R program
Please no written by hand solutions
IN R program
ts=time series retr=return(monthly)
price <- data$OILprice
ts.plot(price)
price_ts = ts (price, frequency = 12, start = c(1990, 1))
ts.plot(price_ts)
lag = stats::lag
lag_price = lag(price_ts,k=-1)
retr = 100*((price_ts/lag_price)-1)
ts.plot(retr)
date_alt = as.Date(data$DATE , format = "%Y-%m-%d")
T = length(retr)
lag_return = retr[1:T-1]
df_return = data.frame(data$DATE[-(1:2)], retr[-1], lag_return)
In r program write the codes:
a)Calculate the persistence of return series, i.e. corr(yt, yt−1)=?.
b) Test for unit root in monthly returns (Dickey-Fuller test).
c) Estimate AR(1) and AR(2) models using the return series.
d) Do out-of-sample predictions using the models in part c.
e) How do we find which model does a better job at out-of-sample prediction?
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