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Short Guides to Microeconometrics
Fall 2016
Kurt Schmidheiny
Unversität Basel
Elements of Probability Theory
1
2
Random Variables and Distributions
A random variable is a variable whose values are determined by a prob-
Elements of Probability Theory
ability distribution. This is a casual way of defining random variables
which is sufficient for our level of analysis. For more advanced probability
theory, a random variable will be defined as a real-valued function over
some probability space.
Contents
In section 1 to 3, a random variable is denoted by capital letters, e.g.
1 Random Variables and Distributions
3
1.1
Univariate Random Variables and Distributions . . . . . .
3
1.2
Bivariate Random Variables and Distributions
5
. . . . . .
X, whereas its realizations are denoted by small letters, e.g. x.
1.1
Univariate Random Variables and Distributions
A univariate discrete random variable is a variable that takes a countable
2 Moments
7
number K of real numbers with certain probabilities. The probability
2.1
Expected Value or Mean . . . . . . . . . . . . . . . . . . .
7
that the random variable X takes the value xk among the K possible
2.2
2.3
Variance and Standard Deviation . . . . . . . . . . . . . .
Higher order Moments . . . . . . . . . . . . . . . . . . . .
7
9
realizations is given by the probability distribution
2.4
Covariance and Correlation . . . . . . . . . . . . . . . . .
9
2.5
Conditional Expectation and Variance . . . . . . . . . . .
9
3 Random Vectors and Random Matrices
11
4 Important Distributions
11
4.1
Univariate Normal Distribution . . . . . . . . . . . . . . .
11
4.2
Bivariate Normal Distribution . . . . . . . . . . . . . . . .
11
4.3
Multivariate Normal Distribution . . . . . . . . . . . . . .
13
P (X = xk ) = P (xk ) = pk
with k = 1, 2, ..., K. K may be ∞ in some cases. This can also be written
as

p1




 p2
P (xk ) =
..


.



