<p>The amount of money donated in fundraising situations is a continuous variable, and not typically considered as count data. However, I assume you have a lot of zeros because not everyone donates, so using models for non-negative data with many zeros could be a good idea.</p>
<p>Among the count data models the <em>negative-binomial</em> and the <em>zero-inflated regression</em> models have the important drawback of not being invariant to the scale of the dependent variable. That is, measuring donations in dollars or in thousands of dollars will lead to different estimates of the elasticities of interest. These models also rely on specific assumptions.</p>
<p>A better alternative often used to estimate trade in gravity equations is the <em>Poisson Pseudo-Maximum Likelihood</em> (PPML) estimator. Reference:  Santos Silva, J.M.C. and Tenreyro, Silvana (2006), <a href="https://personal.lse.ac.uk/tenreyro/jensen08k.pdf" rel="nofollow noreferrer">The Log of Gravity</a>, The Review of Economics and Statistics, 88(4), pp. 641-658.</p>
<p>PPML is consistent, and can even be optimal, when there is under- or over-dispersion. The estimator works also very well even when the proportion of zeros is very large. Check out the <a href="https://personal.lse.ac.uk/tenreyro/lgw.html" rel="nofollow noreferrer">The Log of Gravity</a> page.</p>

CC BY-SA 4.0
2023-01-18T19:44:44.010Z
If there are a lot of 0s, the Heckit method might make more sense.
Michael Gmeiner
CC BY-SA 4.0
2023-01-19T20:21:41.633Z
The issue is that the Heckit method relies on very strong assumptions.
emeryville
CC BY-SA 4.0
2023-01-20T07:27:13.540Z
Sure, but I would claim that assuming data are generated by a negative binomial distribution is also a very strong assumption.
If OP goes down that path, I'd suggest Poisson regression, which is more robust to misspecification.
Michael Gmeiner
CC BY-SA 4.0
2023-01-20T09:35:19.147Z
Totally agree! That's why I suggest PPML!
emeryville
CC BY-SA 4.0
2023-01-20T10:02:58.440Z
I've edited my answer to make it clearer, in case it wasn't. Thanks.
emeryville
CC BY-SA 4.0
2023-01-17T09:39:47.323Z
2023-01-20T09:43:58.527Z
2023-01-20T09:43:58.527Z
emeryville
<p>I know that the negative binomial regression is for count variables - can the amount of money be considered as count data? There is a paper on which I have to extend upon, where the authors are estimating the amount of money donated (depvar) in different types of fundraising situations (treatment dummies). They are using OLS for this and I have to use a different method. The amount of money donated looks like it follows a poisson distribution but it is overdispersed.</p>

CC BY-SA 4.0
2023-01-16T19:02:30.163Z
Welcome to the site.  I think your question is difficult to answer without more information.  Are you proposing to convert amounts of money to count data using bins, eg number of donations up to \$10, number between \$10 and \$20, and so on?  Do you count zeros, ie individuals within the relevant population who donate nothing?  Please edit your question to clarify these points.
Adam Bailey
<econometrics><regression>
Can I use negative binomial regression to estimate amount of money type data?
GRacz
# Can I use negative binomial regression to estimate amount of money type data?

(Asked by: username_0 on 2023-01-16)

I know that the negative binomial regression is for count variables - can the amount of money be considered as count data? There is a paper on which I have to extend upon, where the authors are estimating the amount of money donated (depvar) in different types of fundraising situations (treatment dummies). They are using OLS for this and I have to use a different method. The amount of money donated looks like it follows a poisson distribution but it is overdispersed.

**Tags:** econometrics, regression

> **Comments:**
> **username_1**: Welcome to the site.  I think your question is difficult to answer without more information.  Are you proposing to convert amounts of money to count data using bins, eg number of donations up to \$10, number between \$10 and \$20, and so on?  Do you count zeros, ie individuals within the relevant population who donate nothing?  Please edit your question to clarify these points.  

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## 1 answers

### Answer by username_2 on 2023-01-17. Score: 4

The amount of money donated in fundraising situations is a continuous variable, and not typically considered as count data. However, I assume you have a lot of zeros because not everyone donates, so using models for non-negative data with many zeros could be a good idea.

Among the count data models the *negative-binomial* and the *zero-inflated regression* models have the important drawback of not being invariant to the scale of the dependent variable. That is, measuring donations in dollars or in thousands of dollars will lead to different estimates of the elasticities of interest. These models also rely on specific assumptions.

A better alternative often used to estimate trade in gravity equations is the *Poisson Pseudo-Maximum Likelihood* (PPML) estimator. Reference: Santos Silva, J.M.C. and Tenreyro, Silvana (2006), [The Log of Gravity](https://personal.lse.ac.uk/tenreyro/jensen08k.pdf), The Review of Economics and Statistics, 88(4), pp. 641-658.

PPML is consistent, and can even be optimal, when there is under- or over-dispersion. The estimator works also very well even when the proportion of zeros is very large. Check out the [The Log of Gravity](https://personal.lse.ac.uk/tenreyro/lgw.html) page.

> **Comments:**
> **username_3**: If there are a lot of 0s, the Heckit method might make more sense.  
> **username_2**: The issue is that the Heckit method relies on very strong assumptions.  
> **username_3**: Sure, but I would claim that assuming data are generated by a negative binomial distribution is also a very strong assumption. If OP goes down that path, I'd suggest Poisson regression, which is more robust to misspecification.  
> **username_2**: Totally agree! That's why I suggest PPML!  
> **username_2**: I've edited my answer to make it clearer, in case it wasn't. Thanks.  

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