Original Research

The out-of-sample forecasting performance of variable parameter exchange rate models in South Africa

Gilbert Wesso
South African Journal of Business Management | Vol 26, No 2 | a825 | DOI: https://doi.org/10.4102/sajbm.v26i2.825 | © 2018 Gilbert Wesso | This work is licensed under CC Attribution 4.0
Submitted: 15 October 2018 | Published: 30 June 1995

About the author(s)

Gilbert Wesso, Department of Statistics, University of the Western Cape, South Africa

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Abstract

In this article the out-of-sample forecasting performance of exchange rate determination is examined without imposing the restriction that coefficients are fixed over time. Both fixed and variable coefficient versions of conventional structural models are considered, with and without a lagged dependent variable. A Variable Parameter Regression (VPR) technique based on recursive application of the Kalman filter is used to improve the predictive performance of a class oi monetary exchange rate models.

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Crossref Citations

1. Neural Networks and Econometric Methodologies for South African Exchange Rate Forecasting
G R Wesso
Studies in Economics and Econometrics  vol: 20  issue: 3  first page: 21  year: 1996  
doi: 10.1080/03796205.1996.12129098