Combination of theoretical models for exchange rate forecasting
This paper states that there are exchange rate forecasting gains when combining in-sample data from different models based on economic theory. Data combination is performed using Bayesian model averaging (BMA). Using pooled data by group of countries (developed and emerging economies) generates acc...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Universidad Nacional de Colombia
2024-10-01
|
| Series: | Cuadernos de Economía |
| Subjects: | |
| Online Access: | https://revistas.unal.edu.co/index.php/ceconomia/article/view/98393 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | This paper states that there are exchange rate forecasting gains when combining in-sample data from different models based on economic theory. Data combination is performed using Bayesian model averaging (BMA). Using pooled data by group of countries (developed and emerging economies) generates accuracy gains in an important amount of cases, with respect to forecasts that use country information.
Gains are larger for currencies of developed economies, but accuracy decreases as the forecast horizon is extended. BMA models for developed countries tend to be more “sparse” than emerging countries models
|
|---|---|
| ISSN: | 0121-4772 2248-4337 |