On the Application of Short-term Causal Models

Abstract: Causal (econometric) models are often employed as instruments for for-casting in short-term planning within organizations. The aim of this work is to contribute to an understanding of the short-term aspects of causal model building. The interest focuses mainly on the adjustment problem (as one of a number of data problems) and the problem of auto-correlated disturbances. The general considerations for and against trend and seasonal adjustment in a short-term context are discussed. Furthermore different principles for chosing an appropriate adjustment technique are considered. Imperfections in data and operations performed on the original data, e.g. different adjustments, are some of the causes of auto-correlated disturbances. A multivariate ARMA-process is proposed as one means of taking account of the autocorrelation. An estimation procedure is suggested for recursive systems, the consistency of the estimator is proved and the procedure is applied to an empirical situation: a model for the pulp and paper industry in the United States. Different specifications of the disturbances are compared in the application with regard to fit in observation period and forecasting. Although the model with a multivariate ARMA-specification in disturbances gives a better fit than its alternatives it does not provide better forecasts in a period subsequent to the observation period.