<p>I am not familiar with this specific text, however, I've been asked to elucidate and will do so as an answer, though I do not utilize these techniques often, so there are undoubtedly important elements I have missed.</p>
<p>There is a body of methods called propensity score methods. The primary issue these techniques address is that treatment is typically not exogenous (properly randomized)- many groups are likely to be treated because of their existing characteristics. Under these conditions if one naively estimates the effect of treatment, say through DID, one will estimate the <em>effect of treatment on those that were already going to be treated anyway</em>.  The estimated DID coefficient will then be a biased estimate for the effects of treating the population as a whole.</p>
<p>The key thought of these techniques is to estimate the probability that a particular group will be treated, and then weight the estimation to back out the impact of treatment on the population as a whole.  The manner in which this is done will vary, I recommend reading carefully the Dasgupta, 2019 paper for citations - they surely directly mention where their technique has come from in the methodology section. Searching other papers at random will provide other estimates.</p>
<p>As another note, you do not want to estimate the probability of treatment with probit/logit and use it as an instrument directly - it does not have the desired effect (see control function approaches (CFA), or 2 stage residual inclusion (2SRI)).</p>

CC BY-SA 4.0
2021-12-06T02:28:24.907Z
Thank a lot @RegressFoward. I  just updated the question after asking the author. Could you please have a look on that then. Thanks a heap
Phil Nguyen
CC BY-SA 4.0
2021-12-05T22:07:04.143Z
2021-12-05T22:07:04.143Z
RegressForward
<p>In a paper, <a href="https://www.mendeley.com/catalogue/065edab3-074f-3b5c-989f-458a046471ed/?utm_source=desktop&amp;utm_medium=1.19.8&amp;utm_campaign=open_catalog&amp;userDocumentId=%7B28766b2a-d4bc-451a-a040-a4f863b653ee%7D" rel="nofollow noreferrer">Dasgupta, 2019</a> used Difference-in-Difference approach to see whether anticollusion laws implemented by different countries (staggered implementation) affect firms financial flexibility.</p>
<p><a href="https://www.mendeley.com/catalogue/065edab3-074f-3b5c-989f-458a046471ed/?utm_source=desktop&amp;utm_medium=1.19.8&amp;utm_campaign=open_catalog&amp;userDocumentId=%7B28766b2a-d4bc-451a-a040-a4f863b653ee%7D" rel="nofollow noreferrer">Dasgupta, 2019, p.2610</a> used an approach  called &quot;<strong>prediction model</strong>&quot;</p>
<blockquote>
<p>by only using pre-leniency observations and predict the probability
that the firm will be convicted in the cartel case after the passage
of a leniency law.</p>
</blockquote>
<p>In particular, what they did is</p>
<blockquote>
<p>First, we estimate the propensity of a firm to be convicted in a
cartel case. We use a prediction model based on time-varying firm
characteristics (asset size, leverage, and ROA), country
characteristics (GDP and unemployment), and country fixed effects and
three-digit SIC fixed effects.</p>
</blockquote>
<p>I do not understand how they calculate the &quot;<strong>probability that the firm will be convicted in the cartel case after the passage of a leniency law</strong>&quot; like that by using STATA. The one command I can link to is &quot;<a href="https://www.stata.com/manuals/rpredict.pdf" rel="nofollow noreferrer">predict</a>&quot; but it seems not to work in this case.</p>
<p>Update:</p>
<p><strong>From some suggestions, it seems that a good way is to use probit(or logit) model to predict the possibility as mentioned. But I do not understand how can we assign the value of dependent variable (0 and 1) in Dasgupta's study.</strong></p>

CC BY-SA 4.0
2021-12-04T05:36:02.117Z
Have you looked at propensity treatment models?
RegressForward
CC BY-SA 4.0
2021-12-04T05:37:47.627Z
Hi @RegressFoward, is there any link for it then, I search with the keyword "propensity treatment models state" but it only shows the PSM (propensity matching score)
Phil Nguyen
CC BY-SA 4.0
2021-12-04T05:47:40.530Z
After a while, I found this one (https://onlinelibrary.wiley.com/doi/full/10.1002/pds.3356), is it what you are talking about?
Phil Nguyen
CC BY-SA 4.0
2021-12-04T05:47:50.017Z
It sounds like they are using a propensity treatment score method, there are a variety of approaches in that flavor.
RegressForward
CC BY-SA 4.0
2021-12-05T19:58:57.110Z
@RegressForward, if it is available, really hope that you can post an answer that can benefits all learners including me and get some internet points :D
Phil Nguyen
CC BY-SA 4.0
2021-12-05T19:59:28.973Z
It is also the chance for me to understand more.
Phil Nguyen
<stata><prediction>
What is the "prediction model" in Dasgupta (2019)?
Phil Nguyen
# What is the "prediction model" in Dasgupta (2019)?

