Modeling credit rating migrations conditional on macroeconomic conditions allows financial institutions to assess, analyze, and manage the risk related to a credit portfolio. Existing methodologies to model credit rating migrations conditional on the business cycle suffer from poor accuracy, difficult readability, or model inconsistencies. The modeling methodology proposed in this paper extends ordinal logistic regression to estimate the complete migration matrix including default rates as a function of rating dynamics and macroeconomic indicators. The gradient and Hessian derivations show efficient optimization within the Levenberg–Marquardt algorithm. The proposed modeling methodology is applied to model corporate rating migrations using historical data from 1984 to 2011. It is shown that the resulting model captures the cyclical behavior of credit rating migrations and default rates, and is able to approximate historic migration levels with good precision. The model therefore permits analysis of the impact of economical downturn conditions on a credit portfolio. Copyright © 2013 John Wiley & Sons, Ltd.