hello all,
I have some variables (dependent as well as independent) that appear to perform better after taking the log (ln) of the return ln(pt/pt-1).
Before the transformation, it were just prices. Dickey fuller test (ADF) indicated that most of them were non-stationary.
Since I performed the ADF test before transformation, I believe I have made a mistake here. What is common? Doing ADF before or after transformation of the variables?
The thing is, when one transform a variable (in this case the natural log of returns) the data can become stationary. this is not problematic I believe (?), but:
1. in situation 1 (no transformation) --> ADF --> stationary as well as non stationary variables --> one might use the ARDL model;
2. In situation 2 (transformation) --> ADF --> everything is stationary due to transformation --> use standard OLS.
So what is a good approach according to you?
Second question: can you still use the ARDL model in case of stationary variables? or just only in case of a mixture of I(0) and I(1)?
I have some variables (dependent as well as independent) that appear to perform better after taking the log (ln) of the return ln(pt/pt-1).
Before the transformation, it were just prices. Dickey fuller test (ADF) indicated that most of them were non-stationary.
Since I performed the ADF test before transformation, I believe I have made a mistake here. What is common? Doing ADF before or after transformation of the variables?
The thing is, when one transform a variable (in this case the natural log of returns) the data can become stationary. this is not problematic I believe (?), but:
1. in situation 1 (no transformation) --> ADF --> stationary as well as non stationary variables --> one might use the ARDL model;
2. In situation 2 (transformation) --> ADF --> everything is stationary due to transformation --> use standard OLS.
So what is a good approach according to you?
Second question: can you still use the ARDL model in case of stationary variables? or just only in case of a mixture of I(0) and I(1)?
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