PART ONE
Solutions to Exercises
Chapter 2
Review of Probability

Solutions to Exercises
1.
(a) Probability distribution function for Y
Outcome
(number of heads)
probability
Y
0
Y
0.25
1
Y
0.50
2
0.25
(b) Cumulative probability distribution function for Y
Outcome
(number of heads)
Probability
(c)
Y
Y
0
0
Y
0
1
0.25
1
Y
2
0.75
Y
2
1.0
= E(Y ) (0 0.25) (1 0.50) (2 0.25) 1.00
Using Key Concept 2.3: var(Y ) E(Y 2 ) [E(Y )]2 , and
E(Y 2 ) (02 0.25) (12 0.50) (22 0.25) 1.50
so that var(Y ) E(Y 2 ) [E(Y )]2 1.50 (1.00)2
2.
We know from Table 2.2 that Pr (Y
Pr ( X 1) 0 70. So
(a)
Y
0.50.
0) 0 22, Pr (Y
E(Y ) 0 Pr (Y
1) 0 78, Pr ( X
0) 0 30,
0) 1 Pr (Y 1)
0 0 22 1 0 78 0 78,
X
E( X ) 0 Pr ( X
0) 1 Pr ( X 1)
0 0 30 1 0 70 0 70
(b)
2
X
E[( X
X
)2 ]
(0 0.70)2 Pr ( X
0) (1 0.70)2 Pr ( X 1)
( 0 70)2 0 30 0 30 2 0 70 0 21
2
Y
E[(Y
Y
)2 ]
(0 0.78)2 Pr (Y
0) (1 0.78)2 Pr (Y 1)
( 0 78)2 0 22 0 222 0 78 0 1716
(c) Table 2.2 shows Pr ( X 0, Y
Pr ( X 1, Y 1) 0 63. So
0) 0 15, Pr ( X
0, Y 1) 0 15, Pr ( X 1, Y
0) 0 07,
4
Stock/Watson - Introduction to Econometrics - Brief Edition
cov (X , Y )
XY
E[( X
X
)(Y
(0 - 0.70)(0 - 0.78) Pr( X
Y
0, Y
)]
0)
(0 0 70)(1 0 78) Pr ( X
(1 0 70)(0 0 78) Pr ( X
0 Y 1)
1 Y 0)
(1 0 70)(1 0 78) Pr ( X
1Y
1)
( 0 70) ( 0 78) 0 15 ( 0 70) 0 22 0 15
0 30 ( 0 78) 0 07 0 30 0 22 0 63
0 084,
X
3.
0 084
XY
cor (X , Y )
For the two new random variables W
Y
0 21 0 1716
3 6 X and V
0 4425
20 7Y, we have:
(a)
E(V ) E(20 7Y ) 20 7E(Y ) 20 7 0 78 14 54,
E(W ) E(3 6 X) 3 6E( X) 3 6 0 70 7 2
(b)
2
W
var (3 6 X) 62
2
V
2
X
36 0 21 7 56,
2
2
Y
var (20 7Y ) ( 7)
49 0 1716 8 4084
(c)
cov (3 6 X , 20 7Y ) 6( 7) cov (X , Y )
WV
W
4.
3 528
WV
cor (W , V )
(a) E( X 3 ) 03 (1 p) 13
k
(b) E( X ) 0
k
7 56 8 4084
V
p
p
p
p
k
(1 p) 1
42 0 084
0 4425
(c) E ( X ) 0.3
var ( X ) E( X 2 ) [ E( X )]2
Thus,
0.3 0.09 0.21
0.21 0.46.
To compute the skewness, use the formula from exercise 2.21:
E( X
)3
E( X 3 ) 3[E( X 2 )][E( X)] 2[E( X)]3
0.3 3 0.32 2 0.33
Alternatively, E( X
Thus, skewness
E( X
0.084
)3 = [(1 0.3)3 0.3] [(0 0.3)3 0.7] 0.084
)3/
3
.084/0.463
0.87.
3 528
Solutions to Exercises in Chapter 2
To compute the kurtosis, use the formula from exercise 2.21:
)4
E( X
E( X 4 ) 4[E( X)][E( X 3 )] 6[E( X)]2 [E( X 2 )] 3[E( X)]4
0.3 4 0.32 6 0.33 3 0.34
Alternatively, E( X
0.0777
)4 = [(1 0.3)4 0.3] [(0 0.3)4 0.7] 0.0777
Thus, kurtosis is E( X
)4/
4
= .0777/0.464 1.76
5.
