Best Linear Prediction in Cointegrated Systems
Essays in Honor of Joon Y. Park: Econometric Theory
ISBN: 978-1-83753-209-4, eISBN: 978-1-83753-208-7
Publication date: 24 April 2023
Abstract
This chapter introduces the best linear predictor (BLP) with the asymptotic minimum mean squared forecasting error (MSFE) among linear predictors of variables in cointegrated systems. Accordingly, the authors show that (i) if the autocorrelation coefficient of the cointegration error between the prediction time and the predicted targeting time is larger than ½ (representing a short prediction period), then the BLP is deduced from the random walk model; and (ii) in other cases (representing a long prediction period), the BLP is deduced from the cointegration model. Under this scheme, we suggest a switching predictor that automatically selects the random walk or cointegration model according to the size of the estimated autocorrelation coefficient. These results effectively explain the superiority reversal in the short- and long-term prediction of the exchange rate between the random walk and the structural/cointegration model (known as the Meese–Rogoff or disconnect puzzle).
Keywords
Acknowledgements
Acknowledgments
I thank God for knowing everything and leading me to the Navotas Charity Foundation (http://navotas.or.kr/) and confess that I have received all grace through it. I appreciate the comments of the editor, Zack Miller, and an anonymous referee. However, all remaining errors belong to me as the author. Lastly, I am especially grateful for the loving guidance of Professor Joon Y. Park, who was a member of my PhD committee.
Citation
Kim, Y.-Y. (2023), "Best Linear Prediction in Cointegrated Systems", Chang, Y., Lee, S. and Miller, J.I. (Ed.) Essays in Honor of Joon Y. Park: Econometric Theory (Advances in Econometrics, Vol. 45A), Emerald Publishing Limited, Leeds, pp. 367-391. https://doi.org/10.1108/S0731-90532023000045A013
Publisher
:Emerald Publishing Limited
Copyright © 2023 Yun-Yeong Kim