Explaining investment dynamics in U.S. manufacturing: a generalized (S; s) approach
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Caballero, Ricardo
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Explaining investment dynamics in U.S. manufacturing: a generalized (S; s) approach
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In this paper we derive a model of aggregate investment that builds from the lumpymicroeconomic behavior of firms facing stochastic fixed adjustment costs. Instead of theŽ.standard sharp S, s bands, firms’ adjustment policies take the form of a probability ofŽ.adjustment adjustment hazard that responds smoothly to changes in firms’ capacity gap.The model has appealing aggregation properties, and yields nonlinear aggregate timeseries processes. The passivity of normal times is, occasionally, more than offset by thebrisk response to large accumulated shocks. Using within and out-of-sample criteria, wefind that the model performs substantially better than the standard linear models ofinvestment for postwar sectoral U.S. manufacturing equipment and structures investmentdata.
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We are grateful to Olivier Blanchard, Whitney Newey, James Stock, an editor, three anonymousreferees, and seminar participants at Brown, CEPR-Champoussin, Chicago, Columbia, EconometricŽ.Society Meetings Caracas and Tokyo , EFCC, Harvard, IMPA, LSE, NBER, Princeton, Rochester,Ž.SITE, Toronto, U. de Chile, and Yale for their comments. Financial support to Caballero from theŽ. Ž .National Science and Sloan Foundations and to Engel from FONDECYT Grant 195-510 and theŽ.Mellon Foundation Grant 9608 is gratefully acknowledged.2Since plants’ entry is excluded from their sample, these statistics are likely to represent lowerbounds on the degree of lumpiness in plants’ investment patterns.3We use the word ‘‘project’’ to emphasize the fact that the actual implementation of a projectmay cover more than a year-observation; realistic time-to-build aspects of investment are not incontradiction with the view that investment episodes are lumpy in nature.4Ž.See Chirinko 1994 for a survey of the empirical investment literature.
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Econometrica, Vol. 67, No. 4, pp. 783 - 826, Julio, 1999
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