Forgetting Approaches to Improve Forecasting

ABSTRACT
There is widespread evidence of parameter instability in the literature. One way to account for this feature is through the use of time-varying parameter (TVP) models that discount older data in favor of more recent data. This practice is often known as forgetting and can be applied in several different ways. This paper introduces and examines the performance of different (flexible) forgetting methodologies in the context of the Kalman filter. We review and develop the theoretical background and investigate the performance of each methodology in simulations as well as in two empirical forecast exercises using dynamic model averaging (DMA). Specifically, out-of-sample DMA forecasts of Consumer Price Index (CPI) inflation and S&P500 returns obtained using different forgetting approaches are compared. Results show that basing the amount of forgetting on the forecast error does not perform as well as avoiding instability by placing bounds on the parameter covariance matrix.

WITH
Robert A. Hill, Nova School of Business and Economics, Lisbon, Portugal

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About Authors

Paulo Rodrigues

Nova Faculty Affiliate

Paulo M. M. Rodrigues obtained his PhD in Econometrics from the School of Economic Studies of the University of Manchester. He is a professor of economics at Nova School of Business and Economics, a senior research economist at the Bank of Portugal, and a fellow of both the Clive Granger Centre for Time Series Analysis of the University of Nottingham and the University of Essex´s Centre for Financial Econometrics.