Read excel file, clean it and build ARIMA model to predict Argentina's monetary base change
The excel file was downloaded from official BCRA website (https://www.bcra.gob.ar/)
The first task to solve was the multi-heads columns obtained by transforming the excel file into a data frame.
After a bit of cleaning we got a dataframe with multiple columns indexes: DAILYCHANGE and DAILYSTOCK, both offering the same most important features but one shows the daily change and the other the total stock at a particular day. In addition DAILYCHANGE offers additional columns to show certain BCRA assets in more detail.
Our aim is to predict the feature held_by_public_(1) defined by: monetary circulation that is not in the possession of financial entities.
At first sight we can see that it is not stationary, then we must use differencing
where we apply Dicker-Fuller test obtaining a p value of 0.00046844795099990213
Then we get auto-correlation and partial-autocorrelation functions
Showing some effectiveness during the first two weeks until approximately 04/15/2024, when it begins to converge towards the media.