1. An article was published in which the authors attempted to see if the pharmaceutical industry practiced international price discrimination by estimating a model of the prices of pharmaceuticals in a cross section of 32 countries. The authors felt that if price discrimination existed, then the coefficient of per capita income in a properly specified price equation would be strongly positive. The reason they felt that the coefficient of per capita income would measure price discrimination went as follows: the higher the ability to pay, the lower (in absolute value) the price elasticity of demand for pharmaceuticals and the higher the price a price discriminator could charge. In addition, the authors expected that prices would be higher if pharmaceutical patents were allowed and that prices would be lower if price controls existed, if competition was encouraged, or if the pharmaceutical market in a country was relatively large. That study was cross sectional and included countries as large as the United States and as small as Luxembourg, so you'd certainly expect heteroskedasticity to be a potential problem. Luckily, the dependent variable in the original research was P_i, the pharmaceutical price level in the ith country divided by that of the United States, so the researchers didn't encounter the wide variations in size typically associated with heteroskedasticity. Suppose, however, that we use the same data set to build a model of pharmaceutical consumption:

estimated CV_i = 15.9 + 0.18 N_i + 0.22 P_i + 14.3 PC_i

(0.05) (0.09) (6.4)

t = 3 .322 .532 .24 N = 32 adjusted R² = .31

where:

CV_i=the volume of consumption of pharmaceuticals in the ith country divided by that of the United States

N_i=the population of the ith country divided by that of the United States

IPC_i= a dummy variable equal to 1 if the ith country encouraged price competition, 0 otherwise

a. Use the data in the file titledDRUGS5 to test for heteroskedasticity in the Equation above with both the Breusch-Pagan and the White test at the 5-percent level.

b. If your answer to part a is heteroskedasticity, estimate HC standard errors for the Equation above.

c. Similarly, if you encountered heteroskedasticity, re-estimate the Equation above using a double-log functional form.

Question Choice:

1-the Breusch-Pagan test gives a chi-squared of __________ at the 5-percent level.

Group of answer choices

A-10.91

B-7.81

C-15.51

D-26.82

E-none of the above

2-the Breusch-Pagan test reveals that .....

Group of answer choices

A-we can reject the null hypothesis of heteroskedasticity.

B-we can reject the null hypothesis of homoskedasticity.

C-we cannot reject the null hypothesis of heteroskedasticity.

D-we cannot reject the null hypothesis of homoskedasticity.

E-none of the above

3-the White test gives a chi-squared of __________ at the 5-percent level.

Group of answer choices

A-10.91

B-7.81

C-15.51

D-28.62

E-none of the above

4-the White test reveals that .....

Group of answer choices

A-we can reject the null hypothesis of heteroskedasticity.

B-we can reject the null hypothesis of homoskedasticity.

C-we cannot reject the null hypothesis of heteroskedasticity.

D-we cannot reject the null hypothesis of homoskedasticity.

E-none of the above

5-the resulting HC standard error for N is...

Group of answer choices

A-no heteroskedasticity was found so the double-log functional form was not estimated

B-0.107

C-0.127

D-0.154

6-the standard error for log n is...

Group of answer choices

A-larger than the robust standard error for n

B-smaller than the robust standard error for n

C-equal to the robust standard error for n

D-equal to the standard error of n

**DATA: DRUGS5**

OBS	CV	CVN	DPC	GDPN	IPC	N	P	PP
1	0.014	0.6	0	4.9	0	2.36	60.83	1
2	0.07	1.1	0	6.56	0	6.27	50.63	1
3	18.66	6.6	1	6.56	0	282.76	31.71	0
4	3.42	10.4	1	8.23	1	32.9	38.76	0
5	0.42	6.7	1	9.3	1	6.32	15.22	1
6	0.05	2.2	0	10.3	0	2.33	96.58	1
7	2.21	11.3	0	13	0	19.6	48.01	0
8	0.77	3.9	0	13.2	0	19.7	51.14	1
9	2.2	13.3	0	20.7	0	16.52	35.1	0
10	0.5	8.9	0	21.5	0	5.58	70.74	1
11	1.56	14.1	0	22.4	1	11.09	48.07	0
12	0.21	22	0	24	0	0.96	46.13	1
13	10.48	21.6	0	25.2	1	50.17	63.83	0
14	7.77	27.6	0	34.7	0	28.16	69.68	0
15	3.83	40.6	1	36.1	1	9.42	48.24	0
16	3.27	21.3	0	37.7	0	15.33	70.42	0
17	0.44	33.8	0	39.6	0	1.3	65.95	0
18	0.57	38	0	42.5	0	1.49	73.58	1
19	2.36	47.8	1	49.6	1	4.94	57.25	0
20	8.08	50.7	1	50.1	1	15.93	53.98	0
21	12.02	45.9	1	53.8	0	26.14	69.01	0
22	9.01	54.2	0	55.9	0	16.63	69.68	0
23	9.96	38	1	63.9	1	26.21	71.19	1
24	28.58	54.7	1	68.4	0	52.24	81.88	0
25	1.24	35.2	0	69.6	0	3.52	139.53	0
26	1.54	24.1	0	75.2	0	6.4	137.29	1
27	3.49	76	1	77.7	0	4.59	101.73	1
28	25.14	101.8	1	81.9	0	24.7	91.56	1
29	0.1	60.5	1	82	0	0.17	100.27	1
30	0.7	29.5	0	82.4	0	2.35	157.56	1
31	24.29	83.9	0	83	0	28.95	152.52	1
32	100	100	0	100	1	100	100	1