High, in effect, is defined in this proposition in either of two ways. Most commonly, it has been taken to mean: high enough to warrant remedial intervention of some sort by the state.4 Recently, however, a growing minority of economists has urged that it be taken to mean instead: high enough to warrant intervention, provided the state can show that rates of return in excess of R1 reflect collusive behavior by the leading firms and not cost advantages which these firms have over their leading rivals.' Both meanings in turn reflect a third: high enough to imply a typical market price closer to PM in Figure lb than to Pc' where PM is the price that would prevail if the leading firms maximized collective, current-period profits and Pc is the price that would prevail if collective, current-period profits approximated zero.6 Proposition 1 rests on a large body of empirical work. Proposition 2, however, does not; nor does it rest on any theoretical analysis. Industrial economists simply have intuited that there is a correspondence between the R1R2 segment in Figure la and the PMP* segment in Figure lb. Are there substantive grounds for the intuition? I argue that there are not. The rates of return that lie along the R1R2 segment are competitive,

Donald R. Chambers, Willard T. Carleton, Donald W. Waldman, A New Approach to Estimation of the Term Structure of Interest Rates, The Journal of Financial and Quantitative Analysis, Vol. 19, No. 3 (Sep., 1984), pp. 233-252

Recent developments in the economics of information emphasize the informational content of prices. We examine the degree to which prices convey information on product quality to uninformed agents. Under perfect competition, we show that a rational expectations equilibrium may not exist. When an equilibrium does exist, the information on quality conveyed by prices depends on the shape of the average cost curves and the relative numbers of informed and uniformed agents.

Abstract ABSTRACT: Earlier attempts to extend cost-volume-profit analyses to stochastic sales behavior are reinterpreted in terms of chance-constrained programming formulations to control for risks of failing to break even. The standard types of breakeven analyses usually employed as deterministic systems in accounting are shown also to apply in a surprisingly general manner to situations where risk or uncertainty in sales demands are present. Ways of extending such analyses to allow for new upper as well as lower limits on volume are developed and interpreted in detail. Suggestions and references are also supplied to indicate how such analyses can be extended further to provide contact with probabilistic programming (linear programming under uncertainty as well as stochastic linear programming and chance-constrained programming) approaches to planning and evaluation of portfolios of projects under combinations of liquidity and payback period constraints.

Abstract ABSTRACT: This paper reports the results of an investigation into the relationships between certain CPA examination candidates' attributes and these subjects' performance for 280 first-time candidates writing the November 1977 and May 1978 examinations in Texas. Findings indicate that scholastic aptitude test scores, accounting GPA, accounting hours completed, school attended, hours of self-study, and completion of a CPA review course have consistently significant associations with examination performance. Attributes lacking significant associations with examination performance include candidates' work experience, age, and completion of an audit course.

This paper proposes the residual-based stochastic predictor as an alternative procedure for obtaining forecasts with a static nonlinear econometric model. This procedure modifies the usual Monte Carlo approach to stochastic simulations of the model in that calculated residuals over the sample period are used as proxies for disturbances instead of random draws from some assumed parametric distribution. In compar-ison with the Monte Carlo predictor, the residual-based should be less sensitive to distributional assumptions concerning disturbances in the system. It is also less demanding computationally. The large-sample asymptotic moments of the residual-based predictor are derived in this paper and compared with those of the Monte Carlo predictor. Both procedures are asymptotically unbiased. In terms of asymptotic mean squared prediction error (AMSPE), the Monte Carlo is efficient relative to the residual-based when the number of replications in the Monte Carlo simulations is large relative to sample size. This order of relative efficiency is reversed, however, when replication and sample sizes are similar. In any event, the amount by which the AMSPE of either predictor exceeds the lower bound for AMSPE is small as a percentage of the lower bound AMSPE when sample and replication sizes are at least of moderate magnitude. The paper also discusses the extension of the residual-based anld Monte Carlo procedures to the estimation of higher order moments and cumulative distribution functions of endogenous variables in the system.