pK
Note that
K
X
if X = x1
if X = x2
if X = xK
pk = 1.
k=1
A univariate continuous random variable is a variable that takes a
continuum of values in the real line. The distribution of a continuous random variable X can be characterized by a density function or probability
Version: 22-9-2016, 09:06
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Short Guides to Microeconometrics
density function (pdf ) f (x). The nonnegative function f (x) is such that
Z x2
P (x1 ≤ X ≤ x2 ) =
f (x)dx.
Elements of Probability Theory
4
or joint probability density function, f (x, y). The nonnegative function
f (x, y) is such that
Z
x1
x2
Z
y2
P (x1 ≤ X ≤ x2 , y1 ≤ Y ≤ y2 ) =
f (x, y)dydx
x1
defines the probability that X takes a value in the interval [x1 , x2 ]. Note
y1
that there is no chance that X takes exactly the value x, P (X = x) = 0.
defines the probability that X and Y take values in the interval [x1 , x2 ]
The probability that X takes any value on the real line is
Z ∞
f (x)dx = 1.
and [y1 , y2 ], respectively. Note that
Z ∞Z ∞
f (x, y)dydx = 1.
−∞
−∞
The distribution of a univariate random variable X is alternatively
described by the cumulative distribution function (cdf )
−∞
The marginal density function or marginal probability density function
is given by
Z
F (x) = P (X < x).
f (x, y)dy
−∞
The cdf of a discrete random variable X is
X
X
F (x) =
P (X = xk ) =
pk .
xk ≤x
∞
f (x) =
xk ≤x
and of a continuous random variable X
Z x
F (x) =
f (t)dt
such that
Z
• F (x) is monotonically nondecreasing
f (x)dx.
x1
The conditional density function or conditional probability density function with respect to the event {Y = y} is given by
f (y|x) =
−∞
F (x) has the following properties:
x2
P (x1 ≤ X ≤ x2 ) = P (x1 ≤ X ≤ x2 , −∞ ≤ Y ≤ ∞) =
f (x, y)
f (x)
provided that f (x) > 0. Note that
Z ∞
f (y|x)dy = 1.
−∞
• F (−∞) = 0 and F (∞) = 1.
Two random variables X and Y are called independent, if and only if
• F (x) is continuous to the left
f (x, y) = f (x) · f (y)
1.2
Bivariate Random Variables and Distributions
A bivariate continuous random variable is a variable that takes a continuum of values in the plane. The distribution of a bivariate continuous
random variable (X, Y ) can be characterized by a joint density function
If X and Y are independent, then:
• f (y|x) = f (y)
• P (x1 ≤ X ≤ x2 , y1 ≤ Y ≤ y2 ) = P (x1 ≤ X ≤ x2 ) · P (y1 ≤ Y ≤ y2 )
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Short Guides to Microeconometrics
Elements of Probability Theory
More generally, if a finite set of n continuous random variables X1 , X2 , X3 , ..., Xn
are mutually independent, then
The following rules hold in general, i.e. for discrete, continuous and
mixed types of random variables:
f (x1 , x2 , x3 , ..., xn ) = f (x1 ) · f (x2 ) · f (x3 ) · ... · f (xn ).
2
2.1
6
• E[α] = α
• E[αX + βY ] = αE[X] + βE[Y ]
Moments
Pn
Pn
• E [ i=1 Xi ] = i=1 E[Xi ]
Expected Value or Mean
• E[X Y ] = E[X] E[Y ]
if X and Y are independent
The expected value or mean of a discrete random variable with probability
distribution P (xk ) and k = 1, 2, ..., K is defined as
E[X] =
K
X
xk P (xk )
k=1
where α ∈ R and β ∈ R are constants.
2.2
Variance and Standard Deviation
The variance of a univariate random variable X is defined as
if the series converges absolutely.
The expected value or mean of a continuous univariate random variable
with density function f (x) is defined as
Z ∞
xf (x)dx
E[X] =
−∞
if the integral exists.
V[X] = E (X − E[X])2 = E[X 2 ] − (E[X])2
The variance has the following properties:
• V[X] ≥ 0
• V[X] = 0
if and only if X = E[X]
For a random variable Z which is a continuous function φ of a discrete
The following rules hold in general, i.e. for discrete, continuous and mixed
random variable X, we have:
E[Z] = E[φ(X)] =
K
X
types of random variables:
φ(xk )P (xk )
k=1
• V[αX + β] = α2 V[X]
• V[X + Y ] = V[X] + V[Y ] + 2Cov[X, Y ]
For a random variable Z which is a continuous function φ of the continuous random variables X and Y , we have:
Z ∞
E[Z] = E[φ(X)] =
φ(x)f (x)dx
−∞
Z ∞Z ∞
E[Z] = E[φ(X, Y )] =
φ(x, y)f (x, y)dx dy
−∞
−∞
• V[X − Y ] = V[X] + V[Y ] − 2Cov[X, Y ]
Pn
Pn
• V i=1 Xi = i=1 V[Xi ] if Xi and Xj independent for all i 6= j
where α ∈ R and β ∈ R are constants.
Instead of the variance, one often considers the standard deviation
√
σX = VX.
7
2.3
Short Guides to Microeconometrics
Higher order Moments
Elements of Probability Theory
8
We say that
The j-th moment around zero is defined as
E (X − E[X])j .
• X and Y are uncorrelated if ρ = 0
2.4
• X and Y are negatively correlated if ρ < 0
• X and Y are positively correlated if ρ > 0
Covariance and Correlation
The Covariance between two random variables X and Y is defined as:
Cov[X, Y ] = E (X − E[X])(Y − E[Y ])
= E[XY ] − E[X]E[Y ]
= E (X − E[X])Y = E X(Y − E[Y ])
2.5
Conditional Expectation and Variance
Let (X, Y ) be a bivariate discrete random variable and P (yk |X) the conditional probability of Y = yk given X. Then the conditional expected
value or conditional mean of Y given X is
The following rules hold in general, i.e. for discrete, continuous and
mixed types of random variables:
E[Y |X] = EY |X [Y ] =
K
X
yk P (yk |X).
k=1
• Cov[αX + γ, βY + µ] = αβCov[X, Y ]
Let (X, Y ) be a bivariate continuous random variable and f (y|x) the
• Cov[X1 + X2 , Y1 + Y2 ]
= Cov[X1 , Y1 ] + Cov[X1 , Y2 ] + Cov[X2 , Y1 ] + Cov[X2 , Y2 ]
• Cov[X, Y ] = 0
conditional mean of Y given X is
if X and Y are independent
where α ∈ R, β ∈ R, γ ∈ R and µ ∈ R are constants.
The correlation coefficient between two random variables X and Y is
defined as:
conditional density of Y given X. Then the conditional expected value or
Cov[X, Y ]
σX σY
denote the corresponding standard deviations. The
Z
∞
E[Y |X] = EY |X [Y ] =
yf (y|X)dy.
−∞
The law of iterated means or law of iterated expecations holds in general, i.e. for discrete, continuous or mixed random variables:
ρX,Y =
where σX and σY
correlation coefficient has the following property:
• −1 ≤ ρX,Y ≤ 1
The following rule holds:
• ραX+γ,βY +µ = ρX,Y
• ρX,Y = 0
if X and Y are independent
where α ∈ R and β ∈ R are constants.
EX E[Y |X] = E[Y ].
The conditional variance of Y given X is given by
2
V[Y |X] = E (Y − E[Y |X])2 X = E Y 2 X − (E[Y |X]) .
The law of total variance is
V[Y ] = EX V [Y |X] + VX E[Y |X] .
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Short Guides to Microeconometrics
Random Vectors and Random Matrices
Elements of Probability Theory
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The following rules hold:
In this section we denote matrices (random or non-random) by bold cap-
• V[a0 x] = a0 V[x] a
ital letters, e.g. X and vectors by small letters, e.g. x.
• V[Ax] = A V[x] A0
Let x = (x1 , . . . , xn )0 be a (n × 1)-dimensional vector such that each
element xi is a random variable. Let X be a (n × k)-dimensional matrix
where the (m × n) dimensional matrix A with m ≤ n has full row rank.
such that each element xij is a random variable. Let a = (a1 , . . . , an )0
If the variance-Covariance matrix V[x] is positive definite (p.d.) then
be a n × 1-dimensional vector of constants and A a (m × n) matrix of
all random elements and all linear combinations of its random elements
constants.
have strictly positive variance:
The expectation of a random vector, E[x), and of a random matrix,
E[X), summarize the expected values of its elements, respectively:




