Estimating Moving Average (MA) Model in R

We will perform the estimation using the msft_ts time series that we created earlier in the first lesson. If you don't have the msft_ts loaded in your R session, please follow the steps to create it as specified in the first lesson.

Let's start by creating a plot of the original data using the plot.ts() function.

1> plot.ts(msft_ts, main="MSFT prices", ylab="Prices")
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We will fit the MA model to this data using the following command:

The output contains many things including the estimated slope (ma1), mean (intercept), and innovation variance (sigma^2) as shown below:

1> msft_ma
2Call:
3arima(x = msft_ts, order = c(0, 0, 1))
4Coefficients:
5         ma1  intercept
6      0.8602    55.2735
7s.e.  0.0259     0.2603
8sigma^2 estimated as 4.952:  log likelihood = -559.83,  aic = 1125.66
9>
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The msft_ma object also contains the residuals (εt ). We can extract the residuals, using the residuals() function in R.

Once you find the residuals εt, the fitted values are just X̂_t=X_t−ε_t. In R, we can do it as follows:

We can now plot both the original and the fitted time series to see how close the fit is.

1ts.plot(msft_ts)
2points(msft_fitted, type = "l", col = 2, lty = 2)
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As we can see, the model does not provide a good fit for the original series. One possible reason is that our original series was not stationary. We can fit the ARIMA model with first order differencing by passing the parameter I=1.

1> msft_ma <- arima(msft_ts, order = c(0, 1, 1))
2> residuals <- residuals(msft_ma)
3> msft_fitted <- msft_ts - residuals
4> ts.plot(msft_ts)
5> points(msft_fitted, type = "l", col = 2, lty = 2)
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