Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-23T10:56:11.375Z Has data issue: false hasContentIssue false
  • English
  • Français

Interrelation, Structural Changes and Cointegration in a Model for Manufacturing Demand in the Nederlands

Published online by Cambridge University Press:  17 August 2016

Franz C. Palm  and
Gerard A. Pfann
Franz C. Palm
Affiliation:
University of Limburg
Gerard A. Pfann
Affiliation:
University of Limburg
Get access

Summary

This paper is concerned with dynamic factor demand systems. First, for the intertemporal expected profit maximization problem given quadratic adjustment costs, it is shown that interrelations between factor inputs result from specific characteristics of the innovations in the technology – not from substitution or adjustment costs trade-off possibilities. Second, in line with the Lucas critique, the impact of a structural change in the process of the explanatory variables on the factor demand decision rules is analyzed. Third, the non-stationarity of the factor demand series can be accounted for by that of relative factor prices when demand and price series are cointegrated.

The model which allows for structural changes in the processes of the explanatory variables and for cointegration is applied to quarterly data for manufacturing in the Netherlands for the period 1971.1 – 1984. IV.

Résumé

Résumé

Cet article traite de systèmes dynamiques de demande de facteurs. On montre tout d'abord que, dans un contexte de maximisation intertemporelle du profit attendu avec des coûts d'ajustement quadratiques, les interrelations entre les facteurs résultent de caractéristiques spécifiques des innovations technologiques, plutôt que de possibilités d'arbitrages entre les coûts d'ajustement ou de substitution. Ensuite, dans la lignée de la critique de Lucas, on analyse l'impact d'un changement structurel dans la formation des variables explicatives sur les règles décisionnelles de demande de facteurs. Enfin, on montre que la non-stationnarité des séries de demande de facteurs peut s'expliquer par la non-stationnarité des prix relatifs des facteurs, lorsque les séries de demande et de prix sont cointégrées. Le modèle qui incorpore cointégration et changements structurels dans la formation des variables explicatives est appliqué au secteur manufacturier des Pays-Bas pour la période 1971. I-1984. IV.

Keywords

825824825824
Type
Research Article
Information
Recherches Économiques de Louvain/ Louvain Economic Review , Volume 57 , Issue 3 , 1991 , pp. 221 - 243
Copyright
Copyright © Université catholique de Louvain, Institut de recherches économiques et sociales 1991 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

The authors wish to thank an unknown referee for helpful comments. Permission by J. Wiley & Sons, Ltd. for reusing some results from Kodde et al. (1990) is gratefully acknowledged. Gerard Pfann thanks the Royal Netherlands Academy of Arts and Science (K.N.A.W.) for financial support

References

Blanchard, O.J. and Kahn, C.M. (1980), The solution of linear difference models under rational expectations, Econometrica 48, 1305–1311.Google Scholar
Chamberlain, G. (1982), Multivariate regression models for panel data, Journal of Econometrics 18, 5–46.Google Scholar
Danthine, J.P. (1989), Modélisation des fluctuations conjoncturelles: survol de quelques récents développements, Recherches Economiques de Louvain 55, 213–244.Google Scholar
Engle, R.F. (1982), Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflations, Econometrica 50, 987–1008.Google Scholar
Engle, R.F., Hendry, D.F. and Richard, J.F. (1983), Exogeneity, Econometrica 51, 277–304.Google Scholar
Engle, R.F. and Granger, C.W.J. (1987), Co-integration and error correction: Representation, estimation and testing, Econometrica 55, 251–276.Google Scholar
Engle, R.F. and Yoo, B.S. (1987), Forecasting and testing in cointegrated systems, Journal of Econometrics 35, 143–159.Google Scholar
Epstein, L.G. and Denny, M.G.S. (1983), The multivariate flexible accelerator model: Its empirical restrictions and an application to U.S. manufacturing, Econometrica 51, 647–674.Google Scholar
Epstein, L.G. and Yatchew, A.J. (1985), The empirical determination of technology and expectations. A simplified procedure, Journal of Econometrics 27, 235–258.Google Scholar
Fuller, W.A. (1976), Introduction to Statistical Time Series, New York, Wiley.Google Scholar
Gourieroux, C., Monfort, A. and Trognon, A. (1985), Moindres carrés asymptotiques, Annales de L'INSEE 58, 91–122.Google Scholar
Hansen, L.P. and Sargent, T.J. (1981), Linear rational expectations models for dynamically interrelated variables, in: Lucas, R. and Sargent, T., eds., Expectations and Econometric Practice. Minneapolis: University of Minnesota Press.Google Scholar
Hansen, L.P. and Singleton, K. (1982), Generalized instrumental variables estimation of nonlinear rational expectations models, Econometrica 50, 1269–1286.Google Scholar
Johansen, S. and Juselius, K. (1990), Maximum likelihood estimation and inference on cointegration with applications to the demand for money, Oxford Bulletin of Economics and Statistics 52, 169–210.Google Scholar
Kodde, D.A., Palm, F.C. and Pfann, G.A. (1990), Asymptotic least-squares estimation – efficiency considerations and applications, Journal of Applied Econometrics 5, 229–243.Google Scholar
Kollintzas, T. (1985), The symmetric linear rational expectations model, Econometrica 53, 963–976.Google Scholar
Liu, L.M., Hudak, G.B., Box, G.E.P., Muller, M.E., Tiao, G.C. (1986), The SCA Statistical System – Reference Manual for Forecasting and Time Series Analysis, Illinois: SCA-Press.Google Scholar
Ljung, G.M. and Box, G.E.P. (1978), On a measure of lack of fit in time series models, Biometrika 65, 297–303.Google Scholar
Lucas, R.E. (1976), Econometric policy evaluation: a critique, in Brunner, K. and Meltzer, A.H., eds., The Phillips Curve and Labor Markets, Amsterdam: North Holland.Google Scholar
Meese, R. (1980), Dynamic factor demand schedules for labour and capital under rational expectations, Journal of Econometrics 14, 141–158.Google Scholar
Morrison, C.J. (1986), Structural models of dynamic factor demand with non-static expectations: An empirical assessment of alternative expectations specifications, International Economic Review 27, 365–386 Google Scholar
Nickell, S.J. (1985), Error correction, partial adjustment and all that: an expository note, Oxford Bulletin of Economics and Statistics 47, 119–129.Google Scholar
Palm, F.C. and Pfann, G.A. (1990), Interrelated demand rational expectations models for two types of labour, Oxford Bulletin of Economics and Statistics 52, 45–68.Google Scholar
Perron, P. (1989), The Great Crash, the oil price shock and the unit root hypothesis, Econometrica 57, 1361–1401.Google Scholar
Phillips, P.C.B., and Durlauf, S. N. (1986), Multiple time series regression with integrated processes, Review of Economic Studies 53, 473–495.Google Scholar
Phillips, P.C.B. (1991), Optimal inference in cointegrated systems, Econometrica 59, 283–306.Google Scholar
Pindyck, R.S. and Rotemberg, J. J (1983), Dynamic factor demands and the effects of energy price shocks, American Economic Review 73, 1066–1079.Google Scholar
Treadway, A.B. (1971), The rational multivariate flexible accelerator, Econometrica 39, 845–855.Google Scholar