7 citations · 11 across the 7 of their papers we have counts for
9 papers
Cointegrated Regressors: Where Folded Concave Penalties Go Beyond LASSO
Hongqi Chen, Chengwang Liao, Ziwei Mei +1
LASSO is one of the most popular regularization methods for high-dimensional regression models. While cointegration is common in practical applications of predictive regressions wi…
Identification and Robust Inference for Multiple Treatments with Possibly Invalid Instruments
Ziwei Mei, Qingliang Fan, Zijian Guo
The instrumental variable (IV) method is widely used to infer causal effects in observational studies with unmeasured confounding, but invalid instruments can compromise both popul…
Nickell Meets Stambaugh: A Tale of Two Biases in Panel Predictive Regressions
Chengwang Liao, Ziwei Mei, Zhentao Shi
In panel predictive regressions with persistent covariates, coexistence of the Nickell bias and the Stambaugh bias imposes challenges for estimation and hypothesis testing. This pa…
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…
Inference for Nonlinear Endogenous Treatment Effects Accounting for High-Dimensional Covariate Complexity
Qingliang Fan, Zijian Guo, Ziwei Mei +1
Nonlinearity and endogeneity are prevalent challenges in causal analysis using observational data. This paper proposes an inference procedure for a nonlinear and endogenous margina…
Nickell Bias in Panel Local Projection: Financial Crises Are Worse Than You Think
Ziwei Mei, Liugang Sheng, Zhentao Shi
Panel local projection (LP) with fixed-effects (FE) is widely adopted for evaluating the economic consequences of financial crises across countries. This paper highlights a fundame…
On LASSO for High Dimensional Predictive Regression
Ziwei Mei, Zhentao Shi
This paper examines LASSO, a widely-used -penalized regression method, in high dimensional linear predictive regressions, particularly when the number of potential predictor…
The boosted HP filter is more general than you might think
Ziwei Mei, Peter C. B. Phillips, Zhentao Shi
The global financial crisis and Covid recession have renewed discussion concerning trend-cycle discovery in macroeconomic data, and boosting has recently upgraded the popular HP fi…
A Heteroskedasticity-Robust Overidentifying Restriction Test with High-Dimensional Covariates
Qingliang Fan, Zijian Guo, Ziwei Mei
This paper proposes an overidentifying restriction test for high-dimensional linear instrumental variable models. The novelty of the proposed test is that it allows the number of c…