Forecasting with an adaptive control algorithm
AbstractWe construct a parsimonious model of the U.S. macro economy using a state space representation and recursive estimation. At the core of the estimation procedure is a prediction/correction algorithm based on a recursive least squares estimation with exponential forgetting. The algorithm is a Kalman filter-type update method which minimizes the sum of discounted squared errors. This method reduces the contribution of past errors in the estimate of the current period coefficients and thereby adapts to potential time variation of parameters. The root mean square errors of out-of-sample forecast of the model show improvement over OLS forecasts. One period ahead in-sample forecasts showed better tracking than OLS in-sample forecasts.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Federal Reserve Bank of St. Louis in its series Working Papers with number 1996-009.
Date of creation: 1996
Date of revision:
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Sargent, Thomas J., 1993. "Bounded Rationality in Macroeconomics: The Arne Ryde Memorial Lectures," OUP Catalogue, Oxford University Press, number 9780198288695.
- Goldberger, Arthur S, 1970. "Unbiased Prediction by Recursive Least Squares," Econometrica, Econometric Society, vol. 38(2), pages 367, March.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Anna Xiao).
If references are entirely missing, you can add them using this form.