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Using Mixtures of Flexible Functional Forms to Estimate Factor Demand Elasticities

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  • GORDON, Stephen

Abstract

Researchers who wish to estimate factor demands using flexible functional forms may now choose from several candidates supplied by the theoretical literature. Unfortunately, the criteria for a priori model selection are not clear. This paper adopts the use of Bayesian methods and argues that there is in fact no need to choose; the optimal strategy is to use a mixture of functional forms to estimate the parameters of interest. Problems of overfitting are avoided by the imposition of the appropriate regulatory conditions. Practical implementation is greatly simplified by the use of Markov chain Monte Carlo techniques. In an example, three well-known functional forms for cost functions are applied to estimate factor demand elasticities in the Canadian manufacturing sector.

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Bibliographic Info

Paper provided by Université Laval - Département d'économique in its series Cahiers de recherche with number 9502.

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Date of creation: 1995
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Handle: RePEc:lvl:laeccr:9502

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Cited by:
  1. Hanrahan, Kevin F. & Westhoff, Patrick C. & Young, Robert E., II, 2001. "Trade Allocation Modeling: Comparing The Results From Armington And Locally Regular Ai Demand System Specifications Of A Uk Beef Import Demand Allocation Model," 2001 Annual meeting, August 5-8, Chicago, IL 20510, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  2. Chua, C.L. & Griffiths, W.E. & O'Donnell, C.J., 2001. "Bayesian Model Averaging in Consumer Demand Systems with Inequality Constraints," Department of Economics - Working Papers Series 806, The University of Melbourne.
  3. Keane, Michael, 2003. "Comment on “Simulation and Estimation of Hedonic Models” by Heckman, Matzkin and Nesheim," MPRA Paper 55141, University Library of Munich, Germany.
  4. McCausland, William J., 2008. "On Bayesian analysis and computation for functions with monotonicity and curvature restrictions," Journal of Econometrics, Elsevier, vol. 142(1), pages 484-507, January.

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