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Energy consumption and economic growth in Central America: Evidence from a panel cointegration and error correction model

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  • Apergis, Nicholas
  • Payne, James E.

Abstract

This study examines the relationship between energy consumption and economic growth for six Central American countries over the period 1980–2004 within a multivariate framework. Given the relatively short span of the time series data, a panel cointegration and error correction model is employed to infer the causal relationship. Based on the heterogeneous panel cointegration test by Pedroni (Pedroni, P., 1999. Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics 61, 653–670; Pedroni, P., 2004. Panel cointegration: asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis: new results. Econometric Theory 20, 597–627), cointegration is present between real GDP, energy consumption, the labor force, and real gross fixed capital formation with the respective coefficients positive and statistically significant. The Granger-causality results indicate the presence of both short-run and long-run causality from energy consumption to economic growth which supports the growth hypothesis.

Suggested Citation

  • Apergis, Nicholas & Payne, James E., 2009. "Energy consumption and economic growth in Central America: Evidence from a panel cointegration and error correction model," Energy Economics, Elsevier, vol. 31(2), pages 211-216.
    • Handle: RePEc:eee:eneeco:v:31:y:2009:i:2:p:211-216
    DOI: 10.1016/j.eneco.2008.09.002
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    More about this item

    Keywords

    Energy consumption; Growth; Panel unit root and cointegration tests; Granger-causality;
    All these keywords.

    JEL classification:

    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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