Macroeconomic forecasting using model averaging
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Recently, there has been a broadening concern on forecasting techniques that are
applied on large data sets, since economists in business and management want to
deal with the great magnitude of information. In this analysis, the issue of forecasting
a large data set by using different model averaging approaches is addressed.
In particular, Bayesian and frequentist model averaging methods are considered,
including Bayesian model averaging (BMA), information theoretic model averaging
(ITMA) and predictive likelihood model averaging (PLMA). The predictive performance
of each scheme is compared with the most promising existing alternatives,
namely benchmark AR model and the equal weighted model averaging (AV) scheme.
An empirical application on Inflation forecasting for five countries using large
data sets within the model averaging framework is applied. The average ARX model
with weights constructed differently according to each model averaging scheme is
compared with both the benchmark AR and the AV model. For the comparison
of the accuracy of forecasts several performance indicators have been provided such
as the Root Mean Square Error (RMSE), the Mean Absolute Error (MAE), the
U-Theil’s Inequality Coefficient (U), Mean Square Forecast Error (MSFE) and the
Relative Mean Square Forecast Error (RMSFE). Next, within the Granger causality
framework through the Diebold & Mariano (DM) test and the Clark & McCracken
(CM) test, whether the data-rich models represented by the three different model
averaging schemes have made a statistically significant improvement relative to the
benchmark forecasts has been tested. Critical values at 5% and at 10% have been
calculated based on bootstrap approximation of the finite sample distribution of the
DM and CM test statistics.
The main outcome is that although the information theoretic model averaging
scheme is a more powerful approach, the other two model averaging techniques can
be regarded as useful alternatives.
Authors
Verra, ChristinaCollections
- Theses [3831]