In this paper forecasting the production volume of the Swedish and Finnish metal and engineering industries one quarter ahead is discussed. A practical way of making use of the predictive information in the answers of the quarterly business survey is presented which is based on the application of the Kalman filter. It is found that the most informative questions of the business survey from the point of view of forecasting are different in the two countries. However, for both Sweden and Finland, the improvement in prediction accuracy after taking account of relevant business survey information is significant when the precision of autoprojective forecasts is used as a baseline.

Let vk(x) be the force of mortality when only risk Rk is acting in a population, and let muk(x) be the force of mortality due to Rk when all of the risks of death are acting, k = 1, ---, c. On the assumption of the independence of the risks of death, the sum of intensitives vk(x) equals the total force of mortality. This example shows that the equality can also hold when the risks of death are dependent and the forces of mortality, muk(x) and vk(x), differ.

Business survey data have recently been used in several studies to shed light on the formation of firms' plans and expectations. The main tool for analysis has been the multivariate conditional log‐linear model. The trichotomous answers in a typical business survey have an ordering. This makes it possible to analyse firms' production decisions with a categorized version of Cox's proportional hazards model. In this paper the production decisions of Finnish firms are investigated using data from the Finnish business survey. The results obtained are largely in accord with the implications of the production smoothing model of Blinder (1982).