Stability Analysis and Forecasting Implications

Forecasting with unstable relationships under the assumption that they are constant, might cause inefficient forecasts. Thus, before embarking upon any forecast exercise, it is convenient to first verify the sequential significance of the relationship, and then its stability. This paper studies recursive and rolling estimators, and related sequential tests to test for significant and constant coefficients. If constancy is rejected, the estimations may suggest dynamic models, that jointly with the considered model, could improve the results of the forecasting exercise. These procedures are applied to the price volume relationship in the stock market as postulated in Campbell, Grossman and Wang (1993). The significance but non stability of the relationship is shown. There are gains in the forecast performance when considering the empirical model for the rolling estimators, jointly with the initial structural model. Key Words: Recursive Estimators, Rolling Estimators, Recursive Sequential Test, Monte Carlo Methods, Campbell-Grossman-Wang model.