The ARDL Method in the Energy-Growth Nexus Field; Best Implementation Strategies

Angeliki N Menegaki

doi:10.3390/economies7040105

Abstract

A vast number of the energy-growth nexus researchers, as well as other “X-variable-growth nexus” studies, such as for example the tourism-growth nexus, the environment-growth nexus or the food-growth nexus have used the autoregressive distributed lag model (ARDL) bounds test approach for cointegration testing. Their research papers rarely include all the ARDL procedure steps in a detailed way and thus they leave other researchers confused with the series of steps that must be followed and the best implementation paradigms so that they not allow any obscure aspects. This paper is a comprehensive review that suggests the steps that need to be taken before the ARDL procedure takes place as well as the steps that should be taken afterward with respect to causality investigation and robust analysis.

Highlights

Since the seminal work by Kraft and Kraft (1978) on the energy-growth nexus, various cointegration and causality methods have been used in this field and the “X-variable growth nexus” framework in general
In a bivariate energy-growth nexus model, the Yt stands for economic growth and the Xt stands for energy consumption
If we find no evidence of cointegration, the specification will be a vector autoregression (VAR) in 1st difference form (Liu 2009)

Summary

Introduction

Since the seminal work by Kraft and Kraft (1978) on the energy-growth nexus, various cointegration and causality methods have been used in this field and the “X-variable growth nexus” framework in general. Studies before the ARDL establishment, and this was much the case for the energy-growth nexus, used cross sectional analysis through their panel data configuration. This entailed that the countries included in those samples were not homogeneous enough with respect to their economic development level (Odhiambo 2009). The initiation of the autoregressive distributed lag (ARDL) method or Bounds test is due to Pesaran and Shin (1999), while its further development is due to Pesaran et al (2001) It is acknowledged as one of the most flexible methods in the econometric analysis of the energy-growth nexus, when the research framework is shaped by regime shifts and shocks.

The Methodology

Stationarity

Cointegration