On Time-Varying Factor Models: Estimation and Testing ∗

By Liangjun Su, Xia Wang. 2017

149 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/bbb95bed47d111bf5279f6d025de71a7cb30b78f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Liangjun Su
2. Shujie Ma
3. Sainan Jin
4. Ke Miao
5. Jianqing Fan
6. Shu-rong Zheng
7. Jianhua Guo
8. J. Gonzalo

In Statistics and Probability

1. Liangjun Su
2. Shujie Ma
3. Sainan Jin
4. Jianqing Fan
5. Shu-rong Zheng
6. Jianhua Guo
7. Ke Miao
8. J. Gonzalo

Citations in the corpus

• Quantile Factor Models (2017, background): “on features behind global extreme temperatures at both tails of their distribution, etc. Related literature There is a recent literature that attempts to make the AFM setup more flexible. For example, Su and Wang (2017) allows for the factor loadings to be time-varying and Pelger and Xiong (2018) adm”
• Shrinkage estimation of common breaks in panel data models via adaptive group fused Lasso (2016, unknown): “Second, if we model the cross section dependence through a factor structure, the factor loadings may also exhibit structural changes over time (see, e.g., Breitung and Eickmeier 2011, Cheng et al. 2015, and Su and Wang 2015) and this further complicates the analysis.”
• Estimation of large dimensional factor models with an unknown number of breaks (2018, background): “…and Swanson (2014) propose a test to check structural stability of both factor loadings and factor-augmented forecasting regression coefficients; Su and Wang (2017) consider estimation and testing in time-varying factor models and their test allows for multiple breaks in the factor loadings.”
• On factor models with random missing: EM estimation, inference, and cross validation (2020, background): “Assumption A.5 imposes some uniform convergence conditions that are similar to those imposed in Su et al. (2015) and Su and Wang (2017).”
• Estimating Number of Factors by Adjusted Eigenvalues Thresholding (2019, methodology): “Su and Wang (2017) used the BIC information criterion to determine the number of common factors for time-varying factor models.”