04 Jun 3.18 Refer to Example 2.1 on years of schooling and average
Question
3.7
Based on the data for the years 1%2 to 1977 for the United States: Dale Bails and Larry Peppersla obtained the following demand function for automobiles: = 5807 + 3.241t – 0.22 = (1.634) where Y=retail sales of passenger cars (thousands) and X=the real disposable income (billions of 1972 dollars). Note: The se for bl is not given
. a. Establish a 95% confidence interval for 82.
b. Test the hypothesis that this interval includes 82 = 0. If not: would you accept this null hypothesis?
c. Compute the t value under ir-10:82 = 0. Is it statistically significant at the 5 percent level? Which t test do you use, one-tailed or two-tailed, and why?
3.9
‘You are given the following data based on 10 pairs of observations on Y and X. Eyi = 1110 Ex; = 1680 XiYi = 204,200 EX = 315400 = 133,301:1 Assuming all the assumptions of CLR.M are fulfilled: obtain
a. ’51 and b2.
b. standard errors of these estimators.
C. r2.
d. Establish 95% confidence intervals for 81 and 82.
e. On the basis of the confidence intervals established in (d): can you accept the hypothesis that 82 = 0? 3.9.
3.10
Based on data for the United States for the period 1965 to 2006 (found in Table 3-4 on the textbook’s Web site): the following regression results were obtained:
GI‘I1 1 —995,5183 8150P3M1t = ) (0.3214) (-3.8255) ( ) where GNP is the gross national product (, in billions) and .1.411 is the money supply (: in billions). Note: A.41 includes currency, demand deposits: traveler’s checks: and other checkable deposits.
a. Fill in the blank parentheses.
T2 0.188
b. The monetarists maintain that money supply has a significant positive impact on GNP. How would you test this hypothesis?
c. What is the meaning of the negative intercept?
d. Suppose JVP1 for 2007 is $750 billion. What is the mean forecast value of GNP for that year? 3.10.
3.18
Refer to Example 2.1 on years of schooling and average hourly earnings. The data for this example are given in Table 2-5 and the regression results are presented in Eq. (2.21). For this regression a. Obtain the standard errors of the intercept and slope coefficients and .r.2.
b. Test the hypothesis that schooling has no effect on average hourly earnings. Which test did you use and why?
c. If you reject the null hypothesis in (b): would you also reject the hypothesis that the slope coefficient in Eq. (121) is not different from 1? Show the necessary calculations.
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