### Working Papers

** Inference for Factor-MIDAS regression models**
__Abstract:__ Factor-MIDAS regression models are often used to forecast a target variable using common factors extracted from a large panel of
predictors observed at higher frequencies. In the paper, we derive the asymptotic distribution of the factor-MIDAS regression estimator coefficients.
We show that there exists an asymptotic bias because the factors are estimated. However, the fact that factors and their lags are aggregated in a MIDAS
regression model implies that the asymptotic bias depends on both serial and cross-sectional dependence in the idiosyncratic errors of the factor model.
Thus, bias correction is more complicated in this setting. Our second contribution is to propose a bias correction method based on a plug-in version of
the analytical formula we derive. This bias correction can be used in conjunction with asymptotic normal critical values to produce asymptotically valid
inference. Alternatively, we can use a bootstrap method, which is our third contribution.
We show that correcting for bias is important in simulations and in an empirical application to forecasting quarterly U.S. real GDP growth rates using monthly factors.

Poster
**Bootstrap Inference for Group Factor Models, with Sílvia Gonçalves and Benoit Perron**
__Abstract:__ Recently, Andreou et al. (2019) have proposed a test for common factors based on canonical correlations between factors estimated separately from each group.
We propose and theoretically justify a simple bootstrap test that avoids the need to estimate the bias and variance of the canonical correlations explicitly.
We verify these conditions
for a wild bootstrap scheme similar to the one proposed in Goncalves and Perron (2014). We also consider an extension of the wild bootstrap that is
robust to serial and cross-sectional dependence of the idiosyncratic error terms. Simulation experiments show that our bootstrap method leads to rejection rates
closer to the nominal level
in all of our designs compared to the asymptotic framework.