SHRINKAGE ESTIMATION OF REGRESSION MODELS WITH MULTIPLE STRUCTURAL CHANGES

By J. Qian, Liangjun Su. Econometric Theory, 2015

66 citations recorded by the source corpus.

In this paper, we consider the problem of determining the number of structural changes in multiple linear regression models via group fused Lasso. We show that with probability tending to one, our method can correctly determine the unknown number of breaks, and the estimated break dates are sufficiently close to the true break dates. We obtain estimates of the regression coefficients via post Lasso and establish the asymptotic distributions of the estimates of both break ratios and regression coefficients. We also propose and validate a data-driven method to determine the tuning parameter. Monte Carlo simulations demonstrate that the proposed method works well in finite samples. We illustrate the use of our method with a predictive regression of the equity premium on fundamental information.

Subfields: Artificial Intelligence, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/31291032477c18936210f6bf8f716eea0adead7e

How researchers see this paper

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

In Artificial Intelligence

1. Sainan Jin
2. Liangjun Su

In Finance

1. Sainan Jin
2. Xia Wang
3. Liangjun Su
4. Halbert White
5. Tzee-Ming Huang
6. Xun Lu
7. Sessi Tokpavi
8. B. Candelon

In Statistics and Probability

1. Liangjun Su
2. Xia Wang
3. Sainan Jin
4. Halbert White
5. Xun Lu
6. Tzee-Ming Huang
7. Yixiao Sun
8. Xiaofeng Shao

Citations in the corpus

• Sieve Estimation of Time-Varying Panel Data Models With Latent Structures (2019, background): “For this reason, numerous studies have been devoted to test for structural changes in panel data models; see Han and Park (1989), Bai and Lluís Carrion-I-Silvestre (2009), Bai (2010), Kim (2011), Chen and Huang (2014), Li, Qian, and Su (2016), and Qian and Su (2016), among others.”