Part I . In Part I, we will study return predictability using monthly returns. a. We will again use the stock market data set used in Robert Shiller's book, Irrational Exuberance. Repeat the steps in the previous lab "Expected Return" to import the data: First, copy, paste, and run the following code chunk to define the load_data function: load_data <- function(data_file) { load (url (paste0 (r' {https:/ /web. ics . purdue . edu/~zeng135/}', data_file, '.rdata') ), envirgGlobalEnv)} Next, call the load_data function to load the data: load_data ("Shiller") An xts data object named Shiller will be loaded to your R environment. Remember to load the quantmod package in order to access the content of an xts object: library (quantmod) I Then calculate the returns on the S&P composite index. b. Recall that the acf function can be used to calculate and plot the autocorrelations of a time series. Use acf to study the autocorrelation structure of the returns. What is your observation? c. Compute the autocorrelation of the returns for lag of 1 month. d. Regress current month's return on the previous month's return. Do you see evidence of return predictability? Why