Transcript Introduction to Time Series Analysis

Time Series Analysis
Definition
A Time Series {xt : t  T} is a collection of random
variables usually parameterized by
1) the real line T = R= (-∞, ∞)
2) the non-negative real line T = R+ = [0, ∞)
3) the integers T = Z = {…,-2, -1, 0, 1, 2, …}
4) the non-negative integers T = Z+ = {0, 1, 2, …}
If xt is a vector, the collection of random vectors
{xt : t  T}
is a multivariate time series or multi-channel time
series.
If t is a vector, the collection of random variables
{xt : t  T} is a multidimensional “time” series or
spatial series.
(with T = Rk= k-dimensional Euclidean space or a kdimensional lattice.)
Example of spatial time series
The project
• Buoys are located in a grid across the Pacific
ocean
• Measuring
– Surface temperature
– Wind speed (two components)
– Other measurements
The data is being collected almost continuously
The purpose is to study El Nino
Technical Note:
The probability measure of a time series is defined by
specifying the joint distribution (in a consistent manner)
of all finite subsets of {xt : t  T}.
i.e. marginal distributions of subsets of random
variables computed from the joint density of a complete
set of variables should agree with the distribution
assigned to the subset of variables.
The time series is Normal if all finite subsets of
{xt : t  T} have a multivariate normal
distribution.
Similar statements are true for multi-channel
time series and multidimensional time series.
Definition:
m(t) = mean value function of {xt : t T} = E[xt]
for t  T.
s(t,s) = covariance function of {xt : t  T}
= E[(xt - m(t))(xs - m(s))] for t,s  T.
For multichannel time series
m(t) = mean vector function of {xt : t  T} = E[xt]
for t T and
S(t,s) = covariance matrix function of {xt : t  T}
= E[(xt - m(t))(xs - m(s))′] for t,s T.
The ith element of the k × 1 vector m(t)
mi(t) =E[xit]
is the mean value function of the time series {xit : t  T}
The i,jth element of the k × k matrix S(t,s)
sij(t,s) =E[(xit - mi(t))(xjs - mj(s))]
is called the cross-covariance function of the two time series
{xit : t  T} and {xjt : t  T}
Definition:
The time series {xt : t  T} is stationary if
the joint distribution of xt1, xt2, ... , xtk is the
same as the joint distribution of xt1+h ,xt2+h ,
... ,xtk+h for all finite subsets t1, t2, ... , tk of T
and all choices of h.
Definition:
The multi-channel time series {xt : t  T} is
stationary if the joint distribution of xt1, xt2,
... , xtk is the same as the joint distribution of
xt1+h , xt2+h , ... , xtk+h for all finite subsets t1,
t2, ... , tk of T and all choices of h.
Definition:
The multidimensional time series {xt : t  T}
is stationary if the joint distribution of xt1,
xt2, ... , xtk is the same as the joint distribution
of xt1+h ,xt2+h , ... ,xtk+h for all finite subsets
t1, t2, ... , tk of T and all choices of h.
The distribution of
observations at these
points in time
same as
The distribution of
observations at these
points in time
Time
Stationarity
Some Implication of Stationarity
If {xt : t  T} is stationary then:
1. The distribution of xt is the same for all t  T.
2. The joint distribution of xt, xt + h is the same
as the joint distribution of xs, xs + h .
Implication of Stationarity for the mean value
function and the covariance function
If {xt : t  T} is stationary then for t  T.
m(t) = E[xt] = m
and for t,s  T.
s(t,s) = E[(xt - m)(xs - m)]
= E[(xt+h - m)(xs+h - m)]
= E[(xt-s - m)(x0 - m)] with h = -s
= s(t-s)
If the multi-channel time series{xt : t  T} is
stationary then for t  T.
m(t) = E[xt] = m
and for t,s T
S(t,s) = S(t-s)
Thus for stationary time series the mean value
function is constant and the covariance function
is only a function of the distance in time (t – s)
If the multidimensional time series {xt : t T} is
stationary then for t  T.
m(t) = E[xt] = m
and for t,s  T.
s(t,s) = E[(xt - m)(xs - m)]
= s(t-s) (called the Covariogram)
Variogram
V(t,s) = V(t - s) = Var[(xt - xs)] = E[(xt - xs)2]
= Var[xt] + Var[xs] –2Cov[xt,xs]
= 2[s(0) - s(t-s)]
Definition:
r(t,s) = autocorrelation function of {xt : tT}
= correlation between xt and xs.
covxt , xs 
s t , s 


