The objective of this step is to construct relevant and robust relationships between a single transformed dependent variable (e.g. PD) and several macro variables. The choice of the macro variables entering each model should be based upon the results of the correlation analysis. This process results in the construction of a range of multifactor models for each dependent variable.
5.7.2
In the context of time series regressions, institutions should choose the most appropriate methodology to perform multifactor regressions. Amongst others, it is recommended to perform multifactor regressions with or without autoregressive terms. It is recommended that institutions include several modelling forms as part the pool of possible model candidates.
5.7.3
The estimation of model coefficients should be performed with recognised professional statistical software and packages. The entire process should be fully documented and replicable by an independent party.
5.7.4
Several performance metrics should be used to rank and choose models. As these metrics depend on the type of models, institutions should use judgements to employ the most appropriate performance metrics per model type. At a minimum, the adjusted R-square should be used for multifactor regression models. For models based on the ARIMA form, a pseudo R-square should be employed as the square of the correlation between the fitted variable and the original dependent variable.