Let X denote temperature in F and Y denote temperature in C. Recall that Y 0 when X 32 and
17.78 (5/9) X. Using Key
Y 100 when X 212; this implies Y (100/180) ( X 32) or Y
Concept 2.3, X 70 F implies that Y
17.78 (5/9) 70 21.11 C, and X 7 F implies
(5/9) 7 3.89 C.
Y
6.
The table shows that Pr ( X 0, Y 0) 0 045, Pr ( X 0, Y 1) 0 709, Pr ( X 1, Y
Pr ( X 1, Y 1) 0 241, Pr ( X 0) 0 754, Pr ( X 1) 0 246, Pr (Y 0) 0 050,
Pr (Y 1) 0 950.
(a)
E(Y )
Y
0 Pr(Y
0) 1 Pr (Y 1)
0 0 050 1 0 950 0 950
(b)
Unemployment Rate
#(unemployed)
#(labor force)
Pr (Y
0) 0 050 1 0 950 1 E (Y )
(c) Calculate the conditional probabilities first:
0|X
0)
Pr ( X 0, Y 0)
Pr ( X 0)
0 045
0 754
0 0597,
Pr (Y 1|X
0)
Pr ( X 0, Y 1)
Pr ( X 0)
0 709
0 754
0 9403,
0|X 1)
Pr ( X 1, Y 0)
Pr ( X 1)
0 005
0 246
0 0203,
Pr (Y 1|X 1)
Pr ( X 1, Y 1)
Pr ( X 1)
0 241
0 246
0 9797
Pr (Y
Pr (Y
The conditional expectations are
E (Y |X 1)
E (Y |X
0 Pr (Y 0|X 1) 1 Pr (Y 1|X 1)
0 0 0203 1 0 9797 0 9797,
0) 0 Pr (Y 0|X 0) 1 Pr (Y 1|X
0 0 0597 1 0 9403 0 9403
(d) Use the solution to part (b),
0)
0) 0 005,
5
6
Stock/Watson - Introduction to Econometrics - Brief Edition
Unemployment rate for college grads
1 E(Y| X 1) 1 0.9797 0.0203.
Unemployment rate for non-college grads
1 E(Y| X
0) 1 0.9403 0.0597.
(e) The probability that a randomly selected worker who is reported being unemployed is a college
graduate is
Pr ( X 1|Y
0)
Pr ( X 1, Y 0)
Pr (Y 0)
0 005
0 050
01
The probability that this worker is a non-college graduate is
Pr ( X
0|Y
0) 1 Pr ( X 1|Y
0) 1 0 1 0 9
(f) Educational achievement and employment status are not independent because they do not satisfy
that, for all values of x and y,
Pr (Y
y|X
x) Pr (Y
y)
For example,
Pr (Y
7.
0|X
0) 0 0597 Pr (Y
Using obvious notation, C M F; thus
implies
(a) C 40 45 $85,000 per year.
(b) cor ( M, F )
Cov ( M , F )
C
, so that Cov (M, F)
M
F
0) 0 050
and
2
C
2
M
2
F
2 cov( M, F ). This
cor (M, F). Thus
Cov ( M, F) 12 18 0.80 172.80, where the units are squared thousands of dollars per year.
(c)
M F
2
C
2
M
2
F
C
813.60
2 cov( M, F ), so that
2
C
M
F
122 182
2 172.80 813.60, and
28.524 thousand dollars per year.
(d) First you need to look up the current Euro/dollar exchange rate in the Wall Street Journal, the
Federal Reserve web page, or other financial data outlet. Suppose that this exchange rate is e
(say e 0.80 euros per dollar); each 1$ is therefore with eE. The mean is therefore e C (in units
of thousands of euros per year), and the standard deviation is e C (in units of thousands of euros
per year). The correlation is unit-free, and is unchanged.
8.
Y
E(Y ) 1,
2
Y
var (Y ) 4. With Z
mZ
s
2
Z
E
var
1
2
1
(Y 1)
2
1
(Y 1)
2
(Y 1),
1
( mY
2
1
s
4
2
Y
1)
1
4
1
(1 1) 0,
2
4 1
Solutions to Exercises in Chapter 2
9.
Value of Y
Value of X
14
0.02
0.17
0.02
0.21
1
5
8
Probability distribution
of Y
22
0.05
0.15
0.03
0.23
30
0.10
0.05
0.15
0.30
40
0.03
0.02
0.10
0.15
Probability
Distribution of
65
X
0.01
0.21
0.01
0.40
0.09
0.39
0.11
1.00
(a) The probability distribution is given in the table above.
