6 papers
Sparse Covariance Neural Networks
Andrea Cavallo, Zhan Gao, Elvin Isufi
Covariance Neural Networks (VNNs) perform graph convolutions on the covariance matrix of input data to leverage correlation information as pairwise connections. They have achieved…
LASSO Inference for High Dimensional Predictive Regressions
Zhan Gao, Ji Hyung Lee, Ziwei Mei +1
LASSO inflicts shrinkage bias on estimated coefficients, which undermines asymptotic normality and invalidates standard inferential procedures based on the t-statistic. Given cross…
Limited-Memory Greedy Quasi-Newton Method with Non-asymptotic Superlinear Convergence Rate
Zhan Gao, Aryan Mokhtari, Alec Koppel
Non-asymptotic convergence analysis of quasi-Newton methods has gained attention with a landmark result establishing an explicit local superlinear rate of O. The…
Identification and Estimation of Categorical Random Coefficient Models
Zhan Gao, M. Hashem Pesaran
This paper proposes a linear categorical random coefficient model, in which the random coefficients follow parametric categorical distributions. The distributional parameters are i…
On LASSO for Predictive Regression
Ji Hyung Lee, Zhentao Shi, Zhan Gao
Explanatory variables in a predictive regression typically exhibit low signal strength and various degrees of persistence. Variable selection in such a context is of great importan…
Implementing Convex Optimization in R: Two Econometric Examples
Zhan Gao, Zhentao Shi
Economists specify high-dimensional models to address heterogeneity in empirical studies with complex big data. Estimation of these models calls for optimization techniques to hand…