Transcript Limits and the Law of Large Numbers

Limits and the Law of Large
Numbers
Lecture XIII
Almost Sure Convergence
 Let w represent the entire random sequence
{Zt}. As discussed last time, our interest
typically centers around the averages of this
sequence:
1 n
bn    t 1 Z t
n
 Definition 2.9: Let {bn(w)} be a sequence of
real-valued random variables. We say that
bn(w) converges almost surely to b, written
bn    b
a.s.
if and only if there exists a real number b
such that
P : bn    b  1
 The probability measure P describes the
distribution of w and determines the joint
distribution function for the entire sequence
{Zt}.
 Other common terminology is that bn(w)
converges to b with probability 1 (w.p.1) or
that bn(w) is strongly consistent for b.
 Example 2.10: Let
1 n
Z n  t 1 Z t
n
where {Zt} is a sequence of independently
and identically distributed (i.i.d.) random
variables with E(Zt)=m<. Then
a .s .
Z n 
m
by the Komolgorov strong law of large
numbers (Theorem 3.1).
 Proposition 2.11: Given g: RkRl (k,l<∞)
and any sequence {bn} such that
bn  b
a.s.
where bn and b are k x 1 vectors, if g is
continuous at b, then
g bn   g b 
a.s.
 Theorem 2.12: Suppose
– y=Xb0+e;
– X’e/n a.s. 0;
– X’X/a.s.M, finite and positive definite.
 Then bn exists a.s. for all n sufficiently
large, and bna.s.b0.
 Proof: Since X’X/n a.s.M, it follows from
Proposition 2.11 that det(X’X/n)
a.s.det(M). Because M is positive definite
by (iii), det(M)>0. It follows that
det(X’X/n)>0 a.s. for all n sufficiently large,
so (X’X/N)-1 exists a.s. for all n sufficiently
large. Hence

bˆn  X ' X
n
1
X'y
n
 In addition,

bˆn  b 0  X ' X n

1
X 'e
n
 It follows from Proposition 2.11 that
a. s .
1
ˆ
b 0  b 0  M  0  b 0
Convergence in Probability
 A weaker stochastic convergence concept is
that of convergence in probability.
 Definition 2.23: Let {bn(w)} be a sequence
of real-valued random variables. If there
exists a real number b such that for every e
> 0,
P : bn    b  e   1
as n , then bn(w) converges in
probability to b.
 The almost sure measure of probability
takes into account the joint distribution of
the entire sequence {Zt}, but with
convergence in probability, we only need to
be concerned with the joint distribution of
those elements that appear in bn(w).
 Convergence in probability is also referred
to as weak consistency.
 Theorem 2.24: Let { bn(w)} be a sequence
of random variables. If
bn  b, then bn 
 b
a.s.
p
If bn converges in probability to b, then
there exists a subsequence {bnj} such that
bn j  b
a.s .
th
r
Convergence in the
Mean
 Definition 2.37: Let {bn(w)} be a sequence
of real-valued random variables. If there
exists a real number b such that


E bn    b  0
r
as n   for some r > 0, then bn(w)
converges in the rth mean to b, written as
bn    b
r .m
 Proposition 2.38: (Jensen’s inequality) Let
g: R1R1 be a convex function on an
interval BR1 and let Z be a random
variable such that P[ZB]=1. Then
g(E(Z))  E(g(Z)). If g is concave on B,
then g(E(Z)) E(g(Z)).
 Proposition 2.41: (Generalized Chebyshev
Inequality) Let Z be a random variable such
that E|Z|r < , r > 0. Then for ever e > 0
P Z  e  
 
E Z
r
e
r
 Theorem 2.42: If bn(w)r.m. b for some r >
0, then bn(w)p b.
Laws of Large Numbers
 Proposition 3.0: Given restrictions on the
dependence, heterogeneity, and moments of
a sequence of random variables {Zt},
a.s.
Z n  m n 
0
where
1 n
Z n  t 1 Z t and m n  E Z n 
n
Independent and Identically
Distributed Observations
 Theorem 3.1: (Komolgorov) Let {Zt} be a
sequence of i.i.d. random variables. Then
Z n  m
a .s .
if and only if E|Zt| <  and E(Zt) = m.
 This result is consistent with Theorem 6.2.1
(Khinchine) Let {Xi} be independent and
identically distributed (i.i.d.) with E[Xi] = m.
Then
P
Xn 
 m
 Proposition 3.4: (Holder’s Inequality) If p >
1 and 1/p+1/q=1 and if E|Y|p <  and E|Z|q
< , then E|YZ|[E|Y|p]1/p[E|Z|q]1/q.
 If p=q=2, we have the Cauchy-Schwartz
inequality
  EZ 
E YZ   E Y
2
1
2
2
1
2
Asymptotic Normality
 Under the traditional assumptions of the
linear model (fixed regressors and normally
distributed error terms) bn is distributed
multivariate normal with:
 
E bˆn  b 0
 
1
2
ˆ
V bn   0 X ' X 
for any sample size n.
 However, when the sample size becomes
large the distribution of bn is approximately
normal under some general conditions.
 Definition 4.1: Let {bn} be a sequence of
random finite-dimensional vectors with
joint distribution functions {Fn}. If Fn(z) 
F(z) as n   for every continuity point z,
where F is the distribution function of a
random variable Z, then bn converges in
distribution to the random variable Z,
denoted
bn 
 Z
d
 Other ways of stating this concept are that
bn converges in law to Z:
bn 
 Z
L
Or, bn is asymptotically distributed as F
A
bn ~ F
In this case, F is called the limiting
distribution of bn.
 Example 4.3: Let {Zt} be a i.i.d. sequence
of random variables with mean m and
variance 2 < . Define
bn

Z

n  E Z n 
V Z 
1
n
2
1
 
n
1
2

n
t 1
Zt  m 
Then by the Lindeberg-Levy central limit
A
theorem (Theorem 6.2.2),
bn ~ N 0,1

 Theorem (6.2.2): (Lindeberg-Levy) Let
{Xi} be i.i.d. with E[Xi]=m and V(Xi)=2.
Then ZnN(0,1).
 Definition 4.8: Let Z be a k x 1 random
vector with distribution function F. The
characteristic function of Z is defined as
f l   Eexp il ' Z 
where i2=-1 and l is a k x 1 real vector.
 Example 4.10: Let Z~N(m,2). Then

f l   exp ilm  l 
2
2
2
 This proof follows from the derivation of
the moment generating function in Lecture
VII.
 Specifically, note the similarity between the
definition of the moment generating
function and the characteristic function:
M X t   E exp tx
f l   E exp ilz 
 Theorem 4.11 (Uniqueness Theorem) Two
distribution functions are identical if and
only if their characteristic functions are
identical.
 Note that we have a similar theorem for
moment generating functions.
 Proof of Lindeberg-Levy:
– First define f(l) as the characteristic function
for Zt-m and let fn(l) be the characteristic
function of
n Z n  m n 
1
 
n  n
1
2

n
t 1
Zt  m 

– By the structure of the characteristic function
we have
 l 
f n l   f 

 n 
n
  l 
ln  f n l   n ln  f 
 
   n 
– Taking a second order Taylor series expansion
of f(l) around l=0 gives
f l   1   l
2 2
Thus,

ln  f n l   n ln 1  l
2
2
 n  l 2 as n  
o l
2
2n
 
o l
2
2
 Thus, by the Uniqueness Theorem the
characteristic function of the sample
approaches the characteristic function of the
standard normal.