E(Y ) 14 0.21 22 0.23 30 0.30 40 0.15 65 0.11 30.15
E(Y 2 ) 142 0.21 222 0.23 30 2 0.30 402 0.15 652 0.11 1127.23
Var(Y) E(Y 2 ) [E(Y )]2
Y
218.21
14.77
(b) Conditional Probability of Y|X
14
0.02/0.39
22
0.03/0.39
8 is given in the table below
Value of Y
30
40
0.15/0.39 0.10/0.39
65
0.09/0.39
E(Y|X 8) 14 (0.02/0.39) 22 (0.03/0.39) 30 (0.15/0.39)
40 (0.10/0.39) 65 (0.09/0.39) 39.21
2
E(Y | X
8) 142 (0.02/0.39) 222 (0.03/0.39) 30 2 (0.15/0.39)
402 (0.10/0.39) 652 (0.09/0.39) 1778.7
Var(Y ) 1778.7 39.212
15.54
YX 8
(c) E( XY )
241.65
(1 14 0.02) (1 22 : 0.05)
(8 65 0.09) 171.7
Cov( X, Y ) E( XY ) E( X )E(Y ) 171.7 5.33 30.15 11.0
Corr( X, Y ) Cov( X, Y )/(
10. Using the fact that if Y
X
Y
N mY , s
) 11.0 /(5.46 14.77) 0.136
2
Y
then
Y
Y
Y
~ N (0,1) and Appendix Table 1, we have
(a)
Pr (Y
3)
Pr
Y 1
2
3 1
2
F (1) 0 8413
(b)
Pr(Y
0) 1 Pr(Y
1 Pr
Y
3
3
0)
0 3
3
1 F ( 1) F (1) 0 8413
7
8
Stock/Watson - Introduction to Econometrics - Brief Edition
(c)
Pr (40 Y
52)
40 50 Y 50 52 50
5
5
5
F (0 4) F ( 2) F (0 4) [1 F (2)]
0 6554 1 0 9772 0 6326
Pr
(d)
Pr (6 Y
8)
6 5
Pr
Y
5
8 5
2
2
2
F (2 1213) F (0 7071)
0 9831 0 7602
0 2229
11. (a)
(b)
(c)
(d)
(e)
0.90
0.05
0.05
When Y ~ 102 , then Y/10 ~ F10, .
Y Z 2 , where Z ~ N(0,1), thus Pr (Y 1) Pr ( 1 Z 1) 0.32.
12. (a)
(b)
(c)
(d)
(e)
(f)
0.05
0.950
0.953
The tdf distribution and N(0, 1) are approximately the same when df is large.
0.10
0.01
13. (a) E(Y 2 ) Var (Y )
2
Y
1 0 1; E(W 2 ) Var (W )
2
W
100 0 100.
(b) Y and W are symmetric around 0, thus skewness is equal to 0; because their mean is zero, this
means that the third moment is zero.
E (Y
(c) The kurtosis of the normal is 3, so 3
Y
$
Y
)4
; solving yields E(Y 4 ) 3; a similar calculation
yields the results for W.
(d) First, condition on X
E(S|X
2
0) 0; E(S |X
0, so that S W:
0) 100, E(S 3 |X
0) 0, E(S 4 |X
0) 3 100 2.
Similarly,
E(S|X 1) 0; E(S2 |X 1) 1, E(S3 |X 1) 0, E(S 4 |X 1) 3.
From the large of iterated expectations
E(S )
2
E ( S |X
2
E(S ) E(S |X
3
3
E(S ) E( S |X
E(S 4 ) E(S 4 |X
0) Pr (X 0) E( S |X
1) Pr( X
1) 0
2
0) Pr (X 0) E(S |X 1) Pr( X 1) 100 0.01 1 0.99 1.99
0) Pr (X 0) E( S 3 |X
1) Pr( X
1) 0
0) Pr (X 0) E(S 4 |X 1) Pr( X 1) 3 1002 0.01 3 1 0.99 302.97
Solutions to Exercises in Chapter 2
(e)
S
E(S ) 0, thus E ( S
2
S
Similarly,
E(S
S
S
)2
)3
E ( S 3 ) 0 from part d. Thus skewness
E ( S 2 ) 1.99, and E ( S
)4
S
0.
