Knowledge that Transforms

DISCUSSIONS OF IDENTIFICATION of parameters in simultaneous equation econometric models almost invariably assume that data are in the form of aggregative time series, i.e., only one measurement of each variable is available in each time period. This note shows that parameters in a model which is underidentified by the usual rank and order criteria at the aggregative level may be identified when disaggregated data aie available. The argument is presented in terms of a traditional textbook example of an underidentified model which consists of a demand and a supply function for a single commodity that are linear in price, and a market clearing equilibrium equation.

[Global necessary conditions are obtained for a discrete capital version of the putty-clay model first introduced by Johansen [7]. Convergence of the optimal solution to a steady state is discussed for concave utilities. Also, the role of obsolescence is analyzed when utility is linear]

IN A RECENT PAPER Cargill and Meyer [1] use Monte Carlo simulations to examine the behavior of some distributed lag estimators when the lag structure is misspecified. The authors hold that it is not easy to compare the Almon polynomial lag method and OLS analytically if the restrictions imposed by the Almon procedure are not correct. It is our intention to derive analytical expressions for the bias in the Almon estimator. We postulate a linear distributed lag model

THE STABILITY PROPERTIES of a competitive economy are well known-at least under the artificial tatonnement assumption which avoids the problem of who changes prices. This note demonstrates that similar, if not stronger, properties carry over to a monopolistic economywhere price formation is completely explained by the independent optimizing behavior of individual agents. The discussion may be interpreted as that of monopoly as the opposite polar case to that of competition or, equivalently, as that of the stability of the concept of equilibrium presented in an earlier paper [1]. Price adjustment is by individuals, following [3 and 4], through their perceived demand curves, following [2 and 5]. There are n commodities (the last being a numeraire) identified by ownership: agent i is endowed with one unit of commodity i and no other commodity. Each agent (i) chooses a price pi ) 0 for his commodity and a consumption x' e R% , so that a state of the economy is an array (p1. p, x . x), or (p, X). Given some existing state (p, X) each agent (i) first chooses pi to maximize his expected income pixi, where xi is his expected sales, given by his perceived linear demand curve (with negative slope one, say) xi(pi) = x-i + Pi - pi; here Xi = I 5i is the existing aggregate demand. Taking account of the constraint xi < 1 this gives

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.