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A Demand System for Input Factors when there are Technological Changes in Production

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Abstract

In a system with n input factors there are n − 1 independent cost shares. An often-used approach in estimating factor demand systems is to (implicitly or explicitly) assume that there is a (independent) cointegrating relationship for each of the n − 1 independent cost shares. However, due to technological changes there might not be as many cointegrating relationships as there are (independent) cost shares. The paper presents a flexible demand system that allows for both factor neutral technological changes as well as technological changes that affect the relative use of the different factors. The empirical tests indicate that there are fewer cointegrating relationships than usually implied by using conventional estimation approaches. This result is consistent with technological changes. I argue that since such unexplained technological changes are likely to affect input factor decisions, a demand system that allows for such changes should be preferred.

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Paper provided by Research Department of Statistics Norway in its series Discussion Papers with number 556.

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Date of creation: Sep 2008
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Handle: RePEc:ssb:dispap:556

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Keywords: Factor demand; technological changes; growth rates;

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  1. Håvard Hungnes, 2005. "Identifying Structural Breaks in Cointegrated VAR Models," Discussion Papers 422, Research Department of Statistics Norway.
  2. Arvid Raknerud & Terje Skjerpen & Anders Rygh Swensen, 2003. "A linear demand system within a Seemingly Unrelated Time Series Equation framework," Discussion Papers 345, Research Department of Statistics Norway.
  3. Saikkonen, Pentti & Lütkepohl, Helmut, 1997. "Local power of likelihood ratio tests for the cointegrating rank of a VAR process," SFB 373 Discussion Papers 1997,58, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  4. Jurgen A. Doornik, 1998. "Approximations To The Asymptotic Distributions Of Cointegration Tests," Journal of Economic Surveys, Wiley Blackwell, vol. 12(5), pages 573-593, December.
  5. Richard, J-F, 1980. "Models with Several Regimes and Changes in Exogeneity," Review of Economic Studies, Wiley Blackwell, vol. 47(1), pages 1-20, January.
  6. Slade, Margaret E., 1989. "Modelling stochastic and cyclical components of technical change : An application of the Kalman filter," Journal of Econometrics, Elsevier, vol. 41(3), pages 363-383, July.
  7. Allen, Chris & Urga, Giovanni, 1999. "Interrelated Factor Demands from Dynamic Cost Functions: An Application to the Non-energy Business Sector of the UK Economy," Economica, London School of Economics and Political Science, vol. 66(263), pages 403-13, August.
  8. Abadir,Karim M. & Magnus,Jan R., 2005. "Matrix Algebra," Cambridge Books, Cambridge University Press, number 9780521822893, October.
  9. Harbo, Ingrid, et al, 1998. "Asymptotic Inference on Cointegrating Rank in Partial Systems," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(4), pages 388-99, October.
  10. Rainer Klump & Peter McAdam & Alpo Willman, 2007. "The long-term sucCESs of the neoclassical growth model," Oxford Review of Economic Policy, Oxford University Press, vol. 23(1), pages 94-114, Spring.
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Cited by:
  1. Pål Boug & Ådne Cappelen & Torbjørn Eika, 2013. "Exchange Rate Pass-through in a Small Open Economy: the Importance of the Distribution Sector," Open Economies Review, Springer, vol. 24(5), pages 853-879, November.
  2. Pål Boug & Ådne Cappelen & Torbjørn Eika, 2013. "The importance of the distribution sector for exchange rate pass-through in a small open economy. A large scale macroeconometric modelling approach," Discussion Papers 731, Research Department of Statistics Norway.

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