In: Advanced Math
The demand for roses was estimated using quarterly figures for the period 1971 (3rd quarter) to 1975 (2nd quarter). Two models were estimated and the following results were obtained:
Y = Quantity of roses sold (dozens)
X2 = Average wholesale price of roses ($ per dozen)
X3 = Average wholesale price of carnations ($ per dozen)
X4 = Average weekly family disposable income ($ per week)
X5 = Time (1971.3 = 1 and 1975.2 = 16)
ln = natural logarithm
The standard errors are given in parentheses.
(0.327) (0.659) (1.201) (0.128)
R2 = 77.8% D.W. = 1.78 N = 16
B. ln YtÙ = 10.462 - 1.39 ln X2t
(0.307)
R2 = 59.5% D.W. = 1.495 N = 16
Correlation matrix:
ln X2 |
ln X3 |
ln X4 |
ln X5 |
|
ln X2 |
1.0000 |
-.7219 |
.3160 |
-.7792 |
ln X3 |
-.7219 |
1.0000 |
-.1716 |
.5521 |
ln X4 |
.3160 |
-.1716 |
1.0000 |
-.6765 |
ln X5 |
-.7792 |
.5521 |
-.6765 |
1.0000 |
a) How would you interpret the coefficients of ln X2, ln X3 and ln X4 in model A?
What sign would you expect these coefficients to have? Do the results concur with your expectation?
b) Are these coefficients statistically significant?
c) Use the results of Model A to test the following hypotheses:
i) The demand for roses is price elastic
ii) Carnations are substitute goods for roses
iii) Roses are a luxury good (demand increases more than proportionally as income rises)
d) Are the results of (b) and (c) in accordance with your expectations? If any of the tests are statistically insignificant, give a suggestion as to what may be the reason.
e) Do you detect the presence of multicollinearity in the data? Explain.
f) Do you detect the presence of serial correlation? Explain
g) Do the variables X3, X4 and X5 contribute significantly to the analysis? Test the joint significance of these variables.
h) Starting from model B, assuming that at the time point of January, 1973, there was a disaster that heavily affected the quantity of roses produced. Suggest a model to check if we have to use two different models for the data before and after the disaster. (Using dummy variable).