Three examples of econometric research in the Netherlands

Transcription

1 Three examples of econometric research in the Netherlands By cand. oecon. Trygve Haavelmo, Oslo, p.t. in Geneva Tinbergen, J., A n e c o n o m e t r i c a p p r o a c h t o b u s i n e s s c y c l e p r o b l e m s. (Actualités scientifiques er industrielles Impasses économiques 2.) Paris Hermann & Cie. 73 pp. Fr. 18,-. Dalmulder, J. J. J., Dr., O n e c o n o m e t r i c s. Some suggestions concerning the method of econometrics and its application to studies regarding the influence of rationalisation on employment in the U.S.A. (Nederlandsch Economisch Instituut. Nr.19.) Haarlem De Erven F. Bohn. 88 pp. fl.1,50. Koopmans, T., Dr., L i n e a r r e g r e s s i o n a n a l y s i s o f e c o n o m i c t i m e s e r i e s. (Nederlandsch Economisch Instituut. Nr.20.) Haarlem De Erven F. Bohn. XI, 150 pp. fl.2,-. As apparent from the titles, the present works deal with central problems of the quantitative analysis of business cycles. T i n b e r g e n analyses certain actual economic problems in the Netherlands using mathematical-statistical methods of business cycle research. Here, he follows the central idea that he has already developed in several earlier works and in the current book briefly formulates as follows (p.8): We may start from the proposition that every change in economic life has a number of proximate causes. These proximate causes themselves have their own proximate causes which in turn are indirect deeper causes with respect to the first mentioned change, and so on. Thus a network of causal relationships can be laid out connecting all the successive changes occurring in an economic community. From this point of view, the analysis and correspondingly the chapter division of the present book falls into the following natural steps. First, the field of research under scrutiny is delimitated and a simplified model constructed, containing only certain characteristic basic features of the field. In the present case, the field of research is delineated by the specific task of the analysis, namely first and foremost to study the influence of international factors. A set of variables is selected by simplifying assumptions and causal relations between these are approximated by linear equations. The analysis is a short-run analysis, thus the trend is eliminated from the time series. This construction of the model is treated in close conjunction with the statistical analysis of the time series of o b s e r v e d variables. Both the choice of variables and the form of the relations is 1

2 substantially influenced by a cross-validation of the statistical observations by means of multiple regression. In this way, the author obtains a dynamic system of 22 independent equations with 22 variables. Subsequently, this system is solved mathematically (ch. II), that is, the system s pattern of movement is determined when the variables adhere to the determined causal relations. The pattern of movement of the international factors is assumed known here, and the results of several alternative patterns are analysed. This is a necessary and for small national economies also entirely justified simplification, when one does not want to deal with the problem of international business cycles as a whole. Two alternatives are considered, namely first the invariability of the international factors and, secondly, the case of cyclical movements. It appears that the business cycles of the various national economies are primarily influenced by the international movements. Thirdly, the impacts of practical economic policy interventions are treated. The system of dynamic equations is especially well suited to study these impacts. Economic policies are here expressed as changes of certain entities in the system of equations and their effect can be analysed by solving the equations. The effects of different types of economic policy interventions are studied in detail, for example the effect of public employment, rationalization, a reduction in wages, etc. This analysis clearly shows the difference between the various levels of stabilization (i.e. here the trends) that are obtainable by applying different types of economic policies. Due to the rigorous logical construction of the theoretical system and the close connection to the real economic life through statistical analysis running in parallel at each point, the author succeeded in arriving at exceptionally clear statements of a series of fundamental phenomena of business cycles. Of course, the results must be treated with due caution, here as always when simplifying assumptions are being applied. Nobody is perhaps more concerned with this than the author himself. He calls his system merely an example of quantitative economic analysis. Yet, many of the results are doubtlessly of great practical relevance and directly applicable. D a l m u l d e r has in the first half of his book made an attempt to place econometric research within the larger field of the various sciences. This presentation is done on purely philosophical grounds. He arrives at a ranking of those sciences that he calls practically- 2

