It is shown that cardinal value allocations may fail to be symmetric. Specifically, agents with identical preferences and identical endowments can be treated very differently at a cardinal value allocation. This casts further doubt on the interpretation of the weights as endogenous utility comparisons.
[Many distributions have been used as descriptive models for the size distribution of income. This paper considers two generalized beta distributions which include many of these models as special or limiting cases. These generalized distributions have not been used as models for the distribution of income and provide a unified method of comparing many models previously considered. Expressions are reported which facilitate parameter estimation and the analysis of associated means, variances, and various measures of inequality. The distributions considered are fit to U.S. family income and their relative performance is compared.]
Recent work in game theory has shown that, in principle, it may be possible for firms in an industry to form a self-policing cartel to maximize their joint profits. This paper examines the nature of cartel self-enforcement in the presence of demand uncertainty. A model of a noncooperatively supported cartel is presented, and the aspects of industry structure which would make such a cartel viable are discussed.
[Results associated with Neumann-type economic growth models are described. We present a new generalization of the Neumann model with the idea of explaining and unifying certain basic results obtained by M. Morishima and J. L̵os̄ in proving general existence theorems. The idea of replacing constant input and output matrices in the Neumann model by those which depend continuously on the growth rate and on the price vector suggested and developed by Morishima, is coupled with an asymmetric-type generalization of the original Neumann model, developed by J. L̵os̄. Consequently, many of our results resemble and partly replace those obtained in both model generalizations.]
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.
[This paper develops a unified framework for formulating econometric models of discrete/continuous consumer choices in which the discrete and continuous choices both flow from the same underlying (random) utility maximization decision. As a special case a number of models suitable for empirical application are developed where the discrete choice is among different brands of a commodity. Since these brands are essentially substitutes, the consumer prefers to buy only one brand at any time; discrete choice is which brand to select and the continuous choice is how many units to buy.]
The paper uses a generalized Nash bargain to analyze input levels, profits, and wages in the absence of binding contracts, and compares these with the convenitional binding contracts model. It is shown that if the union has any power, investment is lower in the absence of binding contracts. The associated input levels and shareholders' profits are identical to those that emerge if contracts are binding and the firm acts as if it faces a cost of capital which is a linear combination of the purchase price of capital and the resale value. This implicit cost is greater than the purchase price, is an increasing function of union power, and is independent of the profit function and the alternative wage. Increases in union power reduce shareholders' profits but may increase wages at some points and decrease wages at others. In the absence of binding contracts, shareholders' profits are lower but there is a critical level of union power (depending on the profit function) such that the union is worse off if its power is higher than this level and better off if it is lower.
This paper focuses on developing and adapting statistical models of counts (nonnegative integers) in the context of panel data and using them to analyze the relationship between patents and R & D expenditures. Since a variety of other economic data come in the form of repeated counts of some individual actions or events, the methodology should have wide applications. The statistical models we develop are applications and generalizations of the Poisson distribution. Two important issues are (i) Given the panel nature of our data, how can we allow for separate persistent individual (fixed or random) effects? (ii) How does one introduce the equivalent of disturbances-in-the-equation into the analysis of Poisson and other discrete probability functions? The first problem is solved by conditioning on the total sum of outcomes over the observed years, while the second problem is solved by introducing an additional source of randomness, allowing the Poisson parameter to be itself randomly distributed, and compounding the two distributions. Lastly, we develop a test statistic for the presence of serial correlation when fixed effects estimators are used in nonlinear conditional models.