# 14-010/IV/DSF71 (2014-01-14)

Author(s)
Francesco Calvori, Department of Statistics 'G. Parenti', University of Florence, Italy; Drew Creal, Booth School of Business, University of Chicago; Siem Jan Koopman, VU University Amsterdam; Andre Lucas, VU University Amsterdam
Keywords:
time-varying parameters; observation driven models; parameter driven models; structural breaks; generalized autoregressive score model; regime switching; credit risk
JEL codes:
C12, C52, C22

We develop a new parameter stability test against the alternative of observation driven generalized autoregressive score dynamics. The new test generalizes the ARCH-LM test of Engle (1982) to settings beyond time-varying volatility and exploits any autocorrelation in the likelihood scores under the alternative. We compare the test's performance with that of alternative tests developed for competing time-varying parameter frameworks, such as structural breaks and observation driven parameter dynamics. The new test has higher and more stable power against alternatives with frequent regime switches or with non-local parameter driven time-variation. For parameter driven time variation close to the null or for infrequent structural changes, the test of Muller and Petalas (2010)
performs best overall. We apply all tests empirically to a panel of losses given default over the period 1982--2010 and find significant evidence of parameter variation in the
underlying beta distribution.