The figure below shows the estimated autocorrelation and partial autocorrelations of a time series of n = 60 observations. Based on these plots, we should. Lag AutoCorr -1 0 1 0 1.0000 1 0.9353 2 0.8875 3 0.8413 4 0.7938 5 0.7532 6 0.6906 7 0.6172 8 0.5809 9 0.5331 10 0.4894 11 0.4385 12 0.3822 13 0.3410 Partial -1 0 1 1.0000 0.9353 0.1016 0.0042 -0.0293 0.0272 -0.1883 -0.1681 0.2315 -0.0502 -0.0259 -0.0638 -0.0354 -0.0061 Select one: a. Fit an AR(1) model to the time series b. Transform the data by taking logs c. Fit an MA(1) model to the time series d. Difference the series to obtain stationary data
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Step 1: The autocorrelation plot shows a definite trend, indicating that the time series is not stationary. Show more…
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