3 practical speculative sciences, according to the different goals pursued, and the order of these goals is in turn defined by religious-philosophical reflections. The econometric science is placed in the group: theoretical economics, and is here defined by its name. In general, this part of the book is more a presentation of the author s worldview than assertions that can be discussed on an objective basis. The second part of the book, which supposedly should serve as a practical application of the first part, is not well connected to it and is of an entirely different character. The author has, in the beginning of this part, deducted a very interesting scheme of economic reality in order to study the effects of the rationalization on employment in the United States. Here he contrasts the trend movements with the movement of the deviations from the trend and analyses both. Certain policy parameters are assumed to be constant in the short run but variable in the long run. This seems to be an interesting and very fruitful way of looking at the problem, and the author has been clear and precise in the formulations also of the remaining theoretical foundations of the system. The author might have chosen a less complicated system to serve as an example for a method. As it is now, he obtains a tremendous number of equations and the specific relations can thus not be discussed so thoroughly as would have been desirable for an example. For the same reason, his discussion of the solution of the system of equations (which, by the way, unfortunately contains on page 62 an incorrect statement about the solution of a transcendental equation) is somewhat too brief; the same applies to the treatment of the statistical verification of the system. The only key result at least apparently is a wave of 18-month duration, which according to the author is consistent with certain studies in the United States. However, the evidence that the author produces does not seem to be sufficient to support this somewhat solitary result. K o o p m a n s treats in his book a central statistical-theoretical problem, which is closely connected to the quantitative-mathematical analysis of business cycles, namely the problem of multivariate regression for time series, particularly applied to the world economy. Regression analysis is of fundamental importance, not only for the statistical verification of economic laws that are formulated a priori, but also as basis for the formulation of such laws. Two main questions of regression analysis are posed here and can briefly be formulated as follows: Is the hypothesis of a given functional relation between some variables in 3

4 accordance with the available observation material? And, secondly, if the hypothesis can be maintained, how can the various parameters of the hypothesis be e s t i m a t e d from these data, and how certain are these estimates? Koopmans has in particular dealt with the latter question. The starting point for the author is the difference between the classical Fisherian method of regression with its presumption of one dependent variable, which is determined by a number of independent variables, and the confluence analysis of Professor Frisch in which the dependencies, as common in economic theory, are considered to be mutual. Both methods are discussed thoroughly. At the centre of this discussion, as well as in the subsequent positive part of the book, are the problems, which are conditioned by the fact that we here have to take into consideration two different kind of errors; namely first, errors that arise as the observed variables contain elements, which are not considered in the simplified hypothesis, and, secondly, sampling errors due to insufficient representativity of the limited data material. The latter type of error can by preconditions be removed from the investigation of the variables, whereas an assessment of the former type of error requires further elaboration. The author shows how different treatment of this sort of error leads to different choices of regression types and how it influences the errors in the estimation of the coefficients. The classical sampling error falls within the Fisherian assumption of one dependent and several error-free independent variables. Koopmans considers the most general case that all variables have errors and arrives at approximative expressions for the probability distribution of the regression coefficients in that case. The author himself highlights the dubious value of operating with the concept of a repeated sample with economic time series. He thinks however that strong emphasis of an assessment of the sampling errors forces one to assume certain error benchmarks although this necessitates restrictive presumptions about the distribution of errors. Such benchmarks could however be dangerous and misleading, if they are inadvertently used as limits of variation of the true coefficients. Meanwhile, the errors that stem from the simplifying assumptions about the nature of the functional relationship are probably in general much more important for time series than the sampling errors. The author has carried out his analysis on rigorous mathematical grounds, showing a deep understanding of sampling theory. Particularly clear and instructive is the highlighting 4

5 and critical treatment of the different methods. The positive parts of the book are valuable contributions to the refinement of modern regression analysis and the work can in its entirety be recommended as an excellent textbook of regression theory. (translated by Florian K. Diekert - f.k.diekert@bio.uio.no) 5