DATA: Consumer Loans: Credit Cards and Other Revolving Plans, All Commercial Ban

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DATA: Consumer Loans: Credit Cards and Other Revolving Plans, All Commercial Ban

DATA: Consumer Loans: Credit Cards and Other Revolving Plans, All Commercial Banks
(CCLACBW027NBOG)
Transform the dataset to monthly data and apply a SARIMA(p,d,q)(P,D,Q) model to the data
following those steps:
1- Import the data into R from FRED website.
2- After importing it into R, restrict the dataset to the period from April 2014 through end of 2019.
3- Split the data into training and testing set, where the training set covers the training period from
the first week in April 2014 until the last week of March 2019, and the testing set covers the
testing period from the first week of April 2019 until the end of the year.
4- Train your model on the training set then use the trained model to forecast during the testing
period and compare the results of the forecast to the testing set, according to the following
steps:
a. Identify the model parameters using ACF and PACF.
b. Identify the model parameters without having recourse to ACF and PACF.
c. Fit the model on the training data using auto.arima function in R.
d. Plot the model fit and the actual training set in one graph.
e. Use the trained model to create a forecast covering the testing period.
f. Plot the model’s forecast and testing dataset inn the same graph.
g. Test the model’s forecasting power for 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12 periods
ahead, and create a plot that portrays the forecast accuracy for periods 1 through 12.

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