[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]

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]

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]

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]

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

[Sean Harris]

Hi, Can anyone help me?
im experiencing heteroskedasticity within my ARDL and im not sure how to get round this

Thanks

[Sebastian Kripfganz]

Please see Slide 12 of my presentation at the 2018 London Stata Conference about the formulation of the model in EC form. The dependent variable in the EC representation is the first difference of depvar. Regarding the time series operators, please consult the following Stata help file:

help tsvarlist

[Anil Raj]

Sir, please tell me the difference between D1, LD, L2D, L3D etc in SR output. Does it means first difference(D1), one value Lagged (LD), 2 lags (L2D) ? Also how to interpret depvar's LD (its own first lag?) Why the output is showing the ec test result as "D.depwar" ? Has it estimated with first difference?Thank you.

[Anil Raj]

Thank you so much Dr.Sebastian.

[Sebastian Kripfganz]

It means that both the speed-of-adjustment and the LR coefficients are "irrelevant" when there is no LR relationship, in the sense that the speed-of-adjustment coefficient is not statistically significantly different from zero. This conclusion can in itself be "relevant".

[Anil Raj]

Thank you so much for the quick reply. So does it mean that the speed of adjustment is irrelevant if there is no LR ? Then we should interpret SR results only ?

[Sebastian Kripfganz]

The p-value of the speed-of-adjustment term in the regression output does not have a meaningful interpretation. Instead, the p-value of the bounds t-test reported by estat ectest should be considered.

The p-values of the LR and SR coefficients have the usual interpretation. Note that the LR coefficients become meaningless if you cannot reject the null hypothesis that the adjustment coefficient equals zero, i.e. you should not attempt to interpret the LR coefficients if the bounds test does not provide evidence for the existence of a LR relationship. The SR coefficients can still be interpreted.

[Anil Raj]

Hello Everyone,
Can we interpret the p-values of adj term, LR and SR as normal? In my case the adj coeff is -0.072 with a p value of 0.215 and in LR indepvar1 coeff is 1.77 with p value 0.020 and indepvar2 is -1.18 with p value 0.125. I have read in one of the topic that if p value is not significant there is no long term relation. I feel a bit confused here. In this case if there is no long term relationship as per the bounds test results should we interpret the LR with significant p value or should we avoid it LR and interpret only SR results?
Please help.

[Sebastian Kripfganz]

The short-run coefficients in the EC representation are linear functions of the underlying ARDL coefficients. The ARDL coefficients (without option ec) are less easily interpretable. See my comment #340 about the interpretation of the short-run coefficients.

If there is no level relationship, then you can ignore the long-run effects (not the short-run effects). You would still interpret the short-run effects from the EC representation.

[Anil Raj]

@Dr.SebastianThanks a lot for the clarification. One more doubt please. When I run ardl regression without ec (ardl1.jpg) and with ec(ardl2), what is the difference in coefficients of ardl1 and short run coefficients of ardl2? How do we interpret it ? I have gone through your presentation and in page 16 you have said that the short-run accounts for short run fluctuations not due to deviations from long run equilibrium.

In my model the bouds test indicate that there is no levels relationship, so it means that I should ignore the short run coefficients listed in ardl2 ? ie with ec and interpret the ardl1 (with out ec) coefficents ?

Stata screen shots attached.

Kindly help.