This paper shows that the parsimoniously time-varying methodology of Callot and Kristensen (2015) can be applied to factor models. We apply this method to study macroeconomic instability in the United States from 1959:1 to 2006:4 with a particular focus on the Great Moderation. Models with parsimoniously time-varying parameters are models with an unknown number of break points at unknown locations. The parameters are assumed to follow a random walk with a positive probability that an increment is exactly equal to zero so that the parameters do not vary at every point in time. The vector of increments, which is high dimensional by construction and sparse by assumption, is estimated using the Lasso. We apply this method to the estimation of static factor models and factor-augmented autoregressions using a set of 190 quarterly observations of 144 US macroeconomic series from Stock and Watson (2009). We find that the parameters of both models exhibit a higher degree of instability in the period from 1970:1 to 1984:4 relative to the following 15 years. In our setting the Great Moderation appears as the gradual ending of a period of high structural instability that took place in the 1970s and early 1980s.
The authors would like to thank two anonymous referees and participants at the 2014 Advances in Econometrics conference for their comments and suggestions. Furthermore, support from CREATES, Center for Research in Econometric Analysis of Time Series (DNRF78), funded by the Danish National Research Foundation is gratefully acknowledged.
Callot, L. and Kristensen, J. (2016), "Regularized Estimation of Structural Instability in Factor Models: The US Macroeconomy and the Great Moderation", Dynamic Factor Models (Advances in Econometrics, Vol. 35), Emerald Group Publishing Limited, pp. 437-479. https://doi.org/10.1108/S0731-905320150000035011Download as .RIS
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