varxt  varxs 
s t , t  s s, s 
for t,s  T.
If {xt : t  T} is stationary then
r(h) = autocorrelation function of {xt : t  T}
= correlation between xt and xt+h.
covxt  h , xt 
s h
s h



varxt  h  varxt 
s o s o s o
Definition:
The time series {xt : t  T} is weakly
stationary if:
m(t) = E[xt] = m for all t  T.
and
s(t,s) = s(t-s) for all t,s  T.
or
r(t,s) = r(t-s) for all t,s  T.
Examples
Stationary time series
1. Let X denote a single random variable with mean m
and standard deviation s. In addition X may also be
Normal (this condition is not necessary)
Let xt = X for all t  T = { …,, -2, -1, 0, 1, 2, …}
Then E[xt] = m = E[X] for t  T and
s(h) = E[(xt+h - m)(xt - m)]
= Cov(xt+h,xt )
= E[(X - m)(X - m)] = Var(X)
= s2 for all h.
s h 
r h  
 1 for all h.
s o 
Excel file illustrating this time series
2. Suppose {xt : t  T} are identically distributed and
uncorrelated (independent).
T = { …,, -2, -1, 0, 1, 2, …}
Then E[xt] = m for t  T and
s(h) = E[(xt+h - m)(xt - m)]
= Cov(xt+h,xt )
Varxt  h  0

h0
 0
s 2

0
h0
h0
The auto correlation function:
s h 1 h  0
r h 

s o 0 h  0
Comment:
If m = 0 then the time series {xt : t  T} is called a white noise
time series.
Thus a white noise time series consist of independent
identically distributed random variables with mean 0 and
common variance s2
Excel file illustrating this time series
3. Suppose X1, X2, … , Xk and Y1, Y2, … , Yk are
independent independent random variables with
   
E X i   EYi   0 and E X i2  E Yi 2  s i2
Let 1, 2, … k denote k values in (0,p)
For any t T = { …,, -2, -1, 0, 1, 2, …}
k
xt   X i cosi t   Yi sin i t 
i 1
k
  X i cos2p i t   Yi sin 2p i t 
i 1
k

 2pt 
 2pt 
  Yi sin 

   X i cos
i 1 
 Pi 
 Pi 
Excel file illustrating this time series
Then
 k

E xt   E  X i cosi t   Yi sin i t 
 i 1

k
  E  X i  cosi t   E Yi sin i t   0
i 1
s h  Ext h xt 
 k
 E  X i cosi t  h  Yi sin i t  h
 i 1
k

j 1

X j cos j t   Y j sin  j t  



Hence
 k k
s h   E   X i X j cosi t  h  cos j t 
 i 1 j 1
 X iYj cosi t  hsin j t 
 Yi X j sini t  hcos j t 

 YiY j sin i t  h sin  j t  


k
 s i2 cosi t  h  cosi t   sin i t  h sin i t 
i 1




 
since E X iYj  0, E X i X j  0 E YiYj  0 if i  j
   
and E X i2  E Yi 2  s i2
Hence using
cos(A – B) = cos(A) cos(B) + sin(A) sin(B)
k
k
i 1
i 1
s h   s i2 cosi t  h   i t   s i2 cosi h 
and
k
s h 
r h  

s 0
2
s
 i cosi h
i 1
k
2
s
 j
j 1
where wi 
s i2
k
s
j 1
2
j
k
  wi cosi h 
i 1
4. The Moving Average Time series of order q, MA(q)
Let 0 =1, 1, 2, … q denote q + 1 numbers.
Let {ut|t  T} denote a white noise time series with
variance s2.
– independent
– mean 0, variance s2.
Let {xt|t  T} be defined by the equation.
xt  m  0ut  1ut 1  2ut 2   qut q
 m  ut  1ut 1  2ut 2   qut q
Then {xt|t  T} is called a Moving Average time series
of order q. MA(q)
Excel file illustrating this time series
The mean
Ext   Em  0ut  1ut 1  2ut 2   qut q 
 m  0 Eut   1Eut 1   2 Eut 2    q Eut q 
m
The auto covariance function
s  h   E  xt  h  m  xt  m  

