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Abstract
Young, P.C. and Pedregal, D.J. (1998) Data-based mechanistic modelling (of macro economic systems). Chapter 6 in C. Heij et al (Eds.), System Dynamics in Economic and Financial Models, J. Wiley: Chichester and new York.
| Over many years, the first author has promoted a data-based approach to the modelling of environmental and other dynamic systems. This favours simpler mathematical descriptions of complex dynamic processes, arguing that large, highly parameterised models can rarely be justified statistically because of the inherent limitations in the available time-series data. Coupled with the inability to perform planned experiments, such data deficiencies seriously restrict the identifiability of the parameters in such models. This problem is exacerbated by the occurrence of modal dominance in dynamic systems; that is, the fact that the normal response of high order dynamic systems is governed mainly by those few eigenvalues which define the identifiable dominant modes of the system. It seems obvious that, if sufficiently informative time series data are available, the dominant modal structure should be identified directly from these data using objective methods of statistical inference. Data-based Mechanistic (DBM) modelling is a time series approach to environmetric (or other systems) analysis which attempts to extend conventional, data-based, time-series methodology in a manner which enhances the model builder's ability to interpret the identified model in physical, biological, ecological or, in the present context, socio-economic terms. This chapter outlines the DBM approach and applies it to the problem of modelling a quite simple nonlinear relationship between quarterly measures of seasonally adjusted unemployment GNP and total capital investment in the USA over the period 1948 to 1988. |
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Updating responsibility Arun Chotai. This page is copyright of Lancaster University.
12/10/01 - PGM.