[Sebastian Kripfganz]

You can use robust standard errors as explained in my post #239 earlier in this thread.

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

[Daiana Sparta]

Hi! I'm trying to forecast with an ardl model but I keep encountering the same problem: missing values encountered. Could you help me!!!!??? I'm really stuck here!

This is my code and results:

. ardl lr_total litcrmt_i lpbi_limit_sa if time < tq(2019q2), ec maxlags(. . .) maxcombs(80000) exog(dljet_fuel dlseats_intl D1 D2 D3) regstore(ardlreg)

***FORECAST
. estimates store ardlreg

. forecast create ardlreg
Forecast model ardlreg started.

. forecast estimates ardlreg, names(dlrtotal_hatf)
Added estimation results from regress.
Forecast model ardlreg now contains 1 endogenous variable.

. forecast exogenous litcrmt_i lpbi_limit_sa time dljet_fuel dlseats_intl D1 D2 D3
Forecast model ardlreg now contains 8 declared exogenous variables.

. forecast solve, prefix(f_) begin(tq(2019q2)) end(tq(2021q4))

Computing dynamic forecasts for model ardlreg.
----------------------------------------------
Starting period: 2019q2
Ending period: 2021q4
Forecast prefix: f_

2019q2: ...............
2019q3:
missing values encountered
Missing values were encountered while attempting to solve the model at time 2019q3. Variable dlrtotal_hatf evaluates to
missing.
r(416);

[Sebastian Kripfganz]

My guess is that your exogenous variables themselves have missing values from period 2019q2 onwards. You cannot predict future values of your dependent variable without knowledge or predictions of the future values of your exogenous variables.

[Daiana Sparta]

My guess is that your exogenous variables themselves have missing values from period 2019q2 onwards. You cannot predict future values of your dependent variable without knowledge or predictions of the future values of your exogenous variables.

I have double checked and all my exogenous variables have data until 2021q4.

When I run the model without the ec or ec1 options I can get forectasts, but with those options on I could never get the forecast because of this error.

[Sebastian Kripfganz]

Can you please try adding the following line before your forecast solve line:

forecast identity lr_total = L.lr_total + dlrtotal_hatf

[Daiana Sparta]

Can you please try adding the following line before your forecast solve line:

forecast identity lr_total = L.lr_total + dlrtotal_hatf

It works now!!! Thank you very much!!!!!

[Minyoung Yang]

Hello Everyone,

1. I tried to do bound testing using ARDL model with one dependent variable (Y) and three independent variables (X1, X2, X3).
In this case, if I want to know the long-run relationship with Y and X1, and Y and X2 separately, can I use code of "e"?
I mean, I thought variable X3 is necessary for variable Y, but I don't need to know the long-run relationship with Y and X3.
Which means, in this case,

code:

ardl Y X1, e(X2 X3) ec
estat btest

ardl Y X2, e(X1 X3) ec
estat btest

is available?

2. Also, as far as I know, if there are multiple cointegrations in the model, I can't use the ARDL approach.
Is that means I need to use Johansen and Juselius (1990) test and find the rank of the model?

3. Through the study about literature, I set the process about cointegration like this

• First, check the stationary and integrated status of variables.
• If the variables are I(0) or I(1), we can use the ARDL bound test (and preferred because of several advantages).
• Before using ARDL approach, we need to check the rank of the model for reducing unnecessary step (if there are multiple cointegrations, the ARDL approach is meaningless).
• After finding the number of ranks, do the bound testing.
• Because the Johansen cointegration test is appropriate for a model with a large number of sample, we need to check our model again by switching the position of independent and dependent variables.
• If there are bi-directional (not means direction of causality) relation within the model, we can't use the ARDL approach.
• If only one relation exists, we can say our model is cointegrated.

4. Finally, in the Stata, can we do "VECM Granger causality" process? How about using ARDL models residuals?

I'm very appreciated if anybody answers me (even partially, it's fine).
Thank you.

