A likelihood ratio test for stationarity of rating transitions

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Technical Report ; No. 2008,27

Classification and Topics

• Classification: Multiple or Simultaneous Equation Models: Panel Data Models; Spatio-temporal Models ; Multiple or Simultaneous Equation Models: Truncated and Censored Models; Switching Regression Models ; Duration Analysis; Optimal Timing Strategies
• Subject: Stationarity ; Multiple Markov process ; Counting process ; Likelihood ratio ; Panel data

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Weißbach, Rafael ; Walter, Ronja
• Event: Veröffentlichung
• (who): Technische Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
• (where): Dortmund
• (when): 2008

Further information

• Handle: http://hdl.handle.net/10419/36578
• Last update: 10.03.2025, 11:44 AM CET