thank you for these informative articles. Please answer my ambiguity.

All my time series variables are stationary at first difference, now I want to perform diagnostic test such as VIF, Heteroscedasticity, autocorrelation, normality test structural break test etc. Should I use all Differenced variables in the diagnostic test?
Many Thanks!

The trend disappears only after the second differentiation as indicated by the graphical representation of second differencing of GDP. However, the graphical representation of the first differencing of GDP depicts an upward trend. The dickey fuller test for second difference is based upon the trend depicted in the first differencing of GDP.

Also, applying the stationarity test is important because sample dataset includes the economic variables of the Indian economy from 1996 to 2016. The period of financial crisis is just a sub-sample of this dataset. Stationarity is used to analyse the movement of the variables over a period of time. Moreover, the time series need to be stationary for correlation and regression analysis. Otherwise, these results would be misleading.

Hi! Priya. Thank you For this article which is of great use. Besides, I would like to know why you add the trend option while doing the Dickey Fuller test for d2 of GDP as from the figure it seems the upward trend disaprear. Thanks. Another question is the figure shows that the data seems to be much more volatile during financial crisis. Why it is still tested to be stationary? Thanks again.

3 months & 3 weeks ago

I really want to appreciate it, but if you have YouTube, it’s really cool, it’s very educational and useful

^{}3 months & 2 weeks ago

Thanks. We do have a youtube channel: https://www.youtube.com/@projectguru

1 year & 8 months ago

panel data); the advantages of panel data.

4. Define A Fixed Effect model, its basic assumption and Estimation of Panel Data

1 year & 9 months ago

Thank, but may questions is how to select lag length for ADF? which is zero here?

2 years & 3 months ago

Hello,

thank you for these informative articles. Please answer my ambiguity.

All my variables are stationary at first difference so which technique should I proceed now?

2 years & 3 months ago

Hello,

thank you for these informative articles. Please answer my ambiguity.

All my time series variables are stationary at first difference, now I want to perform diagnostic test such as VIF, Heteroscedasticity, autocorrelation, normality test structural break test etc. Should I use all Differenced variables in the diagnostic test?

Many Thanks!

1 year & 8 months ago

How can identify the stationarity

2 years & 10 months ago

How do I go about variable which is stationary and without unit root after using trend regression?

5 years & 1 week ago

The trend disappears only after the second differentiation as indicated by the graphical representation of second differencing of GDP. However, the graphical representation of the first differencing of GDP depicts an upward trend. The dickey fuller test for second difference is based upon the trend depicted in the first differencing of GDP.

Also, applying the stationarity test is important because sample dataset includes the economic variables of the Indian economy from 1996 to 2016. The period of financial crisis is just a sub-sample of this dataset. Stationarity is used to analyse the movement of the variables over a period of time. Moreover, the time series need to be stationary for correlation and regression analysis. Otherwise, these results would be misleading.

5 years & 1 week ago

Hi! Priya. Thank you For this article which is of great use. Besides, I would like to know why you add the trend option while doing the Dickey Fuller test for d2 of GDP as from the figure it seems the upward trend disaprear. Thanks. Another question is the figure shows that the data seems to be much more volatile during financial crisis. Why it is still tested to be stationary? Thanks again.

5 years & 4 months ago

Tried both but they all increase in the same proportion.

5 years & 6 months ago

Very helpful indeed. Your explanation is very clear.