This paper presents the Conditional Probability of Default (CoPoD) methodology for modelling the probabilites of loan defaults (PoD) by small and medium enterprises (SMEs) and unlisted firms as functions of identifiable macroeconomic and financial variables. The process of modelling PoDs represents a challenging task, since the time series of PoDs usually contain few observations, thus making ordinary least squares (OLS) estimation imprecise or unfeasible. CoPoD improves the measurement of the impact of macroeconomic variables on PoDs and consequently the measurement of loans' credit risk through time, thereby making a twofold contribution. First, econometrically, it recovers estimators that show greater robustness than OLS estimators in finite sample settings under the Mean Square Error criterion. Second, economically, on the basis of economic theory and empirical evidence, CoPoD can incorporate a procedure to select a relevant set of macroeconomic explanatory variables that have an impact on the PoDs. We implement CoPoD with information from Norway and Mexico.