[Sebastian Kripfganz]

1. If you can justify that X3 does not affect the long-run relationship, you can indeed use the exog() option. Note however that ardl will not obtain an optimal lag order for this variable in that case. If you want to include lags of X3, you need to specify them as well in the exog() option.

2. Basically, yes. But you can include multiple variables in the same cointegrating relationships, i.e. Y could be cointegrated with X1 and X2 jointly. Also, there could be multiple cointegrating relationships as long as there is only one that includes Y. For example, if there is a cointegrating relationship between Y and X1 and another one between X1 and X2, an ARDL model with Y as the dependent variable would be possible.

[Minyoung Yang]

Thank you for your answer.

If possible, can I ask an additional question about your answer?

1. If I do the ARDL Bound test just with Y and X3 (in my previous question) and find there is no cointegration, can I use this variable as an exogenous variable?

2. According to the basic concept of cointegration, I can understand your answer. But then, I just confused about the concept of "multiple cointegrations".
Is that means I need to check the models like following using ARDL Bound test?

• Y ~ X1 X2 X3
• X1 ~ Y X2 X3
• X2 ~ Y X1 X3
• X3 ~ Y X2 X3

Of course, in this case, I just separate the independent and dependent variables (which means I didn't make the ARDL model because of convenience).

3. This one is an extra question about the variable itself. By the definition, we need to use "level" variance when we try to find a long-run relationship (not only the case of ARDL).
If we use differenced level variables, because it loses their long-run property, it can't appropriate analysis (sometimes spurious regression that all of us don't want).
But, sometimes, actually more frequently than I imagine, many papers and articles use differenced level variables.
Is that means, if we have enough number of data (so we can less concern about the loss of information), can we say differentiation of our variables as the econometric treatment?
If so, is it possible to do the ARDL approach with differenced level data?

Actually, the more I study, the more confusing it seems.
So I would be very grateful if you could give me an explanation about that.

Again, thank you very much for your favor.

[Sebastian Kripfganz]

1. If X1 and X2 are relevant predictors of Y but you exclude them from your specification, then your estimates might suffer from an omitted variables bias. If you are using all variables together, i.e. ardl Y X1 X2 X3, ec, and you find that X3 is insignificant in the long-run relationship, then you could simplify your model by specifying X3 as an exogenous variable instead.

2. You cannot use the ARDL bounds test to perform these multiple checks. Running all of these tests would already violate the assumption that there is at most one long-run relationship involving Y. In other words, once you find a long-run relationship in the first model, the other three specifications immediately violate the underlying assumption and the corresponding bounds test becomes invalid.

3. Generally, you should only use differenced data if there is no long-run relationship among the variables. If you look at the regression output of the ec representation, then without a long-run relationship only differenced variables would remain. That means, if there is a long-run relationship but you only use differenced variables, you would have again an omitted variables problem.

[Minyoung Yang]

Thank you for your rapid and clear answer.

Now, I can understand that I need to do more study on this topic.
Also, I will try to avoid applying it in the wrong way.

Thanks.

[Slah Slimani]

Hello everyone,
I am using a PANEL ARDL model under STATA and I want to know if there is a stata command that allows to execute the Bound test .... Thank you very much for answering me

[Sebastian Kripfganz]

The ardl command is for use with a single time series only and does not support panel data. The bounds test itself is only valid for time series data.

[Mette Marit Johansen]

Hi Sebastian Kripfganz

My supervisor advice me to include dummies also in the LR relationship. It seems like you only include the dummies in the SR relation (ref. www.stata.com/meeting/chicago16/slides/chicago16_kripfganz.pdf) ARDL.pdf ARDL.pdf ARDL.pdf . What are your thoughts on this?

Does it make sense to also include the dummies in the static long run equation? If so, what are the interpretation of the estimatet coefficients in the LR relation?

[Sebastian Kripfganz]

I guess it might make sense to do that in certain situations. The dummies in the LR relationship than might account for a structural break in that relationship. You can achieve this by simply specifying the dummy variables together with the other independent variables and assigning zero lags with the lags() option for them. Note however that the bounds test would no longer be valid in such a situation.