(Asked by: username_0 on 2021-12-04)

In a paper, [Dasgupta, 2019](https://www.mendeley.com/catalogue/065edab3-074f-3b5c-989f-458a046471ed/?utm_source=desktop&utm_medium=1.19.8&utm_campaign=open_catalog&userDocumentId=%7B28766b2a-d4bc-451a-a040-a4f863b653ee%7D) used Difference-in-Difference approach to see whether anticollusion laws implemented by different countries (staggered implementation) affect firms financial flexibility.

[Dasgupta, 2019, p.2610](https://www.mendeley.com/catalogue/065edab3-074f-3b5c-989f-458a046471ed/?utm_source=desktop&utm_medium=1.19.8&utm_campaign=open_catalog&userDocumentId=%7B28766b2a-d4bc-451a-a040-a4f863b653ee%7D) used an approach called "**prediction model**"

> by only using pre-leniency observations and predict the probability
> that the firm will be convicted in the cartel case after the passage
> of a leniency law.

In particular, what they did is

> First, we estimate the propensity of a firm to be convicted in a
> cartel case. We use a prediction model based on time-varying firm
> characteristics (asset size, leverage, and ROA), country
> characteristics (GDP and unemployment), and country fixed effects and
> three-digit SIC fixed effects.

I do not understand how they calculate the "**probability that the firm will be convicted in the cartel case after the passage of a leniency law**" like that by using STATA. The one command I can link to is "[predict](https://www.stata.com/manuals/rpredict.pdf)" but it seems not to work in this case.

Update:

**From some suggestions, it seems that a good way is to use probit(or logit) model to predict the possibility as mentioned. But I do not understand how can we assign the value of dependent variable (0 and 1) in Dasgupta's study.**

**Tags:** stata, prediction
> **Comments:**
> **username_1**: Have you looked at propensity treatment models?  
> **username_0**: Hi @RegressFoward, is there any link for it then, I search with the keyword "propensity treatment models state" but it only shows the PSM (propensity matching score)  
> **username_0**: After a while, I found this one (https://onlinelibrary.wiley.com/doi/full/10.1002/pds.3356), is it what you are talking about?  
> **username_1**: It sounds like they are using a propensity treatment score method, there are a variety of approaches in that flavor.  
> **username_0**: @RegressForward, if it is available, really hope that you can post an answer that can benefits all learners including me and get some internet points :D  
> **username_0**: It is also the chance for me to understand more.  

---

## 1 answers

### Answer by username_1 on 2021-12-05. Score: 3 | Accepted answer

I am not familiar with this specific text, however, I've been asked to elucidate and will do so as an answer, though I do not utilize these techniques often, so there are undoubtedly important elements I have missed.

There is a body of methods called propensity score methods. The primary issue these techniques address is that treatment is typically not exogenous (properly randomized)- many groups are likely to be treated because of their existing characteristics. Under these conditions if one naively estimates the effect of treatment, say through DID, one will estimate the *effect of treatment on those that were already going to be treated anyway*. The estimated DID coefficient will then be a biased estimate for the effects of treating the population as a whole.

The key thought of these techniques is to estimate the probability that a particular group will be treated, and then weight the estimation to back out the impact of treatment on the population as a whole. The manner in which this is done will vary, I recommend reading carefully the Dasgupta, 2019 paper for citations - they surely directly mention where their technique has come from in the methodology section. Searching other papers at random will provide other estimates.

As another note, you do not want to estimate the probability of treatment with probit/logit and use it as an instrument directly - it does not have the desired effect (see control function approaches (CFA), or 2 stage residual inclusion (2SRI)).

> **Comments:**
> **username_0**: Thank a lot @RegressFoward. I  just updated the question after asking the author. Could you please have a look on that then. Thanks a heap  

---

