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Model selection and model averaging are popular approaches for handling modeling uncertainties. The existing literature offers a unified framework for variable selection via penalized likelihood and the tuning parameter selection is vital for consistent selection and optimal estimation. Few...
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Based on a set of carefully designed Monte Carlo exercises, this paper documents the behavior and performance of several newly developed advanced forecast combination algorithms in unstable environments, where performance of candidate forecasts are cross-sectionally heterogeneous and dynamically...
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