Causal Relationship between Energy Consumption and GDP Growth Revisited: A Dynamic Panel Data Approach

Huang, Bwo‐Nung; Hwang, M. J.; Yang, Chao-Ping

doi:10.2139/ssrn.2484437

Cited by 26 publications

(36 citation statements)

References 55 publications

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“…Furthermore, there are several empirical studies that employ panel VAR models. These include Rousseau and Wachtel (2000), Choe (2003), Buch et al (2005), Love and Zicchino (2006), Chong and Gradstein (2007), Aspachs et al (2007), and Huang et al (2008). Most of these studies use IV or GMM estimators, which have become a standard strategy for estimating dynamic panel models since the work of Holtz-Eakin et al (1988) and Arellano and Bond (1991).…”

Section: Introductionmentioning

confidence: 99%

Improved GMM estimation of panel VAR models

Hayakawa

2016

Computational Statistics & Data Analysis

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Section: Introductionmentioning

confidence: 99%

Improved GMM estimation of panel VAR models

Hayakawa

2016

Computational Statistics & Data Analysis

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“…The results supported by different studies in the different regions of the world (See for example, Tharion et al (2005), Fordham and Walker (2005), Huang et al (2008), Dumas (2014), Solarin and Sahu (2014) etc. All of the studies partially supported the role of energy consumption on energy consumption across the globe.…”

Section: Resultsmentioning

confidence: 55%

Military Expenditures and Unemployment Nexus for Selected South Asian Countries

et al. 2015

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The objective of the study is to examine the relationship between military expenditures and unemployment rate in the panel of selected SAARC countries namely India, Nepal, Pakistan and Sri Lanka over the period ranging from 1990 to 2013. In particular, the study used multivariate framework to examine the long-run relationship between military expenditures and unemployment rate by taking into account the effect of macroeconomic variables namely energy consumption, GDP per capita, Foreign Direct Investment (FDI) net inflows and population growth rate. The empirical results show that all the variables exhibit non-stationary behavior and have long-run relationships between them. The results of panel DOLS show that military expenditures favor the employment rate in the SAARC region, as the estimated coefficient of military expenditure has a negative and more elastic relationship with the unemployment rate. Other factors i.e., energy consumption, GDP per capita and incoming FDI significantly decreases the unemployment rate in the SAARC region. However, population growth rate does not exhibit significant association with the unemployment rate during the period under the study. In a second regression apparatus, energy consumption, and GDP per capita significantly decrease the military spending, however, population growth rate significantly increases the military expenditures in the region. The results of causality indicate that there is an inverse causality between energy consumption, GDP per capita and population growth rate with the unemployment rate, while there is no significant causation between military spending and the rest of the variables in the short-run. In the long-run, there is a significant causation between the variables with the exception of population growth rate in SAARC region. These results are valuable both for policy makers and government officials to formulate their strategic polices for SAARC countries.

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“…So many empirical studies have employed the panel data model to study the relationship between energy consumption and economic growth in different countries or regions in different periods. Such as RenukaMahadevan, John Asafu-Adjaye [21], Bwo-Nung Huang [22], Chien-Chiang Lee, Yi-Bin Chiu [23], Ilhan Ozturk et al [24], Nicholas Apergis [25], Nicholas Apergis [26].…”

Section: Methodsmentioning

confidence: 99%