normal.test

<code>normal.test</code> performs multi-period testing of PD model predictive power. This procedure can be applied
on the level of the rating grade as well on the portfolio level.

The goal of this package is to cover the most common steps in probability of default (PD) rating model development and validation.
The main procedures available are those that refer to univariate, bivariate, multivariate analysis, calibration and validation.
Along with accompanied 'monobin' and 'monobinShiny' packages, 'PDtoolkit' provides functions which are suitable for different
data transformation and modeling tasks such as:
imputations, monotonic binning of numeric risk factors, binning of categorical risk factors, weights of evidence (WoE) and
information value (IV) calculations, WoE coding (replacement of risk factors modalities with WoE values), risk factor clustering,
area under curve (AUC) calculation and others. Additionally, package provides set of validation functions for testing homogeneity,
heterogeneity, discriminatory and predictive power of the model.

Andrija Djurovic

PDtoolkit

Collection of Tools for PD Rating Model Development and
Validation

normal.test function

<dl><dt>pdc</dt>
<dd>Numeric vector of calibrated probabilities of default (PD).</dd>
<dt>odr</dt>
<dd>Numeric vector of observed default rates.</dd>
<dt>alpha</dt>
<dd>Significance level of p-value for implemented tests. Default is 0.05.</dd></dl>

Arguments

Multi-period predictive power test — normal.test

<dl>

<dt>pdc</dt>
<dd>Numeric vector of calibrated probabilities of default (PD).</dd>


<dt>odr</dt>
<dd>Numeric vector of observed default rates.</dd>


<dt>alpha</dt>
<dd>Significance level of p-value for implemented tests. Default is 0.05.</dd>

</dl>

normal.test: Multi-period predictive power test

Description

Usage

Value

Arguments

References

Examples