Econometrica

Journal Of The Econometric Society

An International Society for the Advancement of Economic
Theory in its Relation to Statistics and Mathematics

Edited by: Guido W. Imbens • Print ISSN: 0012-9682 • Online ISSN: 1468-0262

Econometrica: Mar, 1970, Volume 38, Issue 2

The Predictive Performance of Econometric Models of Quarterly Investment Behavior

https://doi.org/0012-9682(197003)38:2<213:TPPOEM>2.0.CO;2-I
p. 213-224

Dale W. Jorgenson, Jerald Hunter, M. Ishag Nadiri

In this paper four alternative quarterly econometric models of investment behavior are compared with regard to predictive performance. Predictive performance may be assessed in two ways: (i) We compare prediction errors for a period of prediction with errors for a period of fit. (ii) We fit investment functions for both periods and test for structural change. These two procedures may be viewed as alternative tests of the hypothesis of structural change; the second is more powerful from the statistical point of view. Test of predictive performance supplement the comparisons of alternative models given in our preceding paper [17]. Goodness of fit may be exaggerated by consideration of a wide range of alternatives and selection of the one that fits best. If goodness of fit is exaggerated, a predictive test should produce evidence of structural change between the period of fit and the period of prediction. Of course, the better an econometric model fits the data, the more stringent this criterion for predictive performance. The econometric models included in our study are those of Anderson [1], Eisner [7], Jorgenson and Stephenson [19], and Meyer and Glauber [21]. On the basis of predictive performance the ranking of the alternative models is as follows: (1) Eisner, (2) Jorgenson-Stephenson, (3) Meyer-Glauber, and (4) Anderson. This ranking is similar to that resulting from comparisons based on goodness of it presented in our preceding paper [17]. For econometric models of quarterly investment behavior, the models that fit the best also have the best predictive performance.


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