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Combining Forecasts from Nested Models By Todd E. Clark and
Michael W. McCracken |
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Abstract Motivated by the common finding that linear
autoregressive models forecast better than models that incorporate
additional information, this paper presents analytical, Monte Carlo, and
empirical evidence on the effectiveness of combining forecasts from nested
models. In our analytics, the unrestricted model is true, but as the sample
size grows, the DGP converges to the restricted model. This approach
captures the practical reality that the predictive content of variables of
interest is often low. We derive MSE-minimizing weights for combining the
restricted and unrestricted forecasts. In the Monte Carlo and empirical
analysis, we compare the effectiveness of our combination approach against
related alternatives, such as Bayesian estimation. Keywords: Forecast combination, predictability, forecast evaluation Back to top RWP home |
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