This paper presents a maximum likelihood estimation of term structures of conditional corporate default probabilities, incorporating the dynamics of firm-specific and macroeconomic covariates. Using over 390,000 firm-months of data on over 2700 U.S. Industrial firms from 1979 to 2004, the study finds that the level and shape of the estimated term structure of conditional future default probabilities depend on a firm's distance to default (a volatility-adjusted measure of leverage), trailing stock return, trailing S&P 500 returns, and U.S. interest rates. Distance to default is the most influential covariate. Default intensities are estimated to be lower with higher short-term interest rates. The out-of-sample predictive performance of the model is an improvement over that of other available models. The model is based on a Markov state vector X_t of firm-specific and macroeconomic covariates that causes variation over time in a firm's default intensity λ_t = Λ(X_t), which is the conditional mean arrival rate of default measured in events per year. The firm exits for other reasons, such as merger or acquisition, with an intensity α_t = A(X_t). The total exit intensity is thus α_t + λ_t. The study provides maximum likelihood estimators of term structures of conditional corporate default probabilities, incorporating the dynamics of firm-specific and macroeconomic covariates. The model is shown to have improved out-of-sample predictive performance over other available models. The study also finds that the estimated model can be used to calculate probabilities of joint default of groups of firms, or other properties related to default correlation. The model underestimates default correlation relative to average pairwise sample correlations of default reported in DeServigny and Renault (2002). The study also finds that the estimated model can be used to estimate the likelihood, by some future date, of either default or a given increase in conditional default probability. This and related transition-risk calculations could play a role in credit rating, risk management, and regulatory applications. The study also finds that the estimated model can be used to calculate probabilities of joint default of groups of firms, or other properties related to default correlation. The model underestimates default correlation relative to average pairwise sample correlations of default reported in DeServigny and Renault (2002). The study also finds that the estimated model can be used to estimate the likelihood, by some future date, of either default or a given increase in conditional default probability. This and related transition-risk calculations could play a role in credit rating, risk management, and regulatory applications.This paper presents a maximum likelihood estimation of term structures of conditional corporate default probabilities, incorporating the dynamics of firm-specific and macroeconomic covariates. Using over 390,000 firm-months of data on over 2700 U.S. Industrial firms from 1979 to 2004, the study finds that the level and shape of the estimated term structure of conditional future default probabilities depend on a firm's distance to default (a volatility-adjusted measure of leverage), trailing stock return, trailing S&P 500 returns, and U.S. interest rates. Distance to default is the most influential covariate. Default intensities are estimated to be lower with higher short-term interest rates. The out-of-sample predictive performance of the model is an improvement over that of other available models. The model is based on a Markov state vector X_t of firm-specific and macroeconomic covariates that causes variation over time in a firm's default intensity λ_t = Λ(X_t), which is the conditional mean arrival rate of default measured in events per year. The firm exits for other reasons, such as merger or acquisition, with an intensity α_t = A(X_t). The total exit intensity is thus α_t + λ_t. The study provides maximum likelihood estimators of term structures of conditional corporate default probabilities, incorporating the dynamics of firm-specific and macroeconomic covariates. The model is shown to have improved out-of-sample predictive performance over other available models. The study also finds that the estimated model can be used to calculate probabilities of joint default of groups of firms, or other properties related to default correlation. The model underestimates default correlation relative to average pairwise sample correlations of default reported in DeServigny and Renault (2002). The study also finds that the estimated model can be used to estimate the likelihood, by some future date, of either default or a given increase in conditional default probability. This and related transition-risk calculations could play a role in credit rating, risk management, and regulatory applications. The study also finds that the estimated model can be used to calculate probabilities of joint default of groups of firms, or other properties related to default correlation. The model underestimates default correlation relative to average pairwise sample correlations of default reported in DeServigny and Renault (2002). The study also finds that the estimated model can be used to estimate the likelihood, by some future date, of either default or a given increase in conditional default probability. This and related transition-risk calculations could play a role in credit rating, risk management, and regulatory applications.