My research develops econometric and statistical methods for financial, economic, and environmental data. A central theme is robust inference and real-time monitoring for dependent data, including Adjusted-Range Self-Normalisation, long-run variance inference, structural-change detection, functional time series, factor models, and distributional Granger causality.
On the applied side, my work connects these methods to risk monitoring in financial derivatives and cryptocurrency markets, machine-learning-based measurement of digital-finance competitiveness, and environmental management and ecological economics, including virtual water in global supply chains. Across these projects, I aim to link econometric theory with practical tools for empirical research, regulation, and teaching.
Selected working papers and ongoing research projects are listed below.
Using city-specific synthetic controls for 99 prefectures (2004–2017), this paper studies the 2013 Yangtze River Delta enlargement and documents a ‘Winner-Takes-More’ pattern with limited cross-boundary spillovers.
This paper analyses China’s Hukou system as a membership architecture in which formal status assigns claims, while administrative gateways determine whether those claims become usable entitlements.
This working paper develops a unified framework for online monitoring of distributional Granger causality using self-normalized score detectors and anytime-valid quantile e-processes, with an empirical application to BTC downside-risk monitoring using Deribit option signals.
This working paper develops tuning-lean, self-normalized sequential monitors based on discrete Fourier transform (DFT) spectral scores to detect factor-loading instability in large approximate factor models, delivering stable false-alarm control and faster detection of structural change.
This working paper develops adjusted-range self-normalized inference for relevant change-point detection in weakly dependent functional time series, delivering tuning-free procedures and an empirical application to Bitcoin options implied-volatility smiles.
This working paper develops an affine-equivariant multivariate adjusted-range self-normalizer for pivotal joint inference without long-run covariance estimation, bandwidth selection, or diagonalization.
This working paper uses Survey of Professional Forecasters GDP-density forecasts to study whether same-target revisions in downside and upside tail probabilities predict growth-relative return spreads.
This working paper studies how migration-control institutions shape post-migration job quality, using China’s hukou system and survey evidence from non-local-hukou workers in 12 destination labor markets.
This working paper proposes an adjusted-range self-normalized monitoring scheme (RSMS) for online structural-change detection in time series, avoiding kernel, bandwidth, and block-length choices.
This working paper treats political turnover as a stress test for innovation-system resilience, using R&D and patent indicators for 45 economies from 1980 to 2020.
This working paper proposes a nonparametric test of whether a candidate state variable can be omitted from an interest-rate conditional variance function after conditioning on retained state variables.
This working paper studies online model-maintenance rules for functional representations, comparing HAC, Shao-type self-normalization, and adjusted-range self-normalization after training-anchored FPCA compression.
This working paper applies GLMY path homology to transaction-level Bitcoin and Ethereum options data, measuring higher-order directed topology in implied-volatility spillovers and signed trading-pressure networks.
This working paper develops a mathematical-sciences framework for trustworthy AI corpora, organizing corpus trustworthiness around representativeness, consistency, fairness, privacy, and robustness across the lifecycle of collection, cleaning, augmentation, and auditing.
Revise and resubmit at the Journal of Financial Econometrics. This working paper develops tuning-free, self-normalized tests for economically relevant functional Granger causality in Hilbert-space time series, with an application to volatility spillovers between Bitcoin options and spot markets.