[Md Shoeb]

After jwdid and your dep variable you can interact all variables as usual, however you can’t do that with gvar and tvar
those have to remain on the original time-scales. Interactions will make no sense.

Based on the jwdid help file, my understanding was the gvar and tvar can be interacted with an independent variable which will give a result similar to DiD*x effect of the TWFE model

 xtvar(varlist)      Variables declared are only interacted with the time variable

xgvar(varlist)      Variables declared are only interacted with the group/cohort variable.

If interaction is not possible, what is the purpose of xtvar and xgvar ?

Edit: I think I was not clear with my original post. gvar and tvar are only interacted in xtvar() and xgvar(). Sorry for the confusion

[FernandoRios]

That wasnt clear from your previous message
So, whenever you add a control variable, the default is to include it for treatment heterogeneity (Time cohort # variables) as well as interactions with time and cohort dummies.
Sometimes, however, you may not want it to be directly interacted with GxT (no heterogeneity, but allow for more flexible form in the pre-treament. In those cases, you use xgvar and xtvar.
Please see into the paper cited in the helpfile. We tried to include there the exact specification of what happens when you use those options/
F

[Md Shoeb]

That wasnt clear from your previous message
So, whenever you add a control variable, the default is to include it for treatment heterogeneity (Time cohort # variables) as well as interactions with time and cohort dummies.
Sometimes, however, you may not want it to be directly interacted with GxT (no heterogeneity, but allow for more flexible form in the pre-treament. In those cases, you use xgvar and xtvar.
Please see into the paper cited in the helpfile. We tried to include there the exact specification of what happens when you use those options/
F

Thank you for the response. I will definitely look at the papers cited. However, I am still not very clear. Am I incorrect to assume that adding xtvar and xgvar is equivalent to DiD##x1 in the TWFE model ? If yes, are there any models that support interaction of x1 with the DiD term ?

[FernandoRios]

No,
consider the basic DID TWFE (and for a second assume its correct:

y = a0 +a1 * post + a2 * treat + a3 * post x treat + FE_t + FE_i + e

if you use gvar, it will do something like

y = a0 +a1 * post + a2 * treat + a3 * post x treat + b1 * treat * Xs + FE_t + FE_i + e

but treatment effect a3 is not affected by the new interaction (except for change in specification)

[Md Shoeb]

No,
consider the basic DID TWFE (and for a second assume its correct:

y = a0 +a1 * post + a2 * treat + a3 * post x treat + FE_t + FE_i + e

if you use gvar, it will do something like

y = a0 +a1 * post + a2 * treat + a3 * post x treat + b1 * treat * Xs + FE_t + FE_i + e

but treatment effect a3 is not affected by the new interaction (except for change in specification)

So the ATTs aggregated in jwdid only include treat*post even if xtvar and xgvar are included in the specification. Assuming that we are interested in both treat*post and treat*post*x1. Is there any way to estimate these two coefficients ?.

[FernandoRios]

if you are interested in treat*post*x1 standard syntax applies

jwdid y x, ....

which estimates

y = a0 +a1 * post + a2 * treat + a3 * post x treat * X + b1 * treat * Xs +b2 * post* Xs+ FE_t + FE_i + e

but heterogeneity is only possible for simple estimates.

[Fabrizio Leone]

Hi Fernando,

Thanks for this great package. Related to #15 in this thread, is there now a way to test for pretends when "not yet" are used as controls? That would be very helpful. thank you!

[Md Shoeb]

if you are interested in treat*post*x1 standard syntax applies

jwdid y x, ....

which estimates

y = a0 +a1 * post + a2 * treat + a3 * post x treat * X + b1 * treat * Xs +b2 * post* Xs+ FE_t + FE_i + e

but heterogeneity is only possible for simple estimates.

So I run jwdid y x, .....
the aggregation (estat simple or event) will be on a3 * post*treat*X ?

[FernandoRios]

Hi Fernando,

Thanks for this great package. Related to #15 in this thread, is there now a way to test for pretends when "not yet" are used as controls? That would be very helpful. thank you!

Not possible. If you use Not yet, there is no way to differentiate treated from not-yet treated and the timing. You still need to use Never
Or use other approaches for pretreatment (like running models for each pre-treatment period)

Md Shoeb
See the paper, it is explained there how things work

[Md Shoeb]

Not possible. If you use Not yet, there is no way to differentiate treated from not-yet treated and the timing. You still need to use Never
Or use other approaches for pretreatment (like running models for each pre-treatment period)

Md Shoeb
See the paper, it is explained there how things work

Okay. Thank you Fernando for giving so much of your time.

[Sung Ju Cho]

Dear Fernando,

I noticed that the results from jwdid and xthdidregress twfe can differ significantly, especially when the panel is unbalanced or there are missing observations. I suspect the discrepancies may arise from how these commands handle missing data. I have provided my code attached using the example dataset from the jwdid help file. I would greatly appreciate it if you could help clarify the source of these differences. Code and Observations

I conducted comparisons across the following setups:

1. Full sample: Results from jwdid with "notyet" and "never" options compared with xthdidregress twfe.
2. Sub-sample with missing dependent variables: After introducing random missing values to the dependent variable.
3. Sub-sample with additional controls: Results with time-constant control variables (i.X) added to the models.

Summary of Findings

• For the full sample, results for jwdid (with notyet) and xthdidregress twfe (with never) align well, while jwdid (with never) yields slightly different coefficients. Stata's xthdidregress twfe forced notyet to never, though.
• For the sub-sample with missing dependent variables, differences between jwdid and xthdidregress become more pronounced.
• With additional controls, the discrepancies persist, particularly when comparing jwdid with csdid and xthdidregress aipw.

