Amedeo Andriollo
AP of Finance
Université Paris Dauphine - PSL
Research
Working Papers
Causality versus Serial Correlation: an Asymmetric Portmanteau Test
Abstract
This paper studies specification testing in dynamic linear models in the presence of omitted variables. The null hypothesis of interest is weak exogeneity: structural shocks have zero conditional expectation given their own past and the past of omitted variables. Existing tests based on quadratic forms of serial cross-correlations suffer from size distortions because their variance incorporates symmetric dependence in both directions, including causality from past shocks to present omitted variables (inverse causality). This paper proposes a correction that offsets the contribution of inverse causality, yielding an asymmetric Portmanteau statistic that is asymptotically normal under the null, without requiring parametric modeling of the joint dynamics. An empirical application revisits Diercks et al. (2024) and rejects weak exogeneity of Baker et al. (2016)'s EPU shocks. Addressing this failure by augmenting the information set with additional controls leads to a positive inflation response, pointing to a supply-side shock interpretation.
Misspecification and Weak Identification in the Nontraded Factor Zoo
Abstract
We study the size and power properties of t-tests of parameter restrictions for newly-designed methods that aim at reliably estimating risk premia in linear asset pricing models when the crosssectional dimension is large. By simulating a variety of empirically calibrated data generating processes for sample sizes that are typically encountered in empirical work, we evaluate the finitesample performance of the test statistics for scenarios where the factor structure is (i) strong and pervasive; (ii) spurious; (iii) weak/semi-strong and pervasive; (iv) weak/semi-strong and not pervasive; and (v) sparse. PCA-based methods such as those of Lettau and Pelger (2020), Giglio and Xiu (2021), and Giglio et al. (2025) work best when the factors are strong and pervasive, and they continue to exhibit good finite-sample properties when the factors are spurious. However, when the factor structure is semi-strong and pervasive, the split-sample estimator of Anatolyev and Mikusheva (2021) performs substantially better than the PCA-based estimators listed above. In the case of sparse loadings or when the factors are semi-strong and not pervasive, none of the candidate methods displays satisfactory finite-sample properties.
On the Statistical Properties of Tests of Parameter Restrictions in Beta-Pricing Models with a Large Number of Assets
Abstract
We study the size and power properties of t-tests of parameter restrictions for newly-designed methods that aim at reliably estimating risk premia in linear asset pricing models when the crosssectional dimension is large. By simulating a variety of empirically calibrated data generating processes for sample sizes that are typically encountered in empirical work, we evaluate the finitesample performance of the test statistics for scenarios where the factor structure is (i) strong and pervasive; (ii) spurious; (iii) weak/semi-strong and pervasive; (iv) weak/semi-strong and not pervasive; and (v) sparse. PCA-based methods such as those of Lettau and Pelger (2020), Giglio and Xiu (2021), and Giglio et al. (2025) work best when the factors are strong and pervasive, and they continue to exhibit good finite-sample properties when the factors are spurious. However, when the factor structure is semi-strong and pervasive, the split-sample estimator of Anatolyev and Mikusheva (2021) performs substantially better than the PCA-based estimators listed above. In the case of sparse loadings or when the factors are semi-strong and not pervasive, none of the candidate methods displays satisfactory finite-sample properties.