Evidence and Ideology in Macroeconomics: The Case of Investment Cycles

The paper reports the principal findings of a long term research project on the description and explanation of business cycles. The research strongly confirmed the older view that business cycles have large systematic components that take the form of investment cycles. These quasi-periodic movements can be represented as low order, stochastic, dynamic processes with complex eigenvalues. Specifically, there is a fixed investment cycle of about 8 years and an inventory cycle of about 4 years. Maximum entropy spectral analysis was employed for the description of the cycles and continuous time econometrics for the explanatory models. The central explanatory mechanism is the second order accelerator, which incorporates adjustment costs both in relation to the capital stock and the rate of investment. By means of parametric resonance it was possible to show, both theoretically and empirically how cycles aggregate from the micro to the macro level. The same mathematical tool was also used to explain the international convergence of cycles. I argue that the theory of investment cycles was abandoned for ideological, not for evidential reasons. Methodological issues are also discussed

A Precursor to Muth: Tinbergen's 1932 Model of Rational Expectations.

This paper describes a model, published in 1932, in which explicitly rational expectations are used to study a dynamic problem of intertemporal inventory allocation. The model is very similar to Muth (1961), except that the stochastic process in the latter is serially correlated, whereas in Tinbergen's model the disturbances are serially uncorrelated. Hence, Tinbergen was able to solve a rational expectations model thirty years before Muth's much more well known contribution. Copyright 1991 by Royal Economic Society.

Simplicity, Scientific Interference and Econometric Modelling.

Two issues are discussed in this paper. The first is whether a formal definition and justification of simplicity (parsimony) in scientific inference can be found, and whether an optimal level of simplicity is obtainable. A definition of simplicity is possible, as are the optimum conditions for the desired degree of simplicity. The model of inference used here relates Bayesian inference to algorithmic information theory. Simplicity is examined in the light of induction, the Duhem-Quine thesis, and bounded rationality. The second issue relates to the role that simplicity might play in econometric modeling. This is elucidated with some remarks on the 'general to specific' approach to modeling and discussions on the purpose of a model. Copyright 1995 by Royal Economic Society.