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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
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?
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.
I do have another question. Everything regarding the ARDL has worked out fine for me, but I do wonder if it possible to illustrade the results with a graph? I am meaning to include how the independent variable of interest affect the dependent variabe, and a graph illustrating the effect first with the initial lags of the short run into the long run? I have not find how to illustrate an ARDL with a graph, so would be very helpfull if anyone had any solution.
You would not normally specify the variables in first differences. When called with the ec option, the command itself parameterizes the model in first-differenced form (with additional level error correction term).
When configuring an ardl in stata and all the variable are stationary at I(1) would you input the command "ardl d.lngdp d.lncpi d.hc, ec btest" for example. So would you have to difference all the variables before putting them in the command?
The varsoc command is not the most appropriate one for the single-equation ARDL model; it is more appropriate for VAR models. The ardl command does its own lag selection if you just use the aic or bic option, possibly varying the maximum lag order with the maxlags() option. For the bounds test, it is advisable to use a less parsimonious specification to ensure there is no remaining serial correlation; the AIC is therefore generally preferred over the BIC for this purpose.
Perfect, thank you very much. I really appreciate it.
The varsoc command is not the most appropriate one for the single-equation ARDL model; it is more appropriate for VAR models. The ardl command does its own lag selection if you just use the aic or bic option, possibly varying the maximum lag order with the maxlags() option. For the bounds test, it is advisable to use a less parsimonious specification to ensure there is no remaining serial correlation; the AIC is therefore generally preferred over the BIC for this purpose.
Olav Hose
I cannot recommend any p-hacking strategies. You could of course ask whether the model is correctly specified. There might be important variables missing, the maximum lag order may have to be increased to capture all the dynamics, variable transformations (log) might be needed (if not already done), etc. Once you are confident that the model specification is accurate, you then just have to accept the test result. Often, non-significance can be a meaningful result by itself.
Perfect, thank you very much. One final question, I tried out some of the proposals you gave, and when I add extra lags on some of the model, it become statistically significant. However, the lag order of that model is higher as proposed by the BIC criteria which I obtained from the varsoc command. I know it is important not to adjust my model until I get the results which I want, however, would having more or less lags as proposed by the criterium make the model biased?
Olav Hose
I cannot recommend any p-hacking strategies. You could of course ask whether the model is correctly specified. There might be important variables missing, the maximum lag order may have to be increased to capture all the dynamics, variable transformations (log) might be needed (if not already done), etc. Once you are confident that the model specification is accurate, you then just have to accept the test result. Often, non-significance can be a meaningful result by itself.
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