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A Diagnostic Test for the ¡®Dutch Disease¡¯ in the U.S.A using the ARDL Bounds Testing Technique

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  • Roberto Roson
Abstract
This study examines the impact of the most recent oil boom on North Dakota¡¯s agricultural sector. I employ the autoregressive distributive lag (ARDL) model to examine short and long run relationships among four labor competing sectors. The model produces an optimal lag order of ARDL (6,6,6,5). Results reveal an 80% speed of adjustment coefficient. This implies that about 80% of any disequilibrium caused by a shock to the economy can be corrected within a quarter of a year. The oil sector has a negative and positive impact on the agricultural and construction sectors respectively but no significant impact on the manufacturing sector. The impulse response function (IRF) from an orthogonalized structural vector autoregression (SVAR) matrix system revealed no deviation from the boom period equilibrium agricultural GDP. Structural spending policies are recommended to curb the negative effects of another oil boom on labor competing sectors. The introduction of an agricultural wage transfer tax will also be helpful in the event of another oil boom.

Suggested Citation

  • Roberto Roson, 2019. "A Diagnostic Test for the ¡®Dutch Disease¡¯ in the U.S.A using the ARDL Bounds Testing Technique," Research in Applied Economics, Macrothink Institute, vol. 11(1), pages 32-46, March.
    • Handle: RePEc:mth:raee88:v:11:y:2019:i:1:p:32-46
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    References listed on IDEAS

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    1. Weber, Jeremy G., 2012. "The effects of a natural gas boom on employment and income in Colorado, Texas, and Wyoming," Energy Economics, Elsevier, vol. 34(5), pages 1580-1588.
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    4. Bangsund, Dean A. & Hodur, Nancy M., 2013. "Petroleum Industry's Economic Contribution to North Dakota in 2011," Agribusiness & Applied Economics Report 146512, North Dakota State University, Department of Agribusiness and Applied Economics.
    5. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
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