Comment Paul A. Samuelson Paul A. Samuelson Search for other works by this author on: Oxford Academic Google Scholar The Review of Economic Studies, Volume 29, Issue 3, June 1962, Pages 251–254, https://doi.org/10.2307/2295962 Published: 01 June 1962

Parable and Realism in Capital Theory: The Surrogate Production Function Get access Paul A. Samuelson Paul A. Samuelson Cambridge, Mass. Search for other works by this author on: Oxford Academic Google Scholar The Review of Economic Studies, Volume 29, Issue 3, June 1962, Pages 193–206, https://doi.org/10.2307/2295954 Published: 01 June 1962

Nicholas Kaldor, James A. Mirrlees; A New Model of Economic Growth, The Review of Economic Studies, Volume 29, Issue 3, 1 June 1962, Pages 174–192, https:/

I. Introduction, 246. — II. The “need” criterion for entry, 247. — III. Effects on competition, 252. — IV. Unsound credit policies, 256. — V. The problem of bank failures, 260.

In this article we derive the exact finite sample frequency functions of the least squares and maximum likelihood estimators of the marginal propensity to consume, assuming the basic stochastic Keynesian model. The properties of these functions are considered in more detail for particular values of the parameters and sizes of sample. It is concluded that, for samples of 10 or more observations, generated by the model considered (with realistic values of the parameters), the maximum likelihood estimator of the marginal propensity to consume is the better general purpose estimator of this parameter. ALTHOUGH THERE have been several major contributions to the large sample theory of estimators of the parameters of simultaneous equation systems,' there is, as yet, virtually no small sample theory for these estimators. With the exception of Nagar's approximations for the bias and moment matrix of k-class estimators,2 evidence concerning the small sample behaviour of various estimators is confined to two types. The first type includes studies in which the parameters of simultaneous equation models have been estimated, by various methods, from real data, so that the resulting estimates can be compared, by either referring to economic theory and other a priori evidence, or testing predictions.3 The value of such studies is limited, however, by unknown errors in both the data and the specification of the models. The second type of evidence is provided by a number of valuable Monte Carlo studies.4 But these too have certain disadvantages as compared with a mathematical study. One is that, in order to investigate the effects of variations in the sample size or the structural parameters, it would be necessary to analyse many different sets of synthetic samples. Another is that the measures obtained from Monte Carlo studies are, themselves, subject to sampling errors. Moreover, in studies based upon no more than 100 synthetic samples, these errors are not negligible.5

According to Wold, information on some of the variables at time t cannot be used for prediction of the values of the remaining variables at t, in a simultaneous equation system. This, however, is not the case with his causal model. This paper considers the stochastic processes underlying the two models, uses the theory of canonical correlation to discuss Wold's criticism, and suggests the type of additional information necessary to remove these objections. It further shows how both these models are complementary to each other. IN A SERIES of papers, Wold [9, 10, 11, 12] has recently proposed a new type of econometric model, which he calls the implicit causal chain model. The main incentive for this model was an attempt to combine the advantages of Tinbergen's causal model with those of the simultaneous equation systems initiated by Haavelmo [3, 4]. This latter model, which has been studied in detail by the Cowles Commission, is also known as the system. Wold has raised some objections, mainly from the point of view of prediction, against the simultaneous equation system; and in order to remedy these, he relaxed some of the restrictions on the correlational properties of the residuals in the simultaneous equation system. In this paper, these two models are discussed from an angle which offers a possibility for reconciling them. It is shown how the merits of both systems can be utilized by effecting some synthesis of them with the help of the stochastic process underlying both models. Furthermore, by using canonical analysis, it is shown how both these models are complementary. It must, of course, be added that the prediction problem has been considered purely on the basis of the stochastic model assumed, and the possible applicability of different representations of such models, for example, in economic contexts, under wider conditions is not discussed. Wold has objected to interdependent models on grounds other than prediction, especially from the point of view of the interpretation of structural parameters as elasticities, etc. The present paper, however, will not deal with this aspect of the problem. Instead, it deals primarily with the stochastic processes underlying economic models, and the demand and supply model, considered in the next section, is only for illustration.

IN this paper it is assumed that the goal of economic development is rising per capita real incomes and that Paul Hoffman's suggested per capita income figure of $300 annually 1 is acceptable as the income level dividing the underdeveloped from the developed economies. It is the purpose in this paper to examine the capital formation rate and the rate of generation of GNP in Burma since I948, the year in which Burma achieved independence. On the basis of Burma's demonstrated economic performance since independence, an endeavor is made to determine the magnitude of the economic development task confronting the Burmese economy. The method employed in this endeavor is one of determining, under varying sets of assumptions concerning aggregate capital-output ratios and population growth rates, the time required to accumulate capital in sufficient amount to provide real per capita incomes of $300 per year.

John A. Brittain, A Regression Model for Estimation of the Seasonal Component in Unemployment and Other Volatile Time Series, The Review of Economics and Statistics, Vol. 44, No. 1 (Feb., 1962), pp. 24-36

Tig HE appearance of Business Cycle Indicators pTovides a good opportunity to review some of the National Bureau's recent work on business cycles and forecasting.' It is more than a decade since the Bureau published Moore's Statistical Indicators of Cylical Revivals and Recessions, a paper which attained a degree of popularity unusual for National Bureau publications. 2 Since then, Moore's list of indicators has been kept up to date and widely used. The papers brought together in the present volume represent a comprehensive report on the recent work of Moore and his colleagues on cyclical indicators.3 Business Cycle Indicators consists of two volumes, the second of which is an appendix which describes and gives the monthly or quarterly data for a long list of series. Volume I is in three parts. The first and most important includes excerpts from annual reports of the National Bureau by Fabricant and Burns, Moore's earlier Occasional Papers on statistical indicators and several other papers by Moore, the original paper by Mitchell and Burns on indicators published in I938, a paper by Frank Morris on the predictive value of the leading series, and one by W. A. Beckett reporting on a set of indicators for Canada. Moore's revised list of 26 series is presented in Chapter 3. Part II contains six papers, some previously unpublished, on individual leading indicators -profits, business failures, new incorporations and new business firms, manufacturers' new orders, hours worked, and other labor-market series. Of these, the paper by Zarnowitz on new orders will probably be of greatest interest. None of these chapters is likely to excite the reader; they are all essentially descriptive; and some are outright stodgy in their style and treatment of their subject matter. Part III is concerned with methods of using the individual indicators and diffusion indices on a current basis. Two papers by Shiskin report on the work he has done with the indicators at the Census Bureau. Two others are by Moore. One describes an amplitude adjustment for the leading indicators, and the other deals with the average duration of run as a way of summarizing the current behavior of a group of series. I shall make no attempt to review each of these papers. Instead, in the rest of this article, I shall (i) compare the National Bureau's approach to business-cycle research with that which rests on the use of aggregative models and (2) attempt a general evaluation of the usefulness of economic indicators in short-run forecasting.