科学研究

Yi He is an assistant professor from University of Amsterdam. He got his Ph.D. in finance from Tilburg University in 2016. The main research fields are multivariate extreme value statistics, high-dimensional measurement and random matrix theory. His research hase been published in top journals such as Journal of the Royal Statistical Society - Series B, Annals of Statistics, Journal of Business & Economic Statistics.

We develop a powerful quadratic test for the overall significance of many weak exogenous variables in a dense autoregressive model. By equally weighting the sample moments, the test is asymptotically correct in high dimensions even when the number of coefficients is larger than the sample size. Our theory allows a non-parametric error distribution and the estimation of the autoregressive coefficients. Using random matrix theory, we show that the test has the optimal asymptotic testing power among a large class of competitors against local dense alternatives whose direction is free in the eigenbasis of the sample covariance matrix among regressors. The asymptotic results are adaptive to the predictors’ cross-sectional and temporal dependence structure and do not require a limiting spectral law of their sample covariance matrix. The method extends to general nuisance variables beyond autoregressors, and we give a robust modification for irregular scenarios. Monte Carlo studies suggest a good power performance of our proposed test against high dimensional dense alternative for various data generating processes. We apply the test to detect the significance of over one hundred exogenous variables in the FRED-MD database for predicting the monthly growth in the US industrial production index.