Question

Could you provide insights into:

1. How jwdid and xthdidregress twfe handle missing observations differently, particularly in unbalanced panels?
2. Why the results diverge when controls are added or when samples include missing dependent variables?
3. Whether additional adjustments to my syntax are necessary for more consistent comparisons?

[FernandoRios]

Dead Sung Ju
Thank you for your comments regarding jwdid and Stata's xthdidregress. I cannot say much about all the internal details of their implementation, since I only made small checks on their work and mine, and we have had some back and forth regarding how it should or not be implemented. Beyond that, the decisions made for jwdid are mine alone, guided on the papers by Wooldridge, but based on my own interpretations.
I lll try to go over your example, but so far one point that may be of interest. Stata's Never its different from JWDID never. In fact, last time I checked, They do not have an equivalent to "never", so those results will not match.
For the rest, I will have to look at what is going on.
Edit:
One important aspect tho, that may be explaining the differences, is how you define Gvar.
with jwdid, GVAR is predefined. So regardless of how you drop the data, Gvar does not change for a given unit.
However, with xthdidregress, GVAR is not predefined, and instead determined by "treatvar". Thus, depending on which observations are dropped, Gvar could change, causing a difference in the estimations.
F

[Sung Ju Cho]

Dead Sung Ju
Thank you for your comments regarding jwdid and Stata's xthdidregress. I cannot say much about all the internal details of their implementation, since I only made small checks on their work and mine, and we have had some back and forth regarding how it should or not be implemented. Beyond that, the decisions made for jwdid are mine alone, guided on the papers by Wooldridge, but based on my own interpretations.
I lll try to go over your example, but so far one point that may be of interest. Stata's Never its different from JWDID never. In fact, last time I checked, They do not have an equivalent to "never", so those results will not match.
For the rest, I will have to look at what is going on.
Edit:
One important aspect tho, that may be explaining the differences, is how you define Gvar.
with jwdid, GVAR is predefined. So regardless of how you drop the data, Gvar does not change for a given unit.
However, with xthdidregress, GVAR is not predefined, and instead determined by "treatvar". Thus, depending on which observations are dropped, Gvar could change, causing a difference in the estimations.
F

Thank you for the detailed response and for pointing out the key differences between how jwdid and xthdidregress handle "never" and the definition of GVAR. Your clarification is particularly helpful in understanding the potential sources of divergence in the estimations.

I’ll revisit my code with this in mind and examine how these differences might be influencing my results. I also look forward to any additional insights you might provide after reviewing the example.

Thanks again for your time and expertise!

[Ridwan Sheikh]

Dear FernandoRios I hope you are doing fine.

I was trying to replicate the effect of regional trade agreements (RTAs) using the ETWFE framework. This is very similar to the approach outlined in two of your papers:

1. Nagengast et al. (2024). The European single market and intra-EU trade: An assesment with heterogeneity-robust difference-in-differences method. School of Economics, LeBow College of Business, WP 2024-05
2. Mora et al. (2024). The EMU effects on trade: A re-assesment accounting for staggered treatment addoption, StructuraEconomica, WPAE-2024-2407

The definition of variables in my case is given below:
RTA_i, take the value of year when iso_i first became the member of regional trade agreement
RTA_k, take the value of year wheniso_k first became the member of regional trade agreement.

In order to define gvar for the jwdid commond to work, I define the group variable as :

egen RTA_ik = rowmax(RTA_i  RTA_k)
replace RTA_ik = . if missing(RTA_i) | missing(RTA_k)

Here, RTA_ik denotes the first year, in which both the exporter and importer of country pairs (iso_i and iso_k) were part of RTA. In other words, RTA_ik denotes the treatment cohort g if the RTA onset for both iso_i and iso_k was in year g. However, there are two observations to be made:

- if either of RTA_i or RTA_k or both RTA_i and RTA_k are missing, then RTA_ik takes the missing value.
- if RTA_i takes the value of 1995 (say, country i first became the member of RTA in 1995)and RTA_k takes the value of 2000(say, country k first became the member of RTA in 2000), then RTA_ik takes the value of 2000 (that is., i and k were first both memebers of RTA in year 2000).

Question 1). - How to treat missing values of RTA_ik, should we treat them as zero.
- Is my definition of RTA_ik correct.

In my case, I have data on both intranational and international trade flows. However, to accomodat conditional parallel trends in jwdid, I also use certain gravity variables, distance (DIST), contiguity (CNTG), common language (LANG) and colonial status (CLNY), and joint membership to WTO, EU and GATT. Here, for all the observations with i=j (except, DIST), i replace all the values with zero, including my treatment variable RTA_ik.

local gravvars "CNTG LANG CLNY WTO EU GATT RTA_ik" 
foreach x of local gravvars{
replace `x'=0 if iso_i==iso_k
}

Besides, I also use exporter-time, importer-time and country-pair fixed effects, and define time-varying border indicator to account for the common effects of globalization as:

egen idt_ci = group(iso_i year)
egen idt_ck = group(iso_k year)
egen id_ci_ck = group(iso_i iso_k)


gen brdr=1 if iso_i!=iso_k
replace brdr=0 if brdr==.
egen brdr_time = group(brdr year)

xtset id_ci_ck year

Question 2) Am I doing it right, and do I need to create the additional variables to implement jwdid?

Looking for to hearing from you.

Thanks and regards,
(Ridwan)