17. Suppose we decide to use an autoregressive model with a seasonal lag because of the seasonal autocorrelation in the previous problem. We are modeling quarterly data, so we estimate Equation 15: (ln Salest − ln Salest−1) ¼ b0 þ b1(ln Salest−1 − ln Salest−2) þ b2(ln Salest−4 − ln Salest−5) þ εt. Exhibit 16 shows the regression statistics from this equation.

A. Using the information in Exhibit 16, determine whether the model is correctly specified.
B. If sales grew by 1 percent last quarter and by 2 percent four quarters ago, use the model to predict the sales growth for this quarter.

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EXHIBIT 16 Log Differenced Sales: AR(1) Model with Seasonal Lag Johnson & Johnson Quarterly Observations, January 1985-December 2001 Regression Statistics R Standard error 0.4220 0.0318 Observations Durbin-Watson 68 1.8784 Coefficient Standard Error t-Statistic Intercept 0.0121 0.0053 2.3055 Lag 1 -0.0839 0.0958 -0.8757 Lag 4 0.6292 0.0958 6.5693 Autocorrelations of the Residual Lag Autocorrelation Standard Error t-Statistic 1 0.0572 0.1213 0.4720 2 -0.0700 0.1213 -0.5771 3 0.0065 0.1213 -0.0532 4 -0.0368 0.1213 -0.3033