[mehmed han]

Hello Sebastian Kripfganz anl all;

I am new in panel ARDL. When I try to do panel ARDL, I got this message: "sample may not include multiple panels". So, I cannot do panel ARDL.
How can I solve this problem?
Thank you

[Sebastian Kripfganz]

The ardl command is for use with time-series data only. It does not support the estimation of panel ARDL models.

The following Statalist topics might be helpful:

• ARDL panel model in Stata
• Optimal lag length for ARDL in panel

In addition, you might want to search for mean-group (MG) and pooled mean-group (PMG) estimators. In Stata:

search mg
search pmg

[mehmed han]

Hello Sebastian;
Thank you for your help.
İf I can't do panel ARDL in Stata, how can I do it then?
Can I make it vith Eviews?

[Sebastian Kripfganz]

In addition to my previous post, the following Statalist discussion might be of interest regarding panel ARDL:
xtdcce2 - estimate CS-ARDL & CS-DL

[mehmed han]

Hello Sebastian;

Some people says you can use panel ARDL in stata by command by installing xtmg ve xtpmg and using pmg, ccemg, amg estimations.
What is your idea about that?
Thank you for your interest

[NIP NIP]

Hello all!

In the ARDL methology, there is 5 cases.
Could you please help me with some guidelines about when a case should selected over the others?

I'm trying to estimate a ARDL model as follows:

Y = Intercept + b1*Trend + b2*X1 + b3*X2

I selected case II (restricted intercepts; no trends) and then I added the "Trend" variable as a fixed regressor. Is this ok? Will bounds test and long run estimators be ok?

Thanks & Regards!

[Sebastian Kripfganz]

If you want to include a time trend, you should always specify the trend() option rather than directly specifying the trend variable as a regressor. Without seeing your Stata command line, I do not know what you mean by "fixed regressor". If there is a trend in your model, the only relevant cases are 4 or 5. Without the trend() option, the postestimation command for the bounds test does not know that there is a trend in the model and it would show you the wrong critical values and in the restricted case even compute the wrong F-statistic.

In many cases, it is advisable to use the restricted option, which results in case 2 without a trend or case 4 with a trend. For example, if there is a trend in the model but you do not restrict it, then your data-generating process would follow a quadratic time trend under the null hypothesis but a linear trend under the alternative hypothesis. Either of the two processes would usually not be in line with the observed data. With a restricted time trend, however, the deterministic trend component remains linear both under the null and under the alternative hypothesis.

Please ensure that you have the latest version of our ardl command. For details, please see the following new Statalist topic and please ask any further questions about this new command version in this new topic (to avoid any confusion with some outdated comments above): ARDL: updated Stata command for the estimation of autoregressive distributed lag and error correction models

[Mike Zyphur]

Hi Sebastian,
Thank you for your ARDL command! It's very helpful and incredibly well done. I was wondering if you would mind giving me your thoughts on something?

Is there a simple way to sum the short-run lagged effects on a single predictor x when it has many lags? I ask because I'm running many ARDL models in a single .do file. The problem created here is two-fold. On the one hand using lag selection via BIC or AIC will make each ARDL model's lags unique, and therefore no single 'lincom' or 'test' command will work to sum all of the lagged short-run effects for a single predictor. On the other hand the reported output is not the same (using ec1) as the underlying regression model that would be used to construct the sum of the short-run effects using lincom. Of course, the second problem can be solved algebraically, but not the first. In combination, these issues make it very difficult to compute the sum of lagged short-run effects.

Is there a simple solution to this issue that you know of? I've read through all of the ARDL documentation as well as all 22 pages of this thread, but I've not found this question asked or answered previously, which kind of surprised me. Shouldn't it be common to conduct an overall test of the short-run effect of a predictor's short-run effects when it has many lags included in the model?

Thanks again!!
Mike

[Sebastian Kripfganz]

Here is an algorithm that should work:

webuse lutkepohl2
ardl ln_consump ln_inc ln_inv, ec

loc varname "ln_inv"

local lag = el(e(lags), 1, colnumb(e(lags), "`varname'"))
local --lag        // the number of lags in the EC form is one less than the number of lags in levels

if `lag' >= 0 {
    loc lagsum "D.`varname'"
    loc laglist "D.`varname'"
    forv l = 1/`lag' {
        loc lagsum "`lagsum' + L`l'D.`varname'"
        loc laglist "`laglist' L`l'D.`varname'"
    }
    lincom `lagsum'
    test `laglist'
}

ardl stores the number of lags in the level representation of the model in the matrix e(lags). The relevant lag order can be extracted from that matrix. The above code can be easily adjusted to work with the ec1 instead of the ec option by removing the if `lag' >= 0 condition.

[Mike Zyphur]

Thank you, Sebastian, it works splendidly!

[NIP NIP]

TY! Sebastian.

1 noconstant
2 constant restricted
3 constant
4 constant trendvar(trendvarname) restricted
5 constant trendvar(trendvarname)

I used the 4th case since I need an intercept and a linear trend. I created my own trend variable, Trend2.

By fixed regressor I meant exogenous variable.

I used this the following command:

ardl Y X1 X2, exog( D1 D2 ) lags(4,0,0) constant trendvar( Trend2 ) restricted ec

If this is incorrect in any way, please, say it! :D

[Valeria Kos]

Dear all,

I haven't dealt with the ARDL model before so I would like to seek your advice. I have run the bounds test and the null hypothesis is accepted. I suppose it means that all the variables are stationary (but I feel a bit conflicted about it since augmented Dickey-Fuller test results showed integration of order one for two variables and stationarity for another one). What are my next steps? There is no long-run relationship, can I use ARDL estimated to test short-run effects?

Thank you in advance.

Here are the testing results

H0: no level relationship F = 4.250
Case 5 t = -2.077

Finite sample (2 variables, 39 observations, 3 short-run coefficients)

Kripfganz and Schneider (2018) critical values and approximate p-values

| 10% | 5% | 1% | p-value
| I(0) I(1) | I(0) I(1) | I(0) I(1) | I(0) I(1)
---+------------------+------------------+------------------+-----------------
F | 4.381 5.454 | 5.274 6.494 | 7.370 8.919 | 0.111 0.218
t | -3.121 -3.669 | -3.470 -4.052 | -4.185 -4.830 | 0.474 0.645

[Sebastian Kripfganz]

The null hypothesis of the bounds test is that there exists no level relationship. This does not imply that the variables are stationary. In contrast, since neither the test based on the F-statistic nor the one based on the t-statistic reject the null hypothesis, this result would be in line with a nonstationary dependent variable that is not cointegrated with any of the (nonstationary) independent variables.

You can still interpret the short-run effects from the estimated error-correction representation of the model. Given that there is no long-run level relationship, you can also reestimate a more parsimonious (more efficient) ARDL model (without the ec option) where all variables are transformed into first differences (before they enter the model).

Further discussion of the new version of the ARDL command in the following Statalist topic:
ARDL: updated Stata command for the estimation of autoregressive distributed lag and error correction models

[Valeria Kos]

Thank you very much for your promt reply, Professor Kripfganz.

Also, I would like to quote your and Mr. Schneider's presentation at STATA Conference in 2018 in the bibliography section of my Master thesis at Bocconi University since it was instrumental for my research. I want to verify if it is okay with you?

[Sebastian Kripfganz]

Sure, we are happy about every reference to our work.