I'm currently working on an event study to examine abnormal returns.

In the first step, I've calculated abnormal returns in regards to a certain type of company event, consisting of roughly 13,000 events and >4,000 firms.

In the second step, I intend to run a regression analysis with several (control?) variables to see whether some of the effect stems from certain aspects of the event.

So far so good, now I'm having the issue that I want to control for 5-6 factors like market capitalization and total enterprise value. Unfortunately, I don't have every single datapoint for every single of the 13,000 events. As an example, for Event 1 I'm missing market capitalization, for Event 2 the total enterprise value, for Event 3 the M/B-ratio and so on.

Question: Can I still run a meaningful regression even though I have a significant number of NA's or am I required to delete every single event with incomplete data? Given the poor data availability for some variables (which I generally still would love to include), that would result in a very large number of deleted events (>7,000).

  • $\begingroup$ You could try to impute the missing values. See for example Multiple Imputation by Chained Equations (MICE) Explained. $\endgroup$
    – dipetkov
    Jul 8, 2022 at 15:11
  • $\begingroup$ Thanks for your answer, sorry for the late reply. I've had a look into it, the only problem is that I have several binary control variables (e.g., paid in stock or cash, target company is public or private). In such cases, it seems to me that imputation doesn't make sense. $\endgroup$
    – LeCV
    Jul 19, 2022 at 8:27
  • $\begingroup$ There are three options: drop rows/events with missing values; do single value imputation (replace with mean, median, mode, etc.); do multiple imputation. Whether your regression analysis is meaningful if you use only complete data points depends on the reasons the data is missing. Take a look at van Buuren's Flexible Imputation of Missing Data. $\endgroup$
    – dipetkov
    Jul 19, 2022 at 10:09

1 Answer 1


As dipetkov says, is the best solution here. And imputing binary and categorical variables is both reasonable and quite feasible. This is a vignette that shows how to do this in the R package Amelia. And here is a Google scholar search that shows a number of papers about multiple imputation of binary variables, in case you would like to look at the primary literature on the subject.


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