THE DRAMATIC INCREASE in energy prices in the 1970s has stimulated interest in the effect of energy prices on the demands for various factors of production. In analyzing the choice of energy-using characteristics of capital, it is important to recognize that a firm's energy-capital ratio is much more flexible prior to undertaking a capital investment than it is after the capital is put in place. In this paper we examine factor intensity choices in a stochastic putty-clay model. Ex ante, when the firm is making investment decisions, the price of energy is unknown. The energy/capital ratio is flexible ex ante and the firm chooses the optimal energy intensity based on the probability distribution of energy prices. Ex post, the energy/capital ratio is fixed and the price of energy is known. The firm cannot adjust the energy/capital ratio but can choose not to use its capital if the realized price of energy is too high.2 Recently, Kon [3] has studied factor demands in a stochastic putty-clay model in which the price of output is random and the prices of factors of production are known with certainty. In this paper, we focus on the effects of energy prices and thus treat factor prices as random and the output price as known with certainty. This difference in the source of randomness appears to make little difference in the comparison of optimal factor intensity under certainty and under uncertainty; indeed Proposition 1 in this paper corresponds to Kon's Proposition 1. In this paper we then go on to examine the effects on optimal factor intensity of changes in the mean and variance of the price of energy.3 The option to shut down during unfavorable price regimes plays an important role in our analysis. In Section 1 we develop a stochastic putty-clay model and compare a risk-neutral firm's behavior under certainty and uncertainty. The effects on energy-intensity of changes in the mean and variance of energy prices are analyzed in Section 2.

This article proposes a new approach to small sample theory that achieves a meaningful integration of earlier directions of research in this field. The approach centers on the constructive technique of approximating distributions developed recently by the author in [10]. This technique utilizes extended rational approximants (ERA's) which build on the strengths of alternative, less flexible approximation methods (such as those based on asymptotic expansions) and which simultaneously blend information from diverse analytic, numerical and experimental sources. The first part of the article explores the general theory of approximation of continuous probability distributions by means of ERA's. Existence, characterization, error bound, and uniqueness theorems for these approximants are given and a new proof is provided for the convergence result obtained earlier in [10]. Some further aspects of finding ERA's by modifications to multiple-point Pade approximants are presented and the new approach is applied to the noncircular serial correlation coefficient. The results of this application demonstrate how ERA's provide systematic improvements over Edgeworth and saddlepoint techniques. These results, taken with those of the earlier article [10], suggest that the approach offers considerable potential for empirical application in terms of its reliability, convenience, and generality.

[When a principal with private information designs a mechanism to coordinate his subordinates, he faces a dilemma: to conceal his information, his selection of mechanism must not depend on his information; but his information may influence which mechanism he prefers. To resolve this dilemma, this paper develops a theory of inscrutable mechanism selection. The principal's neutral optima are defined as the smallest possible set of unblocked mechanisms. They are shown to exist and are characterized using parametric linear programs. Any safe and undominated mechanism is a neutral optimum. Any neutral optimum is an expectional equilibrium and a core mechanism.]

We compare six concepts of efficiency for economies with incomplete information, depending on the stage at which individuals' welfare is evaluated and on whether incentive constraints are recognized. An example is shown in which an incentive-efficient decision rule may be unanimously rejected by the individuals in the economy. We define durable decision rules, which can resist such unanimous rejection, and show that efficient durable decision rules exist.

[This paper characterizes a market economy with infinitely long-lived consumers, and value-maximizing firms which face costs of adjustment for capital. The temporary equilibrium of this economy is similar to the short-run equilibrium of standard macroeconomic models. Consumption is a function of wealth, investment is related to the value of firms; equilibrium between aggregate demand and aggregate supply is achieved by the endogenous adjustment of the sequence of current and future interest rates. The dynamic behavior of output, consumption, and investment in this economy is the same as in an optimal growth model with adjustment costs. The paper shows this equivalence and then uses it, together with the equivalence of taxes to technological shocks, to study the dynamic effects of fiscal policy.]

A solution method and an estimation method for nonlinear rational expectations models are presented in this paper.The solution method can be used in forecasting and policy applications and can handle models with serial correlation and multiple viewpoint dates.When applied to linear models, the solution method yields the same results as those obtained from currently available methods that are designed specifically for linear models.It is, however, more flexible and general than these methods.The estimation method is based on the maximum likelihood principal.It is, as far as we know, the only method available for obtaining maximum likelihood estimates for nonlinear rational expectations models.The method has the advantage of being applicable to a wide range of models, including, as a special case, linear models.The method can also handle different assumptions about the expectations of the exogenous variables, something which is not true of currently available approaches to linear models.