E ( S 4 ) 302.97. Thus,
kurtosis 302.97 /(1.992 ) 76.5
14. The central limit theorem suggests that when the sample size (n) is large, the distribution of the
sample average (Y ) is approximately N
2
Y
2
Y
(a) n 100,
Pr (Y
Pr (Y
(c) n 64,
101)
2
Y
2
Y
(b) n 165,
2
Y
2
Y
with
n
. Given
Y
100,
2
Y
43 0,
Y
Pr
100
101 100
0 43
0 43
F (1 525) 0 9364
0 2606, and
43
165
n
2
Y
,
0 43, and
43
100
n
Y
98) 1 Pr (Y
98) 1 Pr
Y
100
98 100
0 2606
0 2606
1 F ( 3 9178) F (3 9178) 1 000 (rounded to four decimal places)
2
Y
2
Y
0 6719, and
43
64
64
Pr (101 Y
103) Pr
101 100
Y 100
103 100
0 6719
0 6719
0 6719
F (3 6599) F (1 2200) 0 9999 0 8888 0 1111
15. (a)
Pr (9.6 Y
10.4)
9.6 10
Pr
Pr
Y
4/n
9.6 10
4/n
10
10.4 10
4/n
Z
4/n
10.4 10
4/n
where Z ~ N(0, 1). Thus,
9.6 10
(i) n
20; Pr
(ii) n
100; Pr
(iii) n
1000; Pr
4/n
9.6 10
4/n
9.6 10
4/n
10.4 10
Z
4/n
Z
Z
10.4 10
4/n
10.4 10
4/n
Pr ( 0.89 Z
Pr( 2.00 Z
Pr( 6.32 Z
0.89) 0.63
2.00) 0.954
6.32) 1.000
9
10
Stock/Watson - Introduction to Econometrics - Brief Edition
(b)
Pr (10 c Y
10 c)
Pr
Y
4/n
c
Pr
As n get large
c
4/n
10
c
4/n
4/n
c
Z
4/n
.
gets large, and the probability converges to 1.
c
4/ n
(c) This follows from (b) and the definition of convergence in probability given in Key Concept 2.6.
16. There are several ways to do this. Here is one way. Generate n draws of Y, Y1, Y2, … Yn. Let Xi 1 if
Yi 3.6, otherwise set Xi 0. Notice that Xi is a Bernoulli random variables with X Pr(X 1)
Pr(Y 3.6). Compute X . Because X converges in probability to X Pr(X 1) Pr(Y 3.6), X
will be an accurate approximation if n is large.
17.
Y
2
Y
0.4 and
0.4 0.6 0.24
(a) (i) P( Y
0.43)
Pr
(ii) P( Y
0.37)
Pr
(b) We know Pr( 1.96
0.39 0.4
0.24/n
18. Pr (Y
Y
0.4
0.43 0.4
0.24/n
Y
0.24/n
0.4
0.37 0.4
0.24/n
Z
Pr
Pr
0.24/n
1.96)
0.4
0.24/n
Y
0.6124
0.4
0.24/n
1.22
0.95, thus we want n to satisfy 0.41
1.96. Solving these inequalities yields n
$0) 0 95, Pr (Y
Y
0.27
0.11
0.41 0.4
0.24/n
9220.
$20000) 0 05.
(a) The mean of Y is
Y
0 Pr (Y
$0) 20,000 Pr (Y
$20000) $1000.
The variance of Y is
2
Y
E Y
2
Y
(0 1000)2 Pr Y
(20000 1000)2 Pr (Y
0
20000)
( 1000)2 0 95 19000 2 0 05 1 9 10 7,
1
so the standard deviation of Y is
(b) (i) E (Y )
mY
$1000,
2
Y
(1 9 107 ) 2
Y
2
Y
n
1 9 10 7
100
$4359
1 9 10 5.
(ii) Using the central limit theorem,
Pr (Y
2000) 1 Pr (Y
1 Pr
Y
2000)
1000
2 000 1 000
1 9 10
1 9 105
1 F (2 2942) 1 0 9891 0 0109
19. (a)
5
1.96 and
Solutions to Exercises in Chapter 2
Pr (Y
l
yj )
Pr ( X
xi , Y
11
yj )
i 1
l
Pr (Y y j|X xi )Pr ( X xi )
i 1
(b)
k
k
E (Y )
y j Pr (Y
yj
j 1
j 1
l
k
i 1
j 1
yj Pr (Y
l
l
yj )
Pr (Y
yj |X
xi ) Pr ( X
xi )
i 1
yj |X
xi ) Pr ( X xi )
E (Y |X xi )Pr ( X xi )
i 1
(c) When X and Y are independent,
Pr (X
xi , Y
yj ) Pr (X
xi )Pr (Y
yj )
so
s
E[( X
XY
l
k
mX )(Y
mY )]
( xi mX )( y j mY ) Pr ( X xi , Y y j )
i 1 j 1
l
k
( xi mX )( y j mY ) Pr ( X xi ) Pr (Y y j )
i 1 j 1
l
k
( xi
mX ) Pr ( X
xi )
i 1
E(X
mX )E (Y
mY ) 0 0
X
l
yi )
m
mY ) Pr (Y
yj
Pr (Y
yi |X
0,
0
XY
cor (X , Y )
20. (a) Pr (Y
( yj
j 1
xj , Z
Y
X
zh ) Pr (X
0
Y
xj , Z
zh )
j 1 h 1
(b)
E(Y )
k
yi Pr (Y
yi ) Pr (Y
yi )
i 1
k
i 1
l
l
yi
Pr (Y
yi | X
xj , Z
zh ) Pr (X
xj , Z
zh )
j 1 h 1
m
j 1 h 1
l
m
m
k
yi Pr (Y
yi | X
xj , Z
zh ) Pr (X
xj , Z
zh )
i 1
E(Y| X
xj , Z
zh ) Pr (X
xj , Z
zh )
j 1 h 1
where the first line in the definition of the mean, the second uses (a), the third is a rearrangement,
and the final line uses the definition of the conditional expectation.
