Estimating Sectoral Cycles Using Cointegration and Common Features /
Engle, Robert F.
Estimating Sectoral Cycles Using Cointegration and Common Features / Robert F. Engle, Joao Victor Issler. - Cambridge, Mass. National Bureau of Economic Research 1993. - 1 online resource: illustrations (black and white); - NBER working paper series no. w4529 . - Working Paper Series (National Bureau of Economic Research) no. w4529. .
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.
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Mode of access: World Wide Web.
Estimating Sectoral Cycles Using Cointegration and Common Features / Robert F. Engle, Joao Victor Issler. - Cambridge, Mass. National Bureau of Economic Research 1993. - 1 online resource: illustrations (black and white); - NBER working paper series no. w4529 . - Working Paper Series (National Bureau of Economic Research) no. w4529. .
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.
System requirements: Adobe [Acrobat] Reader required for PDF files.
Mode of access: World Wide Web.