Expectations,substitution, and scrapping in a putty-clay model

The paper presents a framework for analyzing the effect of changing expectations about future prices on a firm's choice of technique, and on its anticipated scrapping of capital equipment. Assuming a putty-clay technology, particular attention is paid to the way in which the scrapping age depends on the degree of ex ante input substitution. Numerical illustrations — based on data for Norwegian manufacturing for the years 1964–1983, an ex ante technology represented by a Generalized Leontief cost function in materials, energy, labor, and capital, and an ARMA representation of the price expectation mechanism — are presented. The results indicate that the price changes in this period may have had a substantial impact on planned scrapping, and on the chosen production techniques.     

Wealth and sustainable development: the role of David Pearce

Green net national product was thought to be one way of measuring sustainable income. However, David Pearce understood that a better measure of sustainable development was to look at what a generation is leaving in form of capital assets to later generations. In this article, his arguments and insights are highlighted against recent theoretical advances.     

Timing of Public Information and Cost Reduction

Received July 31, 2000; revised version received July 18, 2001     

Disequilibrium dynamics in a simple macroeconomic model

The dynamic behavior of a simple macroeconomic disequilibrium model is analyzed in which consumers' changes in money holdings constitute the dynamic link between any two periods. It is shown that, under constant government consumption, a constant production function (no investment), and fixed prices and wages, stationary states of Keynesian unemployment are stable whereas those of repressed inflation are globally unstable. Possibilities of unemployment and output cycles are indicated for fixed wages as well as for some very simple class of wage and price adjustment mechanisms.     

MSE-improvement of the least squares estimator by dropping variables

It is well known that dropping variables in regression analysis decreases the variance of the least squares (LS) estimator of the remaining parameters. However, after elimination estimates of these parameters are biased, if the full model is correct. In his recent paper, Boscher (1991) showed that the LS-estimator in the special case of a mean shift model (cf. Cook and Weisberg, 1982) which assumes no “outliers” can be considered in the framework of a linear regression model where some variables are deleted. He derived conditions under which this estimator outperforms the LS-estimator of the full model in terms of the mean squared error (MSE)-matrix criterion. We demonstrate that this approach can be extended to the general set-up of dropping variables. Necessary and sufficient conditions for the MSE-matrix superiority of the LS-estimator in the reduced model over that in the full model are derived. We also provide a uniformly most powerful F-statistic for testing the MSE-improvement.