Madam, Thanks for the video. Kindly clarify my doubt....my univariate series (yield of oilseeds per acre) is non-stationary as per adf.test. I converted the data to log and still the log series is non-stationary (p value 0.42). Then i created the differenced series of original series using diff command. Now the series is stationary. Is this the right method? However I have seen in couple of videos that takes log difference only.
Hello madam, my data was the fourth difference becomes the stationary in adf test but we used Arima function , we get the second difference becomes the stationary but which one best model madam. Please suggest me madam.
it was a very nice explainations ma'am and very helpful. ma,am my data is not getting stationary at level 2 so can we perform further levels ? or if not then can we perform AIRMA test even without getting stationary? hoping for the quick revert. thankyou ma'am.
@@dr.shobhak6764 mam how did you get that lnsale data? by running vars package? if yes how to put it in excel file to run command auto.arima(lnsales). As i m not able to understand that from where we will get the ln values? i have seen pervious videos of yours for the optimum lag values but if we run the commond (vars) than how will we get the data in the screen. Sorry for the silly questions but it will be great help if you revert me on this. Thankyou.
Madam, reference to my earlier query, can I straight away use ARIMA to the original series ? Will auto.arima() automatically difference the series and convert it into stationary?
Hello ma'am thanks for the video, but when I m using command auto.arima(arsales,ic = ,trace= true)...there is a error argument y is missing, with no default. What should I do?
In my data was the third difference becomes the stationary in add test but the Arima function, we get the second difference becomes the stationary therefore which model is the best madam.
Madam, Thanks for the video. Kindly clarify my doubt....my univariate series (yield of oilseeds per acre) is non-stationary as per adf.test. I converted the data to log and still the log series is non-stationary (p value 0.42). Then i created the differenced series of original series using diff command. Now the series is stationary. Is this the right method? However I have seen in couple of videos that takes log difference only.
Hello madam, my data was the fourth difference becomes the stationary in adf test but we used Arima function , we get the second difference becomes the stationary but which one best model madam. Please suggest me madam.
it was a very nice explainations ma'am and very helpful. ma,am my data is not getting stationary at level 2 so can we perform further levels ? or if not then can we perform AIRMA test even without getting stationary? hoping for the quick revert. thankyou ma'am.
If it's not stationary at level 2 then go for Toda Yamato test
@@dr.shobhak6764 mam how did you get that lnsale data? by running vars package? if yes how to put it in excel file to run command auto.arima(lnsales). As i m not able to understand that from where we will get the ln values? i have seen pervious videos of yours for the optimum lag values but if we run the commond (vars) than how will we get the data in the screen. Sorry for the silly questions but it will be great help if you revert me on this. Thankyou.
Madam, reference to my earlier query, can I straight away use ARIMA to the original series ? Will auto.arima() automatically difference the series and convert it into stationary?
Listen to the video carefully
mam please further do a video on ARCH & GARCH model
K sure
Thanks for the video. Is the .csv data file available somewhere?
Hello ma'am thanks for the video, but when I m using command auto.arima(arsales,ic = ,trace= true)...there is a error argument y is missing, with no default. What should I do?
As u told in the start of the lec that u also explain Arma but u just explain arima not Arma kindly share codes of Arma if u have ?
In this video, you are not check stationary, why madam.
Already I have done a separate video so I skipped it
Ok madam
I have doubts about the below question madam.
What's the question
In my data was the third difference becomes the stationary in add test but the Arima function, we get the second difference becomes the stationary therefore which model is the best madam.