hi, i am quite new with SAS and i am struggling to understand the ADF process for selecting the correct number of lags
As a first step for the ADF test i tried to identify the correct number of lags that i have to include. I use monthly data, therefore i added ADF=12 lags and run the below code :
PROC ARIMA DATA= rethouse_reg1 ;
IDENTIFY VAR = DEPPC_forc STATIONARITY=(ADF=12) ;
then i looked at the ADF and Trend Correlation analysis :
i looked at the trend and correlation analysis graph (see below). My series doesn't have trend (based on the graph) and has mean different than zero (based on table below mean = 6.381432) therefore, i concluded that i have to use the single mean in ADF.
After this i looked at the ADF table (see at the bottom) in the single mean model and starting from the bottom (12 lags) I found that the 9 lags are statistical significant (0.0146<0.05). Based on this i concluded that i have to use 9 lags in the ADF test.
Then i looked at the ACF graph and counted the number of columns which are outside the shed blue area and found 11lags and i understood that i had to include 11lags for the ADF (compare to 9lags that found earlier)
my questions are :
1) Is the steps that i followed correct for selecting the appropriate number of lags for the ADF?
2) If yes, which one of the above results for the correct number of lags i can trust and include in my ADF test and why?
|Name of Variable = DEPPC_forc|
|Mean of Working Series||6.381432|
|Number of Observations||277|
|ugmented Dickey-Fuller Unit Root Tests|
|Type||Lags||Rho||Pr < Rho||Tau||Pr < Tau||F||Pr > F|
I do not master the peculiarities and intricacies of the ADF test anymore. I did, but I haven't used it since a long time.
It takes a while before you can fluently use it (in my case at least).
However, I think this paper may help you out :
An Introduction to Testing for Unit Roots Using SAS®:
The Case of U.S. National Health Expenditures
Donald McCarthy, Department of Research and Evaluation, Kaiser Permanente
There are 10 occurrences of 'lag length' in the paper and a macro that implements the Ng and Perron (2001) approach.
Ng, S and P. Perron. 2001. “Lag Length Selection and the Construction of Unit Root Tests with Good Size and Power.” Econometrica, Vol. 69, No. 6: 1519–1554.
There's also a a rule of thumb based on the number of observations in the sample.
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