Determining the Macroeconomic Factors of External Debt Accumulation in Nigeria: An ARDL Bound Test Approach

Abdullahi, Muhammad Mustapha; Bakar, Nor Aznin Bt Abu; Hassan, Sallahuddin

doi:10.1016/j.sbspro.2015.11.098

Cited by 41 publications

(25 citation statements)

References 9 publications

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“…With a few exceptions, most studies based on panel and cross-sectional data did not use the dynamic estimation techniques like panel ARDL. Similarly, only a few studies used ARDL in single-country cases (such as Abdullahi et al 2015;Adamu -Rasiah 2016;Adane et al 2018;and Brafu-Insaidoo et al 2019). Table 1 also shows that among HIPCs, only Ghana, Ethiopia, and Uganda have been studied.…”

Section: Theoretical Framework and Empirical Literaturementioning

confidence: 99%

Macroeconomic determinants of external indebtedness of Ethiopia: ARDL approach to co-integration

Beyene

Kotosz

2020

Society and Economy

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Although Ethiopia is one of the Heavily Indebted Poor Countries (HIPC), there is a lack of empirical studies about the determinants of its external indebtedness. This paper aims to fill this gap by examining the macroeconomic determinants of the external indebtedness of Ethiopia between 1981 and 2016, using the two- and three-gap models as a theoretical framework and an autoregressive distributed lag bound testing approach. The result shows that in the long run, the savings-investment gap, trade deficit, fiscal deficit, and debt service have a positive and significant impact on external indebtedness. However, the growth rate of gross domestic product, trade openness, and inflation negatively and significantly affect the external indebtedness of the country. These results coincide with the predictions of the two- and three-gap models of the theoretical framework. The study argues that appropriate macroeconomic, social, and supply-side policies are essential to reducing the external indebtedness of Ethiopia.

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Section: Theoretical Framework and Empirical Literaturementioning

confidence: 99%

Macroeconomic determinants of external indebtedness of Ethiopia: ARDL approach to co-integration

Beyene

Kotosz

2020

Society and Economy

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“…Lau et al (2015) found short run causality relationship between the macroeconomic indicators and the external debt and in Malaysia covering the period between 1970 and 2013. Abdullahi et al (2015) examined the macroeconomic factors of external debt accumulation in Nigeria for the 1980 to 2013 period. The empirical results show that interest rate, exchange rate, and budget deficits have negative effect on external debt in the long and short term.…”

Section: Theoretical Framework and Literature Reviewmentioning

confidence: 99%

Factors Affecting External Debt in Transition Economies: The Case of Central Asia and the Caucasus

Sağdıç¹,

Yildiz²

2020

International Journal of Management Economics and Business

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arasında panel veri analizi kullanılarak dış borçları etkileyen temel faktörler incelenmektedir. Panel regresyon bulguları; kamu harcamaları ve borç servisinin dış borçlanmayı pozitif yönde etkilediği ve istatistiksel olarak anlamlı etkiye sahip olduğunu göstermektedir. Tam tersine cari işlemler dengesi, enflasyon oranı ve iç tasarruf oranlarının dış borçlar üzerinde negatif ve anlamlı bir etkiye sahip olduğu görülmektedir. Bu çalışmanın sonuçları, Orta Asya ve Kafkas ülkelerindeki dış borçlanmanın kamu harcamaları, cari işlemler açıkları ve borç servisi ödemelerinde önemli bir finansman kaynağı olarak kullanıldığını göstermektedir.

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“…Because the variables are a mixture of I (0) and I (1) stationarities, in addition to the existence of cointegration, the autoregressive distributed lag model (ARDL model) for panel data is probably the most suitable estimator (Odhiambo, 2009; Bildirici, 2014; Abdullahi et al ., 2015).…”

Section: Model Data and Methodologymentioning

confidence: 99%

“…Furthermore, in our empirical study, we have cointegration among the variables in combination with the mixture of the I (0) and I(1) variables. In this case, the ARDL is regarded as the most appropriate estimator (Odhiambo, 2009; Bildirici, 2014; Abdullahi et al ., 2015). Moreover, the ARDL model allows us to identify short-term and long-term effects by including lags of dependent and independent variables, whether the regressors are endogenous or exogenous (Pesaran and Smith, 1995; Pesaran and Shin, 1998).…”

Section: Model Data and Methodologymentioning

confidence: 99%

Determinants of agricultural emissions: panel data evidence from a global sample

Nguyen

Schınckus

et al. 2020

Envir. Dev. Econ.

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Using the panel data of 89 economies from 1995–2012, this study examines the major drivers of agricultural emissions while considering affluence, energy intensity, agriculture value added and economic integration. We find long-run cointegration among the variables. Furthermore, our empirical results based on a dynamic fixed effects autoregressive distributed lag model show that the increases in income and economic integration – proxied by trade and foreign direct investment (FDI) – are the major contributors to higher greenhouse gas (GHG) emissions from agriculture in the short run. Additionally, the increases in income, agriculture value added and energy consumption are the major drivers of agricultural emissions in the long run. Notably, trade openness and FDI inflows have significantly negative effects on GHG emissions from agriculture in the long run. These results apply to methane and nitrous oxide emissions. The empirical findings vary across three subsamples of countries at different development stages.

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Determining the Macroeconomic Factors of External Debt Accumulation in Nigeria: An ARDL Bound Test Approach

Cited by 41 publications

References 9 publications

Macroeconomic determinants of external indebtedness of Ethiopia: ARDL approach to co-integration

Macroeconomic determinants of external indebtedness of Ethiopia: ARDL approach to co-integration

Factors Affecting External Debt in Transition Economies: The Case of Central Asia and the Caucasus

Determinants of agricultural emissions: panel data evidence from a global sample

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