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Combination of theoretical models for exchange rate forecasting
Combinación de modelos teóricos para el pronóstico de tipo de cambio
DOI:
https://doi.org/10.15446/cuad.econ.v43n92.98393Palabras clave:
Bayesian Model Averaging, exchange rate, forecasting, model uncertainty. (en)promedio modelo bayesiano; tipo de cambio; pronósticos macroeconómicos; incertidumbre del modelo. (es)
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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
Este artículo propone la existencia ganancias en la predicción de tipo de cambio cuando se combinan datos in-sample de diferentes modelos basados en la teoría económica. La combinación se realiza mediante Bayesian Model Averaging. Entrenar el modelo con información de otras economías genera ganancias de precisión en una cantidad importante de casos, respecto a pronósticos que utilizan solo información del país. Mayores ganancias de precisión se encuentran para divisas de economías desarrolladas. Los modelos entrenados para países desarrollados tienden a ser más “escasos” que los modelos de países emergentes
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