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Article

A Nonlinear Autoregressive Distributed Lag (NARDL) Analysis of West Texas Intermediate Oil Prices and the DOW JONES Index

by 1,2,3 and 3,4,5,6,7,8,*
1
School of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia
2
School of Business and Law, Edith Cowan University, Joondalup, WA 6027, Australia
3
Department of Finance, Asia University, Wufeng, Taichung 41354, Taiwan
4
Discipline of Business Analytics, University of Sydney Business School, Darlington, NSW 2006, Australia
5
Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, 3062 PA Rotterdam, The Netherlands
6
Department of Economic Analysis and ICAE, Complutense University of Madrid, 2840 Madrid, Spain
7
Department of Mathematics and Statistics, University of Canterbury, Christchurch 8041, New Zealand
8
Institute of Advanced Sciences, Yokohama National University, Kanagawa 240-8501, Japan
*
Author to whom correspondence should be addressed.
Energies 2020, 13(15), 4011; https://doi.org/10.3390/en13154011
Received: 9 January 2020 / Revised: 13 March 2020 / Accepted: 7 July 2020 / Published: 4 August 2020
(This article belongs to the Special Issue Multivariate Modelling of Fossil Fuel and Carbon Emission Prices)
The paper features an examination of the link between the behaviour of oil prices and DowJones Index in a nonlinear autoregressive distributed lag nonlinear autoregressive distributed lag (NARDL) framework. The attraction of NARDL is that it represents the simplest method available of modelling combined short- and long-run asymmetries. The bounds testing framework adopted means that it can be applied to stationary and non-stationary time series vectors, or combinations of both. The data comprise a monthly West Texas Intermediate (WTI) crude oil series from Federal Reserve Bank of St Louis (FRED), commencing in January 2000 and terminating in February 2019, and a corresponding monthly DOW JONES index adjusted-price series obtained from Yahoo Finance. Both series are adjusted for monthly USA CPI values to create real series. The results of the analysis suggest that movements in the lagged real levels of monthly WTI crude oil prices have very significant effects on the behaviour of the DOW JONES Index. They also suggest that negative movements have larger impacts than positive movements in WTI prices, and that long-term multiplier effects take about 9 to 12 months to take effect. View Full-Text
Keywords: NARDL; Bounds Tests; WTI; DOW JONES; asymmetries; multiplier effects NARDL; Bounds Tests; WTI; DOW JONES; asymmetries; multiplier effects
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MDPI and ACS Style

Allen, D.E.; McAleer, M. A Nonlinear Autoregressive Distributed Lag (NARDL) Analysis of West Texas Intermediate Oil Prices and the DOW JONES Index. Energies 2020, 13, 4011. https://doi.org/10.3390/en13154011

AMA Style

Allen DE, McAleer M. A Nonlinear Autoregressive Distributed Lag (NARDL) Analysis of West Texas Intermediate Oil Prices and the DOW JONES Index. Energies. 2020; 13(15):4011. https://doi.org/10.3390/en13154011

Chicago/Turabian Style

Allen, David E., and Michael McAleer. 2020. "A Nonlinear Autoregressive Distributed Lag (NARDL) Analysis of West Texas Intermediate Oil Prices and the DOW JONES Index" Energies 13, no. 15: 4011. https://doi.org/10.3390/en13154011

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