Final answer:
To perform the required tasks, first graph the historic values, then compute the ACF and PACF, choose the orders p and q based on the criterion, and finally estimate the parameters and write the fitted equation. Comment on each result.
Step-by-step explanation:
To graph the series of historic values, you would first need to access the file assigned to you on d2l. Then, plot the values on a graph where the x-axis represents the period and the y-axis represents the values.
To compute the autocorrelation function (ACF) and partial autocorrelation function (PACF), use statistical software or programming languages like Python or R. These functions measure the correlation between a time series and its lagged values. Plot the ACF and PACF values on separate graphs to visualize the correlation.
To fit an ARMA(p,q) model to the historic series of prices, you need to select the orders p and q. The orders can be chosen based on the Akaike Information Criterion (AIC) or Bayesian Information Criterion (BIC). These criteria help to select the model with the best balance between goodness-of-fit and complexity. Evaluate different combinations of p and q and choose the model with the lowest AIC or BIC.
To estimate the parameters in the ARMA(p,q) model, you can use the maximum likelihood estimation (MLE) method. This method aims to find the parameter values that maximize the likelihood function of the observed data. Once the parameters are estimated, you can write the fitted equation of the ARMA(p,q) model, including only the significant parameters.
Remember to provide comments on each result, mentioning their significance or interpretation in at least one line.