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Suppose that our goal is to fit a logistic regression in order to obtain insolvency probabilities for possible credit takers and produce a credit risk score.

If I have a database available that contains monthly data for every customer and a binary variable that says whether said company is solvent or not, what are the sampling best practices?

A friend gave me the following advice:

  • Select one row of data for each company in order to avoid auto-correlation and other types of association that violate the GLM assumptions;
  • Select a large enough month-of-book in order to match the contract lengths and obtain a good amount of insolvents if possible. For example, if the average operation length is 2 years, we should select only observations that are already 6 months or 1 year old, otherwise they had little time to "become insolvent";
  • Use financial and economical ratios as covariates, such as $EBITDA/Loans$ and $Sales_t/ Sale_{t-1}.$

What are the best practices when assembling a sample to fit my statistical model?

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1 Answer 1

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A couple of remarks:

  • $\textbf{Ensure random sampling}$: It is important to randomly select your sample from the dataset to avoid bias and ensure that the sample is representative of the population.
  • $\textbf{Balance between solvent and insolvent cases}$: Ensure that your sample includes a balanced number of solvent and insolvent cases to avoid any class imbalance issues in your logistic regression model.
  • $\textbf{Adequate sample size}$: Make sure that your sample size is large enough to provide sufficient power for your logistic regression model. A larger sample size will also help in generalizing the results to the population.
  • $\textbf{Controls}$: Among financial and economic ratios, include other relevant controls as well, such as credit history, sector/industry.
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