arXiv subjects

Chengwang Liao

Publications and source records attributed to Chengwang Liao.

3 recordsLinked to original sources

arXiv2026

Cointegrated Regressors: Where Folded Concave Penalties Go Beyond LASSO

LASSO is one of the most popular regularization methods for high-dimensional regression models. While cointegration is common in practical applications of predictive regressions with many regressors, the convex penalty of LASSO overly suppresses the coefficients of cointegrated regressors and therefore fails to deliver consistent parameter estimation. We employ folded concave penalties (FCP), including the well-known SCAD and MCP, to address this issue. Using FCP, we establish two asymptotic results: (i) a local oracle property for the penalized regression, and (ii) a global oracle property for regressions with a penalty-level constraint. In particular, the latter guarantees a global solution that consistently estimates the true coefficients and enjoys oracle prediction performance. Simulation studies and an application to predicting the euro area unemployment rate show that FCP improves variable selection and out-of-sample prediction relative to LASSO.

stat.ME↗

arXiv2026

Nickell Meets Stambaugh: A Tale of Two Biases in Panel Predictive Regressions

In panel predictive regressions with persistent covariates, coexistence of the Nickell bias and the Stambaugh bias imposes challenges for estimation and hypothesis testing. This paper introduces an innovative estimator, the Double IVX (DIVX), inspired by the IVX technique in time series. DIVX effectively removes this composite Nickell-Stambaugh bias and reinstates standard inferential procedures based on the t-statistic. This new procedure achieves unified inference across a wide range of modes of persistence in panel predictive regressions when the cross-sectional dimension and the time dimension are comparably large. Such desirable properties were unattainable by existing methods, including the popular within-group estimator. Extensive Monte Carlo simulations demonstrate the robustness of DIVX under a variety of settings. We apply DIVX to panel data of financial markets in developed economies to examine the predictability of stock returns.

econ.EM↗

arXiv2026

A Relaxation Approach to Synthetic Control

The synthetic control method (SCM) is widely used for constructing the counterfactual of a treated unit based on data from control units in a donor pool. Allowing the donor pool contains more control units than time periods, we propose a novel machine learning algorithm, named SCM-relaxation, for counterfactual prediction. Our relaxation approach minimizes an information-theoretic measure of the weights subject to a set of relaxed linear inequality constraints in addition to the simplex constraint. When the donor pool exhibits a group structure, SCM-relaxation approximates the equal weights within each group to diversify the prediction risk. Asymptotically, the proposed estimator achieves oracle performance in terms of out-of-sample prediction accuracy. We demonstrate our method by Monte Carlo simulations and by an empirical application that assesses the economic impact of Brexit on the United Kingdom's real GDP.

econ.EM↗