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# Estimation with AR(1) Serial Autocorrelation, Example 3

EMETRC-CO4YEJ

Suppose you estimate a model of the price of corn as a function of precipitation. The model has an AR(1) serial autocorrelation error structure, which you account for by estimating feasible generalized least squares. You estimate the coefficient associated with the precipitation variable to be $-0.78$, which is statistically significant at a $95\%$ confidence level.

How would you interpret this marginal effect?

A

A $1\%$ change in the precipitation level leads to a $-0.78\%$ change in the price level.

B

A one unit increase in precipitation level is associated with a $\$0.78$decrease in price. C A one unit increase in the change in the precipitation level is associated with a$\$0.78$ decrease in price.

D

A one unit increase in the change in the precipitation is associated with a $\$0.78$decrease in the change in price. E A one unit increase in the precipitation level is associated with a$\$0.78$ decrease in the change in price.