Page 365 - Special Topic Session (STS) - Volume 3

STS550 Matteo Mogliani
4. Discussion and Conclusion
We proposed a new approach to modeling and forecasting mixed-
frequency regressions (MIDAS) that addresses the issue of simultaneously
estimating and selecting relevant high-frequency predictors in a high-
dimensional environment. Our approach is based on MIDAS regressions
resorting to Almon lag polynomials and an adaptive penalized regression
approach, namely the Group Lasso objective function. The proposed models
rely on Bayesian techniques for estimation and inference. In particular, the
penalty hyper-parameters driving the model shrinkage are automatically
tuned via an Empirical Bayes algorithm based on stochastic approximations.
Simulations show that the proposed models present very good in-sample and
out-of-sample performance. When applied to a forecasting model of U.S. GDP
with high-frequency real and financial predictors, the results suggest that our
models produce significant out-of-sample short-term predictive gains
compared to several alternative models. Further, our findings are broadly in
line with the existing literature, in the extent that high-frequency financial
variables have non-zero, although limited, short-term predictive content.

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