Seemingly unrelated regression tree

Kim, Jaeoh; Cho, HyungJun

doi:10.1080/02664763.2018.1538327

Cited by 11 publications

(8 citation statements)

References 32 publications

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“…For these M equations, there is no relationship between them except that their disturbances are correlated. Based on the previous studies (Felmlee & Hargens, 1988; Kim & Cho, 2019; Kmenta, 1971; Zellner, 1962, 1963), the general specification of SUR simultaneous equation systems can be represented as follows: …”

Section: Theoretical Frameworkmentioning

confidence: 99%

The Interactional Relationships Between Credit Risk, Liquidity Risk and Bank Profitability in MENA Region

Hakimi

Boussaada

Hamdi

2020

Global Business Review

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The purpose of this article is to investigate the relationship between credit risk, liquidity risks and bank profitability within the Middle East and North African (MENA) countries. We selected data related to a sample of conventional banks observed during the period 2004–2015 and we performed the Seemingly Unrelated Regression (SUR) method in the empirical section. The overall results suggest that profitability of MENA banks is negatively and significantly sensitive to an increase in credit and/or liquidity risks. This negative effect was confirmed for either the separate or the interaction effects of these two risks. Furthermore, the findings indicate that bank profitability decreases significantly the level of credit and liquidity risks. We also found that the law and order as institutional quality increases the profitability of MENA banks and decreases both credit and liquidity risks.

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Section: Theoretical Frameworkmentioning

confidence: 99%

The Interactional Relationships Between Credit Risk, Liquidity Risk and Bank Profitability in MENA Region

Hakimi

Boussaada

Hamdi

2020

Global Business Review

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“…External Debt, Investment, and Economic Growth: a Seemingly Unrelated Regression Model for Low-Income Countries 727 such as Kmenta (1971), Felmlee and Hargens (1988), and Kim and Cho (2019).…”

Section: Gambiamentioning

confidence: 99%

“…Based on previous studies (Zellner 1962, Kmenta 1971, Felmlee and Hargens 1988, Kim and Cho 2019, the general specification of SUR simultaneous equation systems can be represented as follows:…”

Section: B Empirical Approach Model Specification and Variables Dementioning

confidence: 99%

External Debt, Investment, and Economic Growth: A Seemingly Unrelated Regression Model for Low-Income Countries

Hakimi

Boussaada

Karmani

2019

Journal of Economic Integration

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This study analyzes whether external debt is a driving factor for investment and economic growth in low-income countries. Using data over the period 2000~2017, we performed an analysis using the 23 countries in the sample and a split-sample analysis wherein we separated less indebted countries (12) from more indebted countries (11). Empirical results of the seemingly unrelated regressions model indicate that external debt significantly decreases investment and economic growth for both the total sample and the sub-samples. In addition, we found that trade openness is positively and significantly related to the level of growth per capita. This positive association is confirmed for both the total sample and the split sample. Findings also indicate that the level of growth exerts a positive and significant effect on investment for the total sample and for less indebted countries.

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“…Unlike others, SUR model is SEM; therefore, this study uses it. This model was developed by Zellner (1962) and later adopted by Kmenta (1971), Felmlee and Hargens (1998), Kim and Cho (2019) and Abdelaziz et al (2019).…”

Section: Methodology Of the Studymentioning

confidence: 99%

The impact of external debt on total factor productivity and growth in HIPCs: non-linear regression approaches

Beyene

Kotosz

2021

IJDI

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Purpose The purpose of this study is to provide an empirical analysis of the impact of external debt on total factor productivity (TFP) and growth along with the TFP channel through which external debt affects the growth of heavily indebted poor countries (HIPCs). Design/methodology/approach This study uses panel data econometrics; basically, the seemingly unrelated regression (SUR) and alternative non-linear (panel threshold) models. For robustness check, it also uses panel-corrected standard errors, feasible generalized least squares and SUR (using alternative variables). Findings External debt significantly reduces both TFP and growth. Besides, it confirms that the relationship between external debt and TFP and gross domestic product growth is non-linear. Further external debt can affect the growth of HIPCs through the TFP channel. However, the threshold model result reveals weak evidence of threshold values although there are some threshold values of 67 and 54 for TFP and growth models, respectively. Originality/value To the best of the authors’ knowledge, this is the first study on most concerned countries (HIPCs) that shows a detailed and complete analysis of the TFP channel and the impact of external debt on growth. Thus, it provides appropriate and sound policies that consider the unique characteristics of the countries. Unlike most previous findings, this study does not support an inverted U-shape relationship between external debt and growth. Further, it provides insights into the relationships among TFP, external debt and growth. Moreover, it considers basic panel econometric tests like cross-sectional dependence, uses a non-linear simultaneous equations model along with the alternative non-linear model and is supported by different robustness checks.

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Seemingly unrelated regression tree

Cited by 11 publications

References 32 publications

The Interactional Relationships Between Credit Risk, Liquidity Risk and Bank Profitability in MENA Region

The Interactional Relationships Between Credit Risk, Liquidity Risk and Bank Profitability in MENA Region

External Debt, Investment, and Economic Growth: A Seemingly Unrelated Regression Model for Low-Income Countries

The impact of external debt on total factor productivity and growth in HIPCs: non-linear regression approaches

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