no code implementations • 2 Dec 2023 • Likai Chen, Georg Keilbar, Liangjun Su, Weining Wang
We find that in the Gaussian approximations to the test statistics, the dependence structures in the data can be safely ignored due to the localized nature of the statistics.
no code implementations • 29 Jul 2023 • Yiren Wang, Peter C B Phillips, Liangjun Su
With the preliminary nuclear-norm-regularized estimation followed by row- and column-wise linear regressions, we estimate the break point based on the idea of binary segmentation and the latent group structures together with the number of groups before and after the break by sequential testing K-means algorithm simultaneously.
no code implementations • 20 Oct 2022 • Yiren Wang, Liangjun Su, Yichong Zhang
In this paper, we propose a class of low-rank panel quantile regression models which allow for unobserved slope heterogeneity over both individuals and time.
no code implementations • 26 Apr 2022 • Liangjun Su, Thomas Tao Yang, Yonghui Zhang, Qiankun Zhou
Similarly to Chudik, Kapetanios and Pesaran (2018), we consider the statistical significance of individual nonparametric additive components one at a time and take into account the multiple testing nature of the problem.
no code implementations • 22 Nov 2021 • Bin Peng, Liangjun Su, Joakim Westerlund, Yanrong Yang
This paper considers a model with general regressors and unobservable factors.
1 code implementation • 19 Oct 2020 • Zhentao Shi, Liangjun Su, Tian Xie
This paper tackles forecast combination with many forecasts or minimum variance portfolio selection with many assets.
no code implementations • 7 May 2020 • Shujie Ma, Liangjun Su, Yichong Zhang
This paper proposes a logistic undirected network formation model which allows for assortative matching on observed individual characteristics and the presence of edge-wise fixed effects.