E[x11 ] E[x12 ] . . . E[x1k ]
E[x1 ]




 E[x21 ] E[x22 ] . . . E[x2k ] 
 E[x2 ] 



E[x] =  .  and E[X] =  .
..
.. 
..
.
.
 ..
 .. 
.
. 
E[xn ]
E[xn1 ]
E[xn2 ] . . .
E[xnk ]
V [a0 x] = a0 V [x] a > 0 for all a 6= 0.
4
Important Distributions
4.1
Univariate Normal Distribution
The density of the univariate normal distribution is given by:
The following rules hold:
f (x) =
• E[a0 x] = a0 E[x]
1 x−µ 2
1
√ e− 2 ( σ ) .
σ 2π
The normal distribution is characterized by the two parameters µ and
• E[Ax] = AE[x]
σ. The mean of the normal distribution is E[X] = µ and the variance
V[X] = σ 2 . We write X ∼ N(µ, σ 2 ).
• E[AX] = AE[X]
• E[tr(X)] = tr(E[X]) for X a quadratic matrix
The univariate normal distribution with mean µ = 0 and variance
2
σ = 1 is called the standard normal distribution N(0, 1).
The variance-Covariance matrix of a random vector, V(x), summarizes
all variances and Covariances of its elements:
0
0
V[x] = E (x − E[x]) (x − E[x]) = E[xx0 ] − (E[x])([E[x])


V[x1 ]
Cov[x1 , x2 ] . . . Cov[x1 , xn ]


V[x2 ]
. . . Cov[x2 , xn ]
 Cov[x2 , x1 ]

.
=
..
..
..
..

.


.
.
.
Cov[xn , x1 ]
Cov[xn , x2 ] . . .
V[xn ]
4.2
Bivariate Normal Distribution
The density of the bivariate normal distribution is
1
p
2πσX σY 1 − ρ2
(
"
2 2
#)
1
x − µX
y − µY
x − µX
y − µY
exp −
+
− 2ρ
.
2(1 − ρ2 )
σX
σY
σX
σY
f (x, y) =
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Short Guides to Microeconometrics
Elements of Probability Theory
If (X, Y ) follows a bivariate normal distribution, then:
12
A n-dimensional random variable x is multivariate normally distributed
• The marginal densities f (x) and f (y) are univariate normal.
with mean µ and variance-Covariance matrix Σ, x ∼ N(µ, Σ) if its density
is:
• The conditional densities f (x|y) and f (y|x) are univariate normal.
−n/2
f (x) = (2π)
−1/2
(det Σ)
2
, E[Y ] = µY , V[Y ] = σY2 .
• E[X] = µX , V[X] = σX
• The correlation coefficient between X and Y is ρX,Y = ρ.
• E[Y |X] = µY +
Y
ρ σσX
(X
− µX ) and V[Y |X] =
σY2
1
0 −1
exp − (x − µ) Σ (x − µ) .
2
Let x ∼ N(µ, Σ) and A a (m × n) matrix with m ≤ n and m linearly
independent rows then we have
2
(1 − ρ ).
Ax ∼ N(Aµ, AΣA0 ).
The above properties characterize the normal distribution. It is the only
distribution with all these properties.
References
Further important properties:
Amemiya, Takeshi (1994), Introduction to Statistics and Econometrics,
• If (X, Y ) follows a bivariate normal distribution, then aX + bY is
also normally distributed:
Hayashi, Fumio (2000), Econometrics, Princeton: Princeton University
2
aX + bY ∼ N(µX + µY , σX
+ σY2 + 2ρσX σY ).
• If X and Y are bivariate normally distributed with Cov[X, Y ] = 0,
then X and Y are independent.
Multivariate Normal Distribution
Let x = (x1 , . . . , xn )0 be a n-dimensional vector such that each element
xi is a random variable. In addition let E[x] = µ = (µ1 , . . . , µn ) and
V[x] = Σ with


σ11
σ12
...
σ1n

 σ21
Σ=
 ..
 .
σ22
..
.
...
..
.

σ2n 
.. 

. 
σn1
σn2
...
σnn
where σij = Cov[xi , xj ].
Press. Appendix A.
Stock, James H. and Mark W. Watson (2012), Introduction to Econometrics, 3rd ed., Pearson Addison-Wesley. Chapter 2.
The reverse implication is not true.
4.3
Cambridge: Harvard University Press.