Estimation of large dimensional factor models with an unknown number of breaks
By Shujie Ma, Liangjun Su. Journal of Econometrics, 2018
56 citations recorded by the source corpus.
In this paper we study the estimation of a large dimensional factor model when the factor loadings exhibit an unknown number of changes over time. We propose a novel three-step procedure to detect the breaks if any and then identify their locations. In the first step, we divide the whole time span into subintervals and fit a conventional factor model on each interval. In the second step, we apply the adaptive fused group Lasso to identify intervals containing a break. In the third step, we devise a grid search method to estimate the location of the break on each identified interval. We show that with probability approaching one our method can identify the correct number of changes and estimate the break locations. Simulation studies indicate superb finite sample performance of our method. We apply our method to investigate Stock and Watson’s (2009) U.S. monthly macroeconomic data set and identify five breaks in the factor loadings, spanning 1959-2006.
Subfields: Economics and Econometrics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/4272cb2a24cfa44ac798b55089e95203d50293ec
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In Economics and Econometrics
- Marco Lippi
- M. Forni
- Markus Pelger
- Alessandro Giovannelli
- Stefano Soccorsi
- Ruoxuan Xiong
- Nicolas Vayatis
- Laurent Oudre
In Statistics and Probability
- Ruoxuan Xiong
- Alessandro Giovannelli
- Nicolas Vayatis
- Laurent Oudre
- Charles Truong
- Marco Lippi
- Markus Pelger
- Stefano Soccorsi
Citations in the corpus
- Selective review of offline change point detection methods (2020, background): “For instance, the trans970 formation can be based on local periodograms [153], or more recently, on PCA 971 and wavelet transforms [154, 155].”
- Dynamic factor model with infinite‐dimensional factor space: Forecasting (2016, background): “…Yamamoto and Tanaka (2015), Chen et al. (2014), Barigozzi et al. (2016), (ii) consistent factor estimation under breaks, Bates et al. (2013), Ma and Su (2016), Cheng et al. (2016), Massacci (2016), (iii) modeling time varying factor models Del Negro and Otrok (2008), Mikkelsen et al. (2015).”
- State-Varying Factor Models of Large Dimensions (2018, background): “structural breaks with pseudo factors, Ma and Su (2018) proposed a three-”