 E ut h  1ut h1  2ut h2   qut hq 

 ut  1ut 1  2ut 2   qut q 
 q

 q
 E    i ut  h i    j ut  j 
 i 0
 j 0

q q

 E  i j ut  h i ut  j 
 i 0 j 0

q
q

  i j E ut  hi ut  j

i 0 j 0
 2  q h

s    i i  h  if i  q
   i 0


0
iq

 
 
2
2
and
E
u

s
.
since E uiu j  0 if i  j.
i
The autocovariance function for an MA(q) time series
 2  q h

s   i i  h  if i  q
s h     i 0


0
iq

The autocorrelation function for an MA(q) time series
 q h

s h     i i  h 
r h  
  i 0

s 0 
0

 q 2
   i  if i  q
 i 0 
iq
5. The Autoregressive Time series of order p, AR(p)
Let b1, b2, … bp denote p numbers.
Let {ut|t  T} denote a white noise time series with
variance s2.
– independent
– mean 0, variance s2.
Let {xt|t  T} be defined by the equation.
xt  b1xt 1  b2 xt 2   b p xt  p    ut
Then {xt|t  T} is called a Autoregressive time series of
order p. AR(p)
Excel file illustrating this time series
Comment:
An Autoregressive time series is not necessarily stationary.
Suppose {xt|t  T} is an AR(1) time series satisfying the
equation:
xt  b1 xt 1    ut
 xt 1  ut
where {ut|t  T} is a white noise time series with
variance s2. i.e. b1 = 1 and  = 0.
xt  xt 1  ut  xt 2  ut 1  ut
 x0  u1  u2   ut 1  ut
Ext   Ex0   Eu1   Eu2    Eut 1   Eut 
 Ex0 
but
Varxt   Varx0   Varu1    Varut 
 Varx0   ts 2
and is not constant.
A time series {xt|t  T} satisfying the equation:
xt  xt 1  ut
is called a Random Walk.
Derivation of the mean,
autocovariance function and
autocorrelation function of a
stationary Autoregressive time series
We use extensively the rules of
expectation
Assume that the autoregressive time series {xt|t T}
be defined by the equation:
xt  b1xt 1  b2 xt 2   b p xt  p    ut
is stationary.
Let m = E(xt). Then
Ext   b1Ext 1   b2 Ext 2    b p Ext  p    Eut 
m  b1m  b2 m   b p m  
1 b  b
1
2
 b p m  

or E xt   m 
1  b1  b 2    b p
The Autocovariance function, s(h)
The Autocovariance function, s(h), of a stationary
autoregressive time series {xt|t  T}can be determined
by using the equation:
xt  b1xt 1  b2 xt 2   b p xt  p    ut
Now   1  b1  b 2 
Thus
 bp m
xt  m  b1 xt 1  m    b p xt  p  m  ut
Hence
s h  Ext h  m xt  m 


 E b1 xt h1  m    b p xt h p  m  ut h xt  m 
 b1Ext h1  m xt  m   


 b p E xt h p  m xt  m   Eut h xt  m 
 b1s h 1   b ps h  p  s ux h
where
0
h0

s ux h  Eut  h xt  m   
Eut xt  m  h  0
Now
s ux  0   E ut  xt  m  

 E ut b1  xt 1  m  


 b p  xt  p  m   ut 


  
 b1Eut xt 1  m   b p E ut xt  p  m   E ut2
s
2
The equations for the autocovariance function of an
AR(p) time series
s 0  b1s 1   b ps  p  s 2
s 1  b1s 0   b ps  p 1
s 2  b1s 1   b ps  p  2
s 3  b1s 2   b ps  p  3
etc
Or using s(-h) = s(h)
s 0  b1s 1   b ps  p  s 2
s 1  b1s 0   b ps  p 1
s 2  b1s 1   b ps  p  2

