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*Since there is no autocorrelation, the ut and st are identical. The st are from Table 12.1.

*Since there is no autocorrelation, the ut and st are identical. The st are from Table 12.1.

The regression based on Table 12.3 is as follows: Yt = 2.5345 + 0.6145Xt (0.6796) (0.1087)

460 PART TWO: RELAXING THE ASSUMPTIONS OF THE CLASSICAL MODEL

This regression is much closer to the "truth" because the Y's are now essentially random. Notice that o2 has increased from 0.8114 (p = 0.7) to 0.9752 (p = 0). Also notice that the standard errors of fa and fa have increased. This result is in accord with the theoretical results considered previously.

12.5 RELATIONSHIP BETWEEN WAGES AND PRODUCTIVITY IN THE BUSINESS SECTOR OF THE UNITED STATES, 1959-1998

Now that we have discussed the consequences of autocorrelation, the obvious question is, How do we detect it and how do we correct for it? Before we turn to these topics, it is useful to consider a concrete example. Table 12.4 gives data on indexes of real compensation per hour (Y) and output per hour (X) in the business sector of the U.S. economy for the period 1959-1998, the base of the indexes being 1992 = 100.

First plotting the data on Y and X, we obtain Figure 12.7. Since the relationship between real compensation and labor productivity is expected to be positive, it is not surprising that the two variables are positively related. What is surprising is that the relationship between the two is almost linear,

TABLE 12.4 INDEXES OF REAL COMPENSATION AND PRODUCTIVITY, UNITED STATES, 1959-1998

Observation

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