Charles J. Cicchetti, Anthony C. Fisher, V. Kerry Smith, An Econometric Evaluation of a Generalized Consumer Surplus Measure: The Mineral King Controversy, Econometrica, Vol. 44, No. 6 (Nov., 1976), pp. 1259-1276
Using cross-section data collected from a panel of institutional investors in 1969, 1970, 1972, this paper focuses on how knowledgeable individuals formulate forecasts of future rates of price inflation, We estimate return-to-normality and error-learning forecasting models and inquire whether such equations can be interpreted simply as reduced forms of an autoregressive forecasting structure. Assuming that respondents' expectations were formed rationally in the sense of Muth, a series of tests lead us to reject the hypothesis of a purely autoregressive forecasting structure. Placed in the context of the return-to-normality model, the decisive evidence consists both of significantly nonzero intercepts and of impor- tant variations in survey respondents' anticipated normal rates of inflation that cannot be explained as a reduced-form reflection of past variation in observed inflation rates. These findings indicate that information not collinear with past realizations of price-level change plays an important role in the forecasting process, important enough to allow expected near-term rates of inflation to follow observed inflation rates more closely than autoregres- sive time series models would suggest.
The measure of the capital intensity of an industry used below is the quotient of the value of gross capital stock and the total annual wages and salaries bill.2 The United Kingdom input-output tables for 1968 are available in a highly disaggregated form and provide the wages and salaries bill for each industry, but the Blue Book estimates of gross capital stock are available only in a much more highly aggregated form. By combining the two sources, comparable figures were obtained for a 17-industry classification. The industries were: Agriculture, Extractive Industries, Food and Drink, Chemicals, Iron and Steel, Engineering, Clothing, Pottery, etc., Timber, Paper, etc., Construction, Gas, Electricity, Water Supply, Transport, Communication, and Distribution and Services. The government sector, insofar as it constitutes public administration financed by tax revenue, is excluded.
[Economists as yet have relatively little evidence concerning the sign of the net cross-price effect in a family model of labor supply. Part of the problem is attributable to a lack of quality data for nonlabor income. In this paper I derive a simple indirect test which does not require accurate estimates of income effects and apply it to data from the National Longitudinal Survey.]
[This paper explores the implications of the additive social welfare function and the max-min criterion on the optimal distributions of labor and income.]
[This is an analytical paper generalizing the standard input-output (IO) model on the one hand and the equally standard method of solving the model by means of power series expansion, on the other. The IO coefficients here are allowed to vary with the endogenous variables in a general fashion, and the model analysis is approached via the iterative scheme, referred to above. The analysis includes (i) detailed and rigorous examination of the properties of the scheme, (ii) clarification of the notion of viability, and (iii) derivation of suitable viability conditions in the present generalized context and their comparison with the corresponding results in the linear model. Finally, the economic interpretation of the computational procedure in terms of a planning process of the "material balance" type is extended to the generalized context. It is shown that this results in a considerable "informational economy" on the part of the central planning authority, which is lacking in the case of the linear model.]
IN REGRESSION ANALYSIS most empirical economists use the well-known Durbin-Watson (DW) procedure [1] to test the hypothesis of no autocorrelation among the disturbances of a linear regression model against the hypothesis of a first-order autocorrelation. The use of this procedure is compromised by the fact that it is a bounds text and, hence, cannot discriminate between the two competing hypotheses for a range of intermediate values of the test statistic. This shortcoming can be eliminated by determining the distribution function [5] for the Durbin-Watson test statistic and enumerating it for a given level of significance and a particular regression matrix. The authors have written a FORTRAN IV program for finding the probability that the DW test statistic is less than the observed value if the null hypothesis of no autocorrelation were true. The program enables the investigator to perform the DW test for either positive or negative correlation by comparing the above probability to a specified level of significance. This procedure provides a conclusive test for first-order autocorrelation. The procedure begins with a transformation of the Durbin-Watson test statistic stated as
DANS CE MODELE, on considere un agent economique seulement. Celui-ci dispose d'un actif de montant c0 et cherche 'a I'affecter au mieux, mettons en totalite sur k placements pendant une periode definie assez courte. Les taux d'interet qui correspondent 'a chaque sorte de placement sont, pour la periode 'a venir en question, aleatoires-objectifs soit (X1, . . ,Xk).1 Designons par (b1,.. ., bk) ou Yk bi = 1 et bi >0 une affectation de co envisagee par cet agent. Le montant d'actif en fin de periode est actuellement aleatoire et donne par l'equation