s  p  b1s  p 1   b ps 0
and
s h  b1s h 1   b ps h  p
for h > p
Use the first p + 1 equations to find s(0), s(1) and s(p)
Then use
s h  b1s h 1   b ps h  p
To compute s(h) for h > p
The Autoregressive Time series of order p, AR(p)
Let b1, b2, … bp denote p numbers.
Let {ut|t  T} denote a white noise time series with
variance s2.
– independent
– mean 0, variance s2.
Let {xt|t  T} be defined by the equation.
xt  b1xt 1  b2 xt 2   b p xt  p    ut
Then {xt|t  T} is called a Autoregressive time series of
order p. AR(p)
If the autoregressive time series {xt|t  T} be
defined by the equation:
xt  b1xt 1  b2 xt 2   b p xt  p    ut
is stationary.
Then

E xt   m 
1  b1  b 2    b p
The Autocovariance function, s(h), of a stationary
autoregressive time series {xt|t  T} be defined by the
equation:
xt  b1xt 1  b2 xt 2   b p xt  p    ut
Satisfy the equations:
The mean

E  xt   m 
1  b1  b2 
 bp
The autocovariance function for an AR(p) time series
s 0  b1s 1   b ps  p  s
s 1  b1s 0   b ps  p 1
2
Yule Walker
Equations
s 2  b1s 1   b ps  p  2

s  p  b1s  p 1   b ps 0
and
s h  b1s h 1   b ps h  p
for h > p
Use the first p + 1 equations (the Yole-Walker Equations)
to find s(0), s(1) and s(p)
Then use
s h  b1s h 1   b ps h  p
To compute s(h) for h > p
The Autocorrelation function, r(h), of a stationary
autoregressive time series {xt|t  T}:
s h 
r h  
s 0
The Yule walker Equations become:
s2
1  b1r 1    b p r  p  
s 0
r 1  b11   b p r  p 1
r 2  b1r 1   b p r  p  2

r  p  b1r  p 1   b p1
and
r h  b1r h 1   b p r h  p
for h > p
To find r(h) and s(0): solve for r(1), …, r(p)
r 1  b11   b p r  p 1
r 2  b1r 1   b p r  p  2

r  p  b1r  p 1   b p1
Then
s 0 
s
2
1  b1r 1    b p r  p 
for h > p
r h  b1r h 1   b p r h  p
Example
Consider the AR(2) time series:
xt = 0.7xt – 1+ 0.2 xt – 2 + 4.1 + ut
where {ut} is a white noise time series with
standard deviation s = 2.0
White noise ≡ independent, mean zero
(normal)
Find m, s(h), r(h)
To find r(h) solve the equations:
r 1  b11 b2 r 1
r  2  b1r 1  b21
or
r 1  (0.7)1  0.2 r 1
r  2   0.7 r 1   0.21
thus
0.7 0.7
r 1 

 0.875
1  .2 0.8
r  2   0.7 0.875   0.2  0.8125
for h > 2
r  h  b1r  h 1  b2 r  h  2
  0.7 r  h 1   0.2 r  h  2
This can be used in sequence to find:
r 3 , r  4 , r 5 ,
etc.
results
h
0
r hh
 ) 1.0000
1
0.8750
2
0.8125
3
0.7438
4
0.6831
5
0.6269
6
0.5755
7
0.5282
8
0.4849
To find s(0) use:
s 0 
s2
1  b1r 1    b p r  p 
or
s  0 
s2
1  b1r 1  b2 r  2 
2.02

1   0.70 0.8750   0.20 0.8125
= 17.778
To find s(h) use:
s  h   s  0  r  h 
To find m use:

m
1  b1  b 2
4.1
4.1


 41
1   0.70    0.20  0.1
An explicit formula for r(h)
Auto-regressive time series of order p.
Consider solving the difference equation:
r h  b1r h 1  b p r h  p  0
This difference equation can be solved by:
Setting up the polynomial
b x  1 b1x  b p x
p
 x 
x  
x 
 1  1    1 
 r 
r
r
1 
2 
p 


where r1, r2, … , rp are the roots of the polynomial
b(x).
The difference equation
r h  b1r h 1  b p r h  p  0
has the general solution:
1
1
1
r h   c1    c2      c p  
 r1 
 r2 
 rp 
h
h
h
where c1, c2, … , cp are determined by using the starting
values of the sequence r(h).
Example: An AR(1) time series
xt  b1xt 1    ut
r 0  1
r 1  b1r 0  b1
for h > 1
r h  b1r h 1  b1h
and
s2
s2
s 0 

