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Modeling Frequency in Operational Losses with Python

Poisson and Negative Binomial regressions are two popular approaches to model frequency measures in the operational loss and can be implemented in Python with the statsmodels package as below:

In [1]: import pandas as pd

In [2]: import statsmodels.api as sm

In [3]: import statsmodels.formula.api as smf

In [4]: df = pd.read_csv("AutoCollision.csv")

In [5]: # FITTING A POISSON REGRESSION

In [6]: poisson = smf.glm(formula = "Claim_Count ~ Age + Vehicle_Use", data = df, family = sm.families.Poisson(sm.families.links.log))

In [7]: poisson.fit().summary()
Out[7]:
<class 'statsmodels.iolib.summary.Summary'>
"""
                 Generalized Linear Model Regression Results
==============================================================================
Dep. Variable:            Claim_Count   No. Observations:                   32
Model:                            GLM   Df Residuals:                       21
Model Family:                 Poisson   Df Model:                           10
Link Function:                    log   Scale:                             1.0
Method:                          IRLS   Log-Likelihood:                -204.40
Date:                Tue, 08 Dec 2015   Deviance:                       184.72
Time:                        20:31:27   Pearson chi2:                     184.
No. Iterations:                     9
=============================================================================================
                                coef    std err          z      P>|z|      [95.0% Conf. Int.]
---------------------------------------------------------------------------------------------
Intercept                     2.3702      0.110     21.588      0.000         2.155     2.585
Age[T.21-24]                  1.4249      0.118     12.069      0.000         1.193     1.656
Age[T.25-29]                  2.3465      0.111     21.148      0.000         2.129     2.564
Age[T.30-34]                  2.5153      0.110     22.825      0.000         2.299     2.731
Age[T.35-39]                  2.5821      0.110     23.488      0.000         2.367     2.798
Age[T.40-49]                  3.2247      0.108     29.834      0.000         3.013     3.437
Age[T.50-59]                  3.0019      0.109     27.641      0.000         2.789     3.215
Age[T.60+]                    2.6391      0.110     24.053      0.000         2.424     2.854
Vehicle_Use[T.DriveLong]      0.9246      0.036     25.652      0.000         0.854     0.995
Vehicle_Use[T.DriveShort]     1.2856      0.034     37.307      0.000         1.218     1.353
Vehicle_Use[T.Pleasure]       0.1659      0.041      4.002      0.000         0.085     0.247
=============================================================================================
"""

In [8]: # FITTING A NEGATIVE BINOMIAL REGRESSION

In [9]: nbinom = smf.glm(formula = "Claim_Count ~ Age + Vehicle_Use", data = df, family = sm.families.NegativeBinomial(sm.families.links.log))

In [10]: nbinom.fit().summary()
Out[10]:
<class 'statsmodels.iolib.summary.Summary'>
"""
                 Generalized Linear Model Regression Results
==============================================================================
Dep. Variable:            Claim_Count   No. Observations:                   32
Model:                            GLM   Df Residuals:                       21
Model Family:        NegativeBinomial   Df Model:                           10
Link Function:                    log   Scale:                 0.0646089484752
Method:                          IRLS   Log-Likelihood:                -198.15
Date:                Tue, 08 Dec 2015   Deviance:                       1.4436
Time:                        20:31:27   Pearson chi2:                     1.36
No. Iterations:                    11
=============================================================================================
                                coef    std err          z      P>|z|      [95.0% Conf. Int.]
---------------------------------------------------------------------------------------------
Intercept                     2.2939      0.153     14.988      0.000         1.994     2.594
Age[T.21-24]                  1.4546      0.183      7.950      0.000         1.096     1.813
Age[T.25-29]                  2.4133      0.183     13.216      0.000         2.055     2.771
Age[T.30-34]                  2.5636      0.183     14.042      0.000         2.206     2.921
Age[T.35-39]                  2.6259      0.183     14.384      0.000         2.268     2.984
Age[T.40-49]                  3.2408      0.182     17.760      0.000         2.883     3.598
Age[T.50-59]                  2.9717      0.183     16.283      0.000         2.614     3.329
Age[T.60+]                    2.6404      0.183     14.463      0.000         2.283     2.998
Vehicle_Use[T.DriveLong]      0.9480      0.128      7.408      0.000         0.697     1.199
Vehicle_Use[T.DriveShort]     1.3402      0.128     10.480      0.000         1.090     1.591
Vehicle_Use[T.Pleasure]       0.3265      0.128      2.548      0.011         0.075     0.578
=============================================================================================
"""

Although Quasi-Poisson regressions is not currently supported by the statsmodels package, we are still able to estimate the model with the rpy2 package by using R in the back-end. As shown in the output below, parameter estimates in Quasi-Poisson model are identical to the ones in standard Poisson model. In case that we want a flexible model approach for frequency measures in the operational loss forecast without pursuing more complex Negative Binomial model, Quasi-Poisson regression can be considered a serious contender.

In [11]: # FITTING A QUASI-POISSON REGRESSION

In [12]: import rpy2.robjects as ro

In [13]: from rpy2.robjects import pandas2ri

In [14]: pandas2ri.activate()

In [15]: rdf = pandas2ri.py2ri_pandasdataframe(df)

In [16]: qpoisson = ro.r.glm('Claim_Count ~ Age + Vehicle_Use', data = rdf, family = ro.r('quasipoisson(link = "log")'))

In [17]: print ro.r.summary(qpoisson)

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)
(Intercept)             2.3702     0.3252   7.288 3.55e-07 ***
Age21-24                1.4249     0.3497   4.074 0.000544 ***
Age25-29                2.3465     0.3287   7.140 4.85e-07 ***
Age30-34                2.5153     0.3264   7.705 1.49e-07 ***
Age35-39                2.5821     0.3256   7.929 9.49e-08 ***
Age40-49                3.2247     0.3202  10.072 1.71e-09 ***
Age50-59                3.0019     0.3217   9.331 6.42e-09 ***
Age60+                  2.6391     0.3250   8.120 6.48e-08 ***
Vehicle_UseDriveLong    0.9246     0.1068   8.660 2.26e-08 ***
Vehicle_UseDriveShort   1.2856     0.1021  12.595 2.97e-11 ***
Vehicle_UsePleasure     0.1659     0.1228   1.351 0.191016
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for quasipoisson family taken to be 8.774501)

    Null deviance: 6064.97  on 31  degrees of freedom
Residual deviance:  184.72  on 21  degrees of freedom
AIC: NA

Number of Fisher Scoring iterations: 4
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Written by statcompute

December 8, 2015 at 11:16 pm

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