Optimale Aufteilung des Stichprobenumfangs bei mehreren Merkmalen

Zusammenfassung Es wird gezeigt, daß sich das vonNeyman undTschuprow für ein einziges Merkmal gelöste Problem der optimalen Aufteilung des Stichprobenumfangs auf vorgegebene Schichten auf ein nichtlineares Programm mit linearen Restriktionen und nichtlinearer Zielfunktion zurückführen läßt. Auf Grund der Konvexitätseigenschaft der Zielfunktion ergibt sich, daß das Minimum der Zielfunktion (Minimum der Streuung des Stichprobenmittels ) stets eindeutig ist. Der beiNeyman undTschuprow mögliche Fall, daß sich in derh-ten Schicht ein Stichprobenumfang ergibt, der größer als der Umfang in der Gesamtheit ist, kann hier nicht auftreten.Durch Einführung einer verallgemeinerten Streuung wird das Problem der optimalen Aufteilung bei vorgegebener Schichtung imk-dimensionalen Merkmalsraum aufk Merkmale verallgemeinert. Es wird gezeigt, daß diese verallgemeinerte Streuung (=Zielfunktion eines nichtlinearen Programms) im allgemeinen nicht über dem ganzen konvexen Bereich der zulässigen Lösungen konvex (von unten) ist. Ein Teilbereich des zulässigen Bereichs, über dem die Zielfunktion konvex ist, wird angegeben. Schließlich werden hierzu vergleichende Ergebnisse gebracht.     

Fit performance of several programs used for non-linear least squares problems

Seven computerprograms for non-linear regression or curve fitting problems are compared. The comparison of the programs, running in different computing centra, is restricted to the fit performance. Six model functions are fitted according to the least squares criterion to data series, arising from practical work. The special least squares minimization programs turned out to be better suited for these problems than general optimizing programs.     

A Stochastic Frontier Production Function with Flexible Risk Properties

This paper considers a stochastic frontier production function which has additive, heteroscedastic error structure. The model allows for negative or positive marginal production risks of inputs, as originally proposed by Just and Pope (1978). The technical efficiencies of individual firms in the sample are a function of the levels of the input variables in the stochastic frontier, in addition to the technical inefficiency effects. These are two features of the model which are not exhibited by the commonly used stochastic frontiers with multiplicative error structures.An empirical application is presented using cross-sectional data on Ethiopian peasant farmers. The null hypothesis of no technical inefficiencies of production among these farmers is accepted. Further, the flexible risk models do not fit the data on peasant farmers as well as the traditional stochastic frontier model with multiplicative error structure.     

Taxation and the stock market valuation of capital gains and dividends: Theory and emphirical results

Dividends seem to be more heavily taxed than capital gains. Why then do corporations pay dividends rather than repurchasing shares or retaining earnings? Either corporations are not acting in the interests of shareholders, or else shareholders desire dividends sufficiently for nontax reasons to offset the tax effect.In this paper, we measure the relative valuation of dividends and capital gains in the stock market, using a variant of the capital asset pricing model. We find that dividends are not valued differently systematically from capital gains. This finding is consistent with share price maximization by firms but inconsistent with the fact that most shareholderspay a heavier tax on dividends.We also show that the relative value of dividends provides an indirect measure of a marginal Tobin's q. The measured value of dividends relative to capital gains tends to be higher during prosperous periods, as is consistent with this interpretation. We hope that this time series on a marginal Tobin's q will prove to be useful in forecasting the rate of investment.     

The product elimination function

Historically, major consideration given to product management has focused on research and development or the introductory stage of the product life cycle. The authors present an empirical study delineating the variables to be considered in the product elimination process. More specifically, the elimination process is evaluated under a situation of poor product performance despite a generally viable market. The basic objectives of the study were: to determine the significant variables in the product elimination process of the Small Appliance Industry; to determine the relative importance of the variables; to examine the interaction among variables. The data obtained through personal structured questionnaire interviews were analyzed and provided a ranking of twenty-six variables relevant to the elimination process. Moreover, the Johnson's Hierarchical Clustering Schemes was applied to determine the interaction among variables. The results indicate that profitability and financial variables are most significant in the elimination decision-process. Second, the primary clusters of importance are concerned with market share, market growth rate, consumer awareness, and competitive action.