Consider the data in Table 7.1. The data pertain to consumption of cups of coffee per day (Y) and real retail price of coffee (X) in the United States for years 1970-1980. Applying OLS to the data, we obtain the following regression results:

The results make economic sense: As the price of coffee increases, on average, coffee consumption goes down by about half a cup per day. The r2 value of about 0.66 means that the price of coffee explains about 66 percent of the variation in coffee consumption. The reader can readily verify that the slope coefficient is statistically significant.

From the same data, the following double log, or constant elasticity, model can be estimated:

Since this is a double log model, the slope coefficient gives a direct estimate of the price elasticity coefficient. In the present instance, it tells us that if the price of coffee per pound goes up by 1 percent, on average, per day coffee consumption goes down by about 0.25 percent. Remember that in the linear model (7.8.8) the slope coefficient only gives the rate of change of coffee consumption with respect to price. (How will you estimate the price elasticity for the

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Rules Of The Rich And Wealthy

Rules Of The Rich And Wealthy

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