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PRINCIPLES OF ECONOMETRICS

5TH EDITION

ANSWERS TO ODD-NUMBERED EXERCISES IN CHAPTER 4

• Chapter 4, Exercise Answers, Principles of Econometrics, 5e 2

EXERCISE 4.1

(a) 2 0.843R =

(b) 2ˆ 8.4249σ =

(c) 2 0.711R =

EXERCISE 4.3

(a) 0ˆ 4.4y = (b) se( ) 1.5875f =

(c) ( 0.6520, 9.4520)− (d) ( 4.8722, 13.6722)−

(e) ( 1.8189, 5.8189 )−

EXERCISE 4.5

(a) 2 0.204R =

(b) ˆ 0.4517yy yxr r= = .

(c) 5 16 210.01131, 0.001351, 0.0063175h h h= = =

(d) 2DFBETAS 0.05237256i = − .

(e) DFFITS 0.06474186i = −

(f) ˆ 0.71691235stuie = −

EXERCISE 4.7

(a) 2 0. 113R =

(b)  60.12ENTERT = (c) [ ]52.95, 173.19− or [ ]0, 173.19 (d) [ ]158.85, 519.57− or [ ]0, 519.57

• Chapter 4, Exercise Answers, Principles of Econometrics, 5e 3

EXERCISE 4.9

(a) The Jarque-Bera = 30.405483. The test statistic value is larger than the critical value and we reject the null hypothesis.

(b) In this case JB = 1.9153333. Thus we fail to reject the null.

(c) In this case JB = 0.88941667. Thus, we fail to reject the null hypothesis.

(d) The log-log model fits the data the best.

(e) (i) The magnitude of the correlation between y and x is the same as the correlation between y and a + bx.

(ii) The 2R = 0.31315216.

(iii) YHAT and RPRICE3 have an exact linear relationship.

(iv) 0.43046721

(v) 0.45616516

(f) The log-log model fits the data best.

EXERCISE 4.11

(a) [5.7059548, 42.134045].

(b) [0.61731625, 56.582684].

EXERCISE 4.13

(a) 2 i i

i

x y x

α =     

. Substituting we have ( )( )

( ) 22 i i

i

x x y y b

x x − −

α = = −

 

(b) ( ) ( ) ( )2 2 2 ˆi i i i i i i ie y x y y b x x y y b x b x e= − α = − − − = − − − =  

• Chapter 4, Exercise Answers, Principles of Econometrics, 5e 4

EXERCISE 4.15

(a) For all values of x the dependent variable will be positive. An x = 0 will create an undefined value.

(b) ( ) ( )21 2 2exp 1dy dx x x−= β + β  × −β  Assuming that 0x > the slope will be positive if 2 0β < .

(c) When 2 0β < , as x approaches zero from above, we see that ( )1 2 1 xβ − β → −∞ and ( )1 2exp 1 0y x= β − β  →  . If x approaches infinity ( )1 2 11 xβ − β → β and ( ) ( )1 2 1exp 1 expy x= β − β  → β  . In Figure XR 4-15(c) part 1 we chose 1 1β = , so

2.7182818y e→ ≅ .

See Figure XR 4-15(c) part 2. If 2 0β > , as x approaches zero from above, we see that

( )1 2 1 xβ + β → ∞ and ( )1 2exp 1y x= β + β  → ∞  . If x approaches infinity ( )1 2 11 xβ + β → β and ( ) ( )1 2 1exp 1 expy x= β + β  → β  . In Figure XR 4-15(c) part 2 we

chose 1 1β = , so 2.7182818y e→ ≅ .

(d) See Figure XR 4-15(d).

The slopes at the x-values 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0 are

• Chapter 4, Exercise Answers, Principles of Econometrics, 5e 5

x dy/dx

.5 .0053674

1 .07326256

1.5 .12352614

2 .13533528

2.5 .12921377

3 .11715428

3.5 .10413275

4 .09196986

We see that the slope increases but then begins to decrease. So, the answer is both.

(e) The first derivative is ( ) ( )21 2 2exp 1dy dx x x−= β + β  × −β  . The second derivative, uses Derivative Rule 6. To solve for the point where the second derivative equals zero we set the second term to zero and solve for x:

2 2

x −β=

EXERCISE 4.17

(a) A possible match is the linear-log model, shown in Figure 4.5(f).

(b)  2_ 278.834 50.729ln( _ ) 0.9276 (se) (20.4216) (3.4378) RATE UK SPEND UK R= − + =

In this case the log-linear model fits the data well, except at the very low and very high ends.

