Research — Qiyang Yu

Job Market Paper

Spillovers Under Common Shocks in Panel Data: Estimation and Inference

Abstract. Economic outcomes reflect both private effects and spillovers across firms, industries, or regions. Distinguishing these effects is difficult when interaction networks are unknown and unobserved common shocks affect both outcomes and covariates. A high-dimensional spillover network must then be distinguished from co-movement generated by common shocks. This paper develops estimation and inference for bilateral spillover and common private effects with unknown networks and unobserved common shocks. Unobserved common shocks affect outcomes through interactive fixed effects. Identification exploits a factor structure in covariates and exogenous idiosyncratic variation under sparsity conditions. Combining factor projection with debiased LASSO and post-selection OLS, we establish asymptotic normality and simultaneous spillover inference while accounting for factor-estimation and regularization errors. An application to industry-level R&D in the United States illustrates the importance of accounting for common shocks: the number of significant spillover links falls by 42%, and the estimated private R&D elasticity declines and becomes statistically insignificant.

Published Papers

Estimation and Inference for CP Tensor Factor Models

with Bin Chen and Yuefeng Han

Journal of Econometrics, 2026

Journal Preprint

Diffusion Index Forecasting with Tensor Data

with Bin Chen and Yuefeng Han

Journal of Econometrics, 2026

Journal Preprint

Working Papers

Useful Factors Are Fewer Than You Think

with Bin Chen and Guofu Zhou

Paper

Work in Progress

Semiparametric Inference in Panel Data with Interactive and High-dimensional Confounding

with Bin Chen and Yukun Ma

We develop Factor-Augmented Double/Debiased Machine Learning (FA-DML) for treatment-effect inference in panels with high-dimensional covariates and latent interactive confounders. A factor-orthogonal score combines machine learning, factor estimation, and block cross-fitting to deliver valid inference, illustrated through bank branching deregulation and income inequality.

Missing Imputation of CP Factor Model

with Bin Chen and Yuefeng Han

We are developing a CP tensor method built on the CC-ISO algorithm by Chen et al. (2026) for completing tensor-valued time series with missing observations.

Asset Pricing with a Latent Network

with Qixiang Gao

We develop a model and estimation algorithm to recover characteristic networks and their risk premia from large asset panels. We apply the method to U.S. stocks to study the cross-sectional pricing of characteristic networks.