1  b1r 1 1  b12
The difference equation
r h  b1r h 1  0
Can also be solved by:
Setting up the polynomial
b x   1  b1 x
 x
1
 1   wherer1 
b1
 r1 
Then a general formula for r(h) is:
h
1
r h   c1    c1b1h  b1h since r 0  1
 r1 
Example: An AR(2) time series
xt  b1 xt 1  b2 xt 2    ut
r 0  1
and r 1  b1  b 2 r 1
b1
or r 1  r1 
1  b2
for h > 1
r h  b1r h 1  b2 r h  2
Setting up the polynomial
b x  1  b1x  b2 x
 x
 1  
 r1 
2

1 1  1  2
x
1    1     x  
x
 r2 
 r1 r2   r1r2 
 b1  b12  4 b 2
where r1 
2b 2
 b1  b12  4 b 2
and r2 
2b 2
1 1
1
Note: b1   and b 2  
r1 r2
r1r2
Then a general formula for r(h) is:
h
1
1
r h   c1    c2  
 r1 
 r2 
For h = 0 and h = 1.
1  c1  c2
b1
c1 c2
r 1  r1 
 
1  b 2 r1 r2
Solving for c1 and c2.
h
Solving for c1 and c2.


r1 1  r
c1 
r1r2  1r1  r2 
and

2
2

r2 r12  1
c2 
r1r2  1r1  r2 
Then a general formula for r(h) is:


h


1
1
r1 1  r
r2 r  1
  
 
r h  
r1r2  1r1  r2   r1  r1r2  1r1  r2   r2 
2
2
2
1
h
If
b12  4b2  0 r1 and r2


are real and
h


1
1
r1 1  r
r2 r  1
  
 
r h  
r1r2  1r1  r2   r1  r1r2  1r1  r2   r2 
2
2
is a mixture of two exponentials
2
1
h
If
b12  4b2  0 r1 and r2 are complex conjugates.
r1  x  iy  R  e
r2  x  iy  R  ei
i
x
1  x 
where R  x  y and tan   ,  tan  
y
 y
2
2
Some important complex identities
e  cos  i sin  , e
i
i
e e
cos  
2
 i
i
 cos  i sin 
i
e e
, sin  
2i
 i
The above identities can be shown using the power series
expansions:
2
3
4
u u u
e  1 u    
2! 3! 4!
u
cos  u 
2
4
6
u u u
 1  
2! 4! 6!
3
5
7
u u u
sin  u   u    
3! 5! 7!
Some other trig identities:
1. cos u  v   cos u  cos  v   sin u  sin  v 
2. cos u  v   cos u  cos  v   sin u  sin  v 
3. sin u  v   sin u  cos  v   cos u  sin  v 
4. sin u  v   sin u  cos  v   cos u  sin  v 
5. cos  2u   cos2 u   sin 2 u 
6. sin  2u   2sin u  cos u 


i


 i 2
r1 1  r
R  e 1 R  e
 2
i
i
r1r2  1r1  r2  R  1 R e  e
2
2

i
2
 i
e  R e
 2
R  1 2i sin  
2




 i


i 2


r2 r  1
R  e R  e 1
 2
i
i
r1r2  1r1  r2  R  1 R e  e
2
1

2
i
 i
R e  e
 2
R  1 2i sin  
2




Hence



h

1
1
r1 1  r
r2 r  1
  
 
r h  
r1r2  1r1  r2   r1  r1r2  1r1  r2   r2 
2
2
2
1
h
ei  R 2e i
e ih
R 2ei  ei
eih
 2
 h  2
 h
R  1 2i sin   R
R  1 2i sin   R





 