(c) If SPEND_UK = 1, then ln(SPEND_UK) = 0, then ( ) 1_ | _ 1E RATE UK SPEND UK = = β

10 15

20 25

30 35

300 350 400 450 500 per capita consumers' expenditures (1970 pounds)

total ownership rate, per 100 Fitted values

XR 4.17(b) UK linear-log

• Chapter 4, Exercise Answers, Principles of Econometrics, 5e 6

(d)  2_ 33.0221 +12.296 ln( _ 280) 0.9840 (se) (1.7274) (0.3805) RATE UK SPEND UK R= − − =

The adjustment improves the fit in the lower and upper ends.

(e)

( ) ( ) 2ln _ 5.72032 1002.266 1 _ 0.8642 (se) (0 .2574) ( 96.3413)

RATE UK SPEND UK R= − =

For these data the log-reciprocal model is almost a straight line, missing the curvature of the data.

10 15

20 25

30

300 350 400 450 500 per capita consumers' expenditures (1970 pounds)

total ownership rate, per 100 Fitted values

XR 4.17(d) UK linear-log 2

10 20

30 40

300 350 400 450 500 per capita consumers' expenditures (1970 pounds)

total ownership rate, per 100 ratelr_uk

XR 4.17(e) UK log-reciprocal

• Chapter 4, Exercise Answers, Principles of Econometrics, 5e 7

(f) The problem is that in the reciprocal the value of the explanatory variable x, here _SPEND UK , becomes large, which makes 1 / _SPEND UK very small.

(g) ( )  [ ]( ) 2ln _ 3.6684 48.3067 1 _ 280 0.9818 (se) (0.0229) (1.5957)

RATE UK SPEND UK R= − − =

The solid fitted line if from the modified model. For comparison we plot the dashed fitted values from part (e).

(h)

(i) The data for Ireland is shown below.

(ii) The linear log model for Ireland is  2_ 181.762 33.768ln( _ ) 0.9326 (se) (16.412) (2.871) RATE IR SPEND IR R= − + =

10 20

30 40

300 350 400 450 500 per capita consumers' expenditures (1970 pounds)

total ownership rate, per 100 ratelr_uk2 ratelr_uk

XR 4.17(g) UK log-reciprocal 2

0 5

10 15

20 to

ta l o

w ne

rs hi

p ra

te , p

er 1

00

250 300 350 400 per capita consumers' expenditures (1968 pounds)

XR 4.17(h)-i Ireland data line plot

• Chapter 4, Exercise Answers, Principles of Econometrics, 5e 8

(iii) The modified linear-log model is  2_ 14.4554 +6.4279 ln( _ 240) 0.9845 (se) ( 1.0317) (0.2548) RATE IR SPEND IR R= − − =

(iv) The log-reciprocal model for Ireland is

( ) ( ) 2ln _ 5.9641 1096.784 1 _ 0.8190 (se) (0.5448 ) ( 163.0473)

RATE IR SPEND IR R= − =

(v) The modified log reciprocal model for Ireland is

0 5

10 15

20

250 300 350 400 per capita consumers' expenditures (1968 pounds)

total ownership rate, per 100 Fitted values

XR 4.17(h)-ii Ireland linear-log

0 5

10 15

20

250 300 350 400 per capita consumers' expenditures (1968 pounds)

total ownership rate, per 100 Fitted values

XR 4.17(h)-iii Ireland linear-log 2

5 10

15 20

250 300 350 400 per capita consumers' expenditures (1968 pounds)

total ownership rate, per 100 ratelr_ir

XR 4.17(h)-iv Ireland log-reciprocal

• Chapter 4, Exercise Answers, Principles of Econometrics, 5e 9

( ) [ ]( ) 2ln _ 2.9895 29.0909 1 _ 240 0.9824 (se) (0.0345) (1.2325)

RATE IR SPEND IR R= − − =

EXERCISE 4.19

(a) RYIELD can be interpreted as the number of hectares needed to produce one tonne of wheat.

(b) In the figure RMULL is the RYIELD series for Mullewa and RNORTH is the RYIELD series for Northampton. In both shires amount of land required to produce show a spike in 1963 and for Mullewa again in 1976-1977 and 1979.

There is an outlier in 1963 in the two shires, implying that a greater number of hectares was needed to produce one tonne of wheat than in any other year. There were similar but less extreme outliers in Mullewa in 1976, 1977 and 1979. Wheat production in Western Australia is highly dependent on rainfall, and so one would suspect that rainfall was low in the above years. A check of rainfall data at http://www.bom.gov.au/climate/data/ reveals that rainfall was lower than usual in 1976 and 1977, but higher than normal in 1963. Thus, it is difficult to assess why 1963 was a bad year; excess rainfall may have caused rust or other disease problems during the growing season, or rain at harvest time may have led to a deterioration in wheat quality.

0 20

250 300 350 400 per capita consumers' expenditures (1968 pounds)

total ownership rate, per 100 ratelr_ir2 ratelr_ir

XR 4.17(h)-v Ireland log-reciprocal 2

• Chapter 4, Exercise Answers, Principles of Econometrics, 5e 10