Where Are Saskatchewan Farmland Prices Headed?

An adaptive expectation model is employed to estimate Saskatchewan farmland values based on land rent. The estimated model is then used to forecast future agricultural land values, given a number of commodity price scenarios. The model illustrates the importance of expectations in the farmland purchase decision. Furthermore, it explains why swings in land prices tend to lag behind what current financial conditions would suggest. Nous avons utilisé un modèle adaptable de détermination des attentes pour déterminer la valeur des terres agricoles de la Saskatchewan à partir du prix de location. Le modèle estimatif est ensuite utilisé pour prévoir la valeur future des terres agricoles, compte tenu ?un certain nombre de scénarios ?évolution des prix des produits. Le modèle montre ?importance des attentes des gens dans les décisions ?achat de terres agricoles. Il explique en outre pourquoi les variations observérs dans les prix des terres ont tendance à marquer un certain retard par rapport à ce que les conditions financières existantes devraient laisser prévoir.     

Een criterium voor het kiezen van een proefopzet

A characterization of D-optimality is given together with several examples where D-optimal designs are computed.     

Dichotomous decisions based on dichotomously scored items: a case study

In a course in elementary statistics for psychology students using criterion-referenced achievement tests, the total test score, based on dichotomously scored items, was used for classifying students into those who passed and those who failed. The score on a test is considered as depending on a latent variable; it is assumed that the students can be dichotomized into the categories “mastery” (with scores on the latent variable above a cutting score), and “no mastery” (with scores below the cutting score on the latent variable). Two problems are considered: (a) How many students are classified incorrectly? Using the binomial error model a procedure is described for computing the classification proportions: p(mastery, passed), p(mastery, failed), p(no mastery, passed), and p(no mastery, failed), (b) What is the optimal cutting score on a test? Using a loss function a procedure for computing the optimal curring score is described.