A Likelihood Ratio Test for Stationarity of Rating Transitions
by Rafael Weißbach of the Technische Universität Dortmund, and
November 27, 2008
Abstract: For a time-continuous discrete-state Markov process as model for rating transitions, we study the time-stationarity by means of a likelihood ratio test. For multiple Markov process data from a multiplicative intensity model, maximum likelihood parameter estimates can be represented as martingale transform of the processes counting transitions between the rating states. As a consequence, the profile partial likelihood ratio is asymptotically x 2 -distributed. An internal rating data set reveals highly significant instationarity.
Keywords: Stationarity, Multiple Markov process, Counting process, Likelihood ratio, Panel data.
Published in: Journal of Econometrics, Vol. 155, No. 2, (April 2010), pp. 188-194.