Estimating Sectoral Cycles Using Cointegration and Common Features / Robert F. Engle, Joao Victor Issler.

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Engle, Robert F

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Material type: Text

TextSeries: Working Paper Series (National Bureau of Economic Research) ; no. w4529.Publication details: Cambridge, Mass. National Bureau of Economic Research 1993.Description: 1 online resource: illustrations (black and white)Online resources:

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Abstract: This paper investigates the degree of short run and long run comovement in U.S. sectoral output data by estimating sectoral trends and cycles. A theoretical model based on Long and Plosser (1983) is used to derive a reduced form for sectoral output from first principles. Cointegration and common features (cycles) tests are performed and sectoral output data seem to share a relatively high number of common trends and a relatively low number of common cycles. A special trend-cycle decomposition of the data set is performed and the results indicate a very similar cyclical behavior across sectors and a very different behavior for trends. In a variance decomposition exercise, for prominent sectors such as Manufacturing and Wholesale/Retail Trade, the cyclical innovation is more important than the trend innovation.

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Previous	nber w4526 Entrepreneurial Decisions and Liquidity Constraints /	nber w4527 Endogenous Innovation in the Theory of Growth /	nber w4528 An Evaluation of Recent Evidence on the Employment Effects of Minimum and Subminimum Wages /	nber w4529 Estimating Sectoral Cycles Using Cointegration and Common Features /	nber w4530 Aging in Germany and the United States: International Comparisons /	nber w4531 Collateral Damage: How Refinancing Constraints Exacerbate Regional Recessions /	nber w4532 Does Central Bank Intervention Increase the Volatility of Foreign Exchange Rates? /	Next

November 1993.

This paper investigates the degree of short run and long run comovement in U.S. sectoral output data by estimating sectoral trends and cycles. A theoretical model based on Long and Plosser (1983) is used to derive a reduced form for sectoral output from first principles. Cointegration and common features (cycles) tests are performed and sectoral output data seem to share a relatively high number of common trends and a relatively low number of common cycles. A special trend-cycle decomposition of the data set is performed and the results indicate a very similar cyclical behavior across sectors and a very different behavior for trends. In a variance decomposition exercise, for prominent sectors such as Manufacturing and Wholesale/Retail Trade, the cyclical innovation is more important than the trend innovation.