective of this paper is to analyse the effects of governance and/or geography on export diversification in Africa's landlocked countries (ALDCs). To do so, we used panel data over the period 1996–2021. The Driscoll-Kraay (1998) method is used for the estimations. The results show that for governance, only the political stability variable has a significant effect on export diversification in the presence of the geography variables. However, with regard to the geographical variables, proximity to the large international market and population size have positive and significant effects on export diversification, whereas the remoteness of access to the sea variable and the natural resource rents variable have negative and significant effects on export diversification in the ALDCs in the presence of the governance variables. We can conclude that in the ALDCs, geography prevails over governance in line with the institutions versus geography debate, as it is the geographical variables that best explain export diversification compared to the governance variables.

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Section: Ii1 Model Specificationmentioning

confidence: 99%

Export diversification in Africa's landlocked countries: governance vs geography

Stephane,

NKOA

2024

Preprint

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“…LeSage (2004), for instance, suggested a non-constant variance Bayesian GWR that regularizes (or down-weights) outliers using priors. Econometric analysis (Ma et al, 2020), regional development analysis (Clifton and Romero-Barrutieta, 2006), and forest analysis (Subedi et al, 2018) have all used the Bayesian GWR. Other robust estimation methods have also been applied, which give outliers less weight.…”

Section: Introductionmentioning

confidence: 99%

Applied fixed effect of Geographically Weighted Panel Regression (GWPR) with M- Estimator approach to estimate sugarcane yield data in East Java

Mondiana,

Pramoedyo,

Iriany

et al. 2024

JANS

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Geographically Weighted Panel Regression (GWPR), a combination of panel regression and geographically weighted regression (GWR), is used to analyze panel data and capture diverse relationship between locations. GWPR was developed on data with panel-fixed effects and applied to modeling data with spatial heterogeneity and time series. One method for estimating parameters in the GWPR is weighted least squares (WLS), which are sensitive to outliers. The present study aimed to use the M- method to estimate GWPR model parameters in data containing outliers using fixed-effect GWPR modeling for the sugar cane yield in East Java of Indonesia from 2019 to 2021. Sugarcane yield data in East Java contained outliers in several areas, including Malang, Blitar, and Ngawi Districts. Because the data contains outliers, a robust method with the M estimator was applied. The results showed that plantation areas significantly affected production in all districts.The R2 of the model was 0.87, showing that GWPR model with M estimation was appropriate in predicting sugarcane yield. Based on the Akaike Information Criterion (AIC) value, the GWPR model with M estimation had better performance than GWPR model alone.

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“…For instance, LeSage (2004) proposed a Bayesian GWR with non-constant variance that regularizes (or down-weights) outliers through priors. The Bayesian GWR has been applied to econometric analysis (Ma et al, 2020), regional development analysis (Clifton and Romero-Barrutieta, 2006), and forest analysis (Subedi et al, 2018). Other robust estimation, which assigns less weights for outliers, has also been applied.…”

Section: Introductionmentioning

confidence: 99%

Adaptively Robust Geographically Weighted Regression

Murakami¹

2021

Preprint

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We develop a new robust geographically weighted regression method in the presence of outliers. We embed the standard geographically weighted regression in robust objective function based on γ-divergence. A novel feature of the proposed approach is that two tuning parameters that control robustness and spatial smoothness are automatically tuned in a data-dependent manner. Further, the proposed method can produce robust standard error estimates of the robust estimator and give us a reasonable quantity for local outlier detection. We demonstrate that the proposed method is superior to the existing robust version of geographically weighted regression through simulation and data analysis.

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Institutions Versus Geography: Subnational Evidence from the United States

Cited by 3 publications

References 5 publications

Export diversification in Africa's landlocked countries: governance vs geography

Export diversification in Africa's landlocked countries: governance vs geography

Applied fixed effect of Geographically Weighted Panel Regression (GWPR) with M- Estimator approach to estimate sugarcane yield data in East Java

Adaptively Robust Geographically Weighted Regression

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