### 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:__ Andreou et al. (2019) have proposed a test for common factors based on canonical correlations between factors
estimated separately from each group. We propose a simple bootstrap test that avoids the need to estimate the bias and variance of
the canonical correlations explicitly and provide high-level conditions for its validity. We verify these conditions for a wild bootstrap scheme
similar to the one proposed in Gonçalves and Perron (2014). Simulation experiments show that this bootstrap approach leads to null rejection rates
closer to the nominal level in all of our designs compared to the asymptotic framework.