The McGraw-Hill Series Economics essentials of economics brue, McConnell, and Flynn Essentials of Economics

TABLE 6.8
SAVRATE
INVRATE
Australia
0.250
0.270
Austria
0.285
0.282
Belgium
0.235
0.224
Canada
0.219
0.231
Denmark
0.202
0.224
Finland
0.288
0.305
France
0.254
0.260
Germany
0.271
0.264
Greece
0.219
0.248
Ireland
0.190
0.218
Italy
0.235
0.224
Japan
0.372
0.368
Luxembourg
0.313
0.277
Netherlands
0.273
0.266
New Zealand
0.232
0.249
Norway
0.278
0.299
Spain
0.235
0.241
Sweden
0.241
0.242
Switzerland
0.297
0.297
U.K.
0.184
0.192
U.S.
0.186
0.186
Note:
SAVRATE
Savings as a ratio of GDP.
INVRATE
Investment expenditure as a ratio of GDP.
*The data are from Richard Blundell and Krishna Pendakur, “Semiparametric Estimation and
Consumer Demand,” 
Journal of Applied Econometrics,
vol. 13, no. 5, 1998, pp. 435–462. Data
reproduced from R. Carter Hill, William E. Griffiths, and George G. Judge, 
Undergraduate Econometrics,
2d ed., John Wiley & Sons, New York, 2001.
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Part One
Single-Equation Regression Models
The actual dataset can be found on this text’s website. The data include only house-
holds with one or two children living in Greater London. The sample does not in-
clude self-employed or retired households.
a.
Using the data on food expenditure in relation to total expenditure, determine which
of the models summarized in Table 6.6 fits the data.
b.
Based on the regression results obtained in (
a
), which model seems appropriate in the
present instance?
Note:
Save these data for further analysis in the next chapter on multiple regression.
6.17. Refer to Table 6.3. Find out the rate of growth of expenditure on durable goods. What is
the estimated 
semielasticity? 
Interpret your results. Would it make sense to run a double-
log regression with expenditure on durable goods as the regressand and time as the
regressor? How would you interpret the slope coefficient in this case?
6.18. From the data given in Table 6.3, find out the growth rate of expenditure on nondurable
goods and compare your results with those obtained from Exercise 6.17.
6.19. Table 6.10 gives data for the U.K. on total consumer expenditure (in £ millions) and
advertising expenditure (in £ millions) for 29 product categories.*
a.
Considering the various functional forms we have discussed in the chapter, which
functional form might fit the data given in Table 6.10?
b.
Estimate the parameters of the chosen regression model and interpret your results.
c.
If you take the ratio of advertising expenditure to total consumer expenditure, what do
you observe? Are there any product categories for which this ratio seems unusually
high? Is there anything special about these product categories that might explain the
relatively high expenditure on advertising?
6.20. Refer to Example 3.3 in Chapter 3 to complete the following:
a.
Plot cell phone demand against purchasing power (PP) adjusted per capita income.
b.
Plot the log of cell phone demand against the log of PP-adjusted per capita income.
c.
What is the difference between the two graphs?
d.
From these two graphs, do you think that a double-log model might provide a better fit
to the data than the linear model? Estimate the double-log model.
e.
How do you interpret the slope coefficient in the double-log model?
f.
Is the estimated slope coefficient in the double-log model statistically significant at the
5% level?
List of Variables:
wfood
=
budget share for food expenditure
wfuel
=
budget share for fuel expenditure
wcloth
=
budget share for clothing expenditure
walc
=
budget share for alcohol expenditure
wtrans
=
budget share for transportation expenditure
wother
=
budget share for other expenditures
totexp
=
total household expenditure
(rounded to the nearest 10 U.K. pounds sterling)
income
=
total net household income
(rounded to the nearest 10 U.K. pounds sterling)
age
=
age of household head
nk
=
number of children
The budget share of a commodity, say food, is defined as:
w
food
=
expenditure on food
total expenditure