12
Stock/Watson - Introduction to Econometrics - Brief Edition
21. (a)
)3
E( X
)2 ( X
E[( X
)] E[ X 3
3
2
E ( X ) 3E ( X )
2X 2
2
3E ( X )
2
X
3
X2
2X
3
2
3
]
2
E ( X ) 3E ( X ) E ( X ) 3 E ( X )[ E ( X )]2 [ E ( X )]3
E ( X 3 ) 3 E ( X 2 ) E ( X ) 2 E ( X )3
(b)
)4
E( X
E[( X 3 3 X 2
E[ X
4
3X
3
4
3X
2
2
2
3X
3
X
)( X
3
3
)]
X
3
3X 2
2
E( X ) 4 E( X )E( X ) 6 E( X )E( X )
2
2
3X
3
4 E( X )E( X )
4
3
]
E( X )4
E ( X 4 ) 4[ E ( X )][ E ( X 3 )] 6[ E ( X )]2 [ E ( X 2 )] 3[ E ( X )]4
22. The mean and variance of R are given by
w 0.08 (1 w) 0.05
2
w2 0.072
(1 w)2 0.042 2 w (1 w) [0.07 0.04 0.25]
where 0.07 0.04 0.25 Cov ( Rs , Rb ) follows from the definition of the correlation between
Rs and Rb.
(a)
0.065;
(b)
0.0725;
(c) w
1 maximizes
;
(d) The derivative of
2
0.044
0.056
0.07 for this value of w.
with respect to w is
2
d
dw
2w .072 2(1 w) 0.042 (2 4w) [0.07 0.04 0.25]
0.0102w 0.0018
solving for w yields w 18 /102 0.18. (Notice that the second derivative is positive, so that this
is the global minimum.) With w 0.18, R .038.
23. X and Z are two independently distributed standard normal random variables, so
X
Z
0,
2
X
2
Z
1,
0.
XZ
(a) Because of the independence between X and Z, Pr (Z
E(Z |X ) E(Z ) 0. Thus E(Y|X) E( X
(b) E( X 2 )
2
X
2
X
3
1, and
Y
3
E( X 2
2
z|X
x) Pr (Z
2
Z|X) E( X |X) E(Z |X)
Z ) E( X 2 )
Z
X
2
z), and
0
X2
1 0 1
(c) E( XY ) E( X ZX) E( X ) E(ZX). Using the fact that the odd moments of a standard normal
random variable are all zero, we have E( X 3 ) 0. Using the independence between X and Z, we
have E(ZX )
0. Thus E( XY ) E( X 3 ) E(ZX ) 0.
Z X
Solutions to Exercises in Chapter 2
13
(d)
Cov (XY )
E[( X
)(Y
X
E ( XY X )
0 0 0
X
24. (a) E(Yi 2 )
2
2
2
Y
0)(Y 1)]
0
X
Y
and the result follows directly.
n
(b) (Yi/ ) is distributed i.i.d. N(0,1), W
2
n
)] E[( X
E ( XY ) E ( X )
0
XY
cor (X , Y )
Y
i 1
(Yi / )2 , and the result follows from the definition of a
random variable.
(c) E(W)
E (W ) E
n
Yi 2
2
i 1
n
E
Yi 2
2
i 1
n.
(d) Write
V
Y1
n
2
i 2 Yi
n 1
Y1 /s
n
i 2 ( Y/ s
)2
n 1
which follows from dividing the numerator and denominator by . Y1/ ~ N(0,1),
2
n 1
, and Y1/ and
the t distribution.
n
i 2
n
i 2
(Yi / )2 ~
(Yi / )2 are independent. The result then follows from the definition of