[Sebastian Kripfganz]

The dynardl package might be useful for that purpose.

[Kong Chantha]

Dear Sir/Madam
I know the command estat sbcusum for testing Parameter stability in model.

How Can STATA runs CUSUMQ ? how about Code/ command ?

thank you

[Sebastian Kripfganz]

We explain in our 2018 UK Stata Conference how to use the sbcusum command. There is also the community-contributed cusum6 command, which can be installed from SSC. It can be used after ardl like any other postestimation command for regress; see the "Postestimation" section of our presentation for general instructions.

[joseph Mgaya]

Greetings, I have these results from the test of Durbin Watson d-statistic( 9, 34) = 2.459839 and Breusch-Godfrey P value of 0.066, i don't feel comfortable with the P value, can i use Newey-West just to be safe? After applying Newey West, the significance of coefficients seems to have improved

[joseph Mgaya]

Greetings, I have these results from the test of Durbin“Watson d-statistic( 9, 34) = 2.459839 and Breusch“Godfrey P value of 0.066, i don't feel comfortable with the P value, can i use Newey-West just to be safe?

[Sebastian Kripfganz]

You shouldn't choose the standard errors based on whether this delivers "improved" statistical significance. In general, I would not expect the standard errors to become smaller - i.e., coefficient estimates to become more significant - when using Newey-West standard errors.

You are right that a p-value of 0.066 is not very comfortable. This could be used as a justification to use Newey-West standard errors. Alternatively, you might want to address the serial correlation in the first place by allowing for higher-order lags in the ARDL specification. If your sample size allows, you could increase the maximum lag order with the ardl option maxlag(), and/or use the AIC instead of the BIC model selection criterion.

[joseph Mgaya]

My sample size is 37 years of annual data.

[Sebastian Kripfganz]

That is relatively small for a time series analysis. Based on this additional information, I would not recommend any longer to increase the maximum lag order.

You might simply want to report both conventional and Newey-West standard errors instead of choosing between them.

[lorenabarberia]

Your package and working paper, as well as this thread are very helpful! Thank you. If you would like to use newey standard errors , I understand how you can use the reported results from the routine below to obtain the short and long-term effects of the model. However, I am not sure how you would conduct the bounds test considering using newey standard errors since one of the reasons you are correcting with newey is because you found autocorrelation present in the residual and the bounds test would also need to consider the new estimates. Any guidance you can offer would be much appreciated.

. webuse lutkepohl2
. quietly ardl ln_consump ln_inc, exog(L(0/3)D.ln_inv) trend(qtr) aic regstore(ardlreg)
. quietly estimates restore ardlreg
. local cmdline `"`e(cmdline)'"'
. gettoken cmd cmdline : cmdline
. newey `cmdline' lag(4)

The local cmdline contains the corresponding command line for the regress command (excluding the command name), not the ardl command. With the ardl option regstore(), the results are stored using the regress command; these are then subsequently recovered with the estimates restore command. The newey command eventually fits the same regression.

To see what is contained in the local cmdline, execute the above code and add the line

. display `"`cmdline'"'

[lorenabarberia]

Also, I am not sure how we could obtain estimates of the residual after newey commands folllowing ardl. The purpose is to verify if we have white noise residuals. I tried:

estimates store newey
estimates restore newey
predict residualnewy, resid

However, Stata reports "option r not allowed r(198);"

. webuse lutkepohl2
. quietly ardl ln_consump ln_inc, exog(L(0/3)D.ln_inv) trend(qtr) aic regstore(ardlreg)
. quietly estimates restore ardlreg
. local cmdline `"`e(cmdline)'"'
. gettoken cmd cmdline : cmdline
. newey `cmdline' lag(4)

The local cmdline contains the corresponding command line for the regress command (excluding the command name), not the ardl command. With the ardl option regstore(), the results are stored using the regress command; these are then subsequently recovered with the estimates restore command. The newey command eventually fits the same regression.

To see what is contained in the local cmdline, execute the above code and add the line

. display `"`cmdline'"'

[Sebastian Kripfganz]

The critical values of the bounds test rely on the assumption of iid errors. If there is remaining serial correlation in the errors, the bounds test is not reliable.

Note that the newey command does not correct the coefficient estimates. It only produces robust standard errors. Hence, if there was serial correlation in the residuals before running the newey command, there will still be serial correlation in the residuals afterwards. The residuals are unchanged.

[joseph Mgaya]

I used the Newey-West error estimator for the short run using ec1 but am missing results for variables that were d=0. How can I get these results?

[joseph Mgaya]

sorry I mean q=0

[lorenabarberia]

Sebastian Kripfganz In the ardl command, it is possible to specify the number of lags for the depedent variable and regressors using "lags(1 1 . . . .)." This is very helpful in some scenarios. I am wondering how you can include lags of the first differences using this same logic. Is there a way to implement this manually so you ensure a certain number of lags of the first differences of specific regressors?

[Sebastian Kripfganz]

The lags of the first-differenced terms in the error-correction representation (option ec or ec1) are determined as the number of lags in the level representation (as specified with option lags()) minus 1. If you want 3 lags of the first-differenced terms, specify option lags(4).