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Suppose we want to predict sales for Johnson & Johnson. Using observations from the first quarter of 1985 to the fourth quarter of 2001, we
Suppose we want to predict sales for Johnson & Johnson. Using observations from the first quarter of 1985 to the fourth quarter of 2001, we estimate an AR(1) model using ordinary least squares on the first-differenced data. We estimate the following equation: (In Sales, In Sales -1) = bo + b1 (In Sales 1 In Sales,-2) + 7. The following table shows the results of the regression Log Differenced Sales: AR(1) Model Johnson & Johnson Quarterly Observations, January 1985-December 2001 Regression Statistics R-squared 0.1957 Standard error 0.0408 Observations 68 Durbin-Watson 2.1226 Coefficient Standard Error t-Statistic Intercept 5.1334 -1.6210 Lag 1 0.0297 0.0058 -0.1956 0.1207 Autocorrelations of the Residual Autocorrelation Standard Error Lag t-Statistic 1 2 3 4 -0.0616 -0.3634 -0.1355 0.6030 0.1213 0.1213 0.1213 0.1213 -0.5083 -2.9969 -1.1170 4.9728 A. Using the regression output in the above table, determine whether the estimates for bo and b1 are valid. B. If this model is mis-specified, describe the steps we should take to determine the appropriate autoregressive time-series model for these data. Suppose we want to predict sales for Johnson & Johnson. Using observations from the first quarter of 1985 to the fourth quarter of 2001, we estimate an AR(1) model using ordinary least squares on the first-differenced data. We estimate the following equation: (In Sales, In Sales -1) = bo + b1 (In Sales 1 In Sales,-2) + 7. The following table shows the results of the regression Log Differenced Sales: AR(1) Model Johnson & Johnson Quarterly Observations, January 1985-December 2001 Regression Statistics R-squared 0.1957 Standard error 0.0408 Observations 68 Durbin-Watson 2.1226 Coefficient Standard Error t-Statistic Intercept 5.1334 -1.6210 Lag 1 0.0297 0.0058 -0.1956 0.1207 Autocorrelations of the Residual Autocorrelation Standard Error Lag t-Statistic 1 2 3 4 -0.0616 -0.3634 -0.1355 0.6030 0.1213 0.1213 0.1213 0.1213 -0.5083 -2.9969 -1.1170 4.9728 A. Using the regression output in the above table, determine whether the estimates for bo and b1 are valid. B. If this model is mis-specified, describe the steps we should take to determine the appropriate autoregressive time-series model for these data
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