R 2 ei h1  e i h1  ei h1  ei h1

h
2
R R  1 2i sin  

R 2 sin  h  1  sin  h  1

h
2
R R  1 sin  



R 2 sin hcos   coshsin    sin hcos   coshsin  

R h R 2  1 sin  


R

2





 1 sinhcos   R 2  1 coshsin 
R h R 2  1 sin 

R2 1
cosh  2 sin hcot 
R 1

Rh
R2 1
if tan   2
cot 
R 1

cos h   sin h  tan  
Rh
cos h   sin h  tan  
Hence r h  
Rh
1
cosh  cos   sin h sin  


cos 
Rh
D cos h   

Rh
a damped cosine wave
cos2    sin 2  
1
where D 

 1  tan2  
cos 
cos 
Example
Consider the AR(2) time series:
xt = 0.7xt – 1+ 0.2 xt – 2 + 4.1 + ut
where {ut} is a white noise time series with
standard deviation s = 2.0
The correlation function found before using the
difference equation:
r(h) = 0.7 r(h – 1) + 0.2 r(h – 2)
h
0
r hh
 ) 1.0000
1
0.8750
2
0.8125
3
0.7438
4
0.6831
5
0.6269
6
0.5755
7
0.5282
8
0.4849
Alternatively setting up the polynomial


x
b  x   1 b1x  b2 x2  1 .7x  .2x2  1  
 r1 

x
1  
 r2 
b1  b  4b 2 .7  .7   4 .2 
where r1 

2b 2
2 .2 
2
2
1
 1.089454
b1  b  4b 2 .7  .7   4 .2 
and r2 

2b 2
2 .2 
2
1
 4.58945
2
Thus



h

1
1
r1 1  r
r2 r  1
  
 
r h  
r1r2  1r1  r2   r1  r1r2  1r1  r2   r2 
2
2
2
1
h
 rr
1 2  1 r1  r2   22.7156
r1 1  r
2
2
  21.8578
r1 1  r22 
 r1r2  1 r1  r2 
and r2  r  1  0.85782
2
1
 0.962237 and
h
r2  r12  1
 r1r2  1 r1  r2 
 0.037763
1
1




r  h   0.962237 
  0.037763

 1.089454 
 4.58945 
h
Another Example
Consider the AR(2) time series:
xt = 0.2xt – 1- 0.5 xt – 2 + 4.1 + ut
where {ut} is a white noise time series with
standard deviation s = 2.0
The correlation function found before using the
difference equation:
r(h) = 0.2 r(h – 1) - 0.5 r(h – 2)
h
0
r hh
 ) 1.0000
1
0.8750
2
0.8125
3
0.7438
4
0.6831
5
0.6269
6
0.5755
7
0.5282
8
0.4849
Alternatively setting up the polynomial


x
b  x   1 b1x  b2 x2  1 .2x  .5x2  1  
 r1 

x
1  
 r2 
 b1  b  4b 2 .2  .2   4  0.5
where r1 

2b 2
2  0.5
2
1
2
.2  1.96

 .2  1.96i
1
2
2
b1  b1  4b 2 .2  .2   4  0.5
and r2 

2b 2
2  0.5
.2  1.96

 .2  1.96i
1
Thus
i
r1  .2  1.96i  R  e
r2  .2  1.96i  R  ei
where
R  x 2  y 2  .22  1.96  2
and
x
0.2
tan   
 0.142857,
y  1.96
1
thus   tan  0.142857  0.141897
2 1
R2 1
cot  .14897   2.33333
Now tan    2 cot   
2 1
R 1
Thus   tan1  2.33333  1.165905
Also D  1  tan 2    1  2.33332  2.538591
D cos  h   
Finally r  h  
Rh

2.538591cos  0.141897h  1.165905
h
22
cos h   sin h  tan  
Hence r h  
Rh
1
cosh  cos   sin h sin  


cos 
Rh
D cos h   

Rh
a damped cosine wave
cos2    sin 2  
1
where D 

 1  tan2  
cos 
cos 
Conditions for stationarity
Autoregressive Time series of
order p, AR(p)
If b1 = 1 and  = 0.
i.e. xt  b1 xt 1  ut
The value of xt increases in magnitude and ut
eventually becomes negligible.
The time series {xt|t  T} satisfies the equation:
xt  b1 xt 1
The time series {xt|t  T} exhibits deterministic
behaviour.
Let b1, b2, … bp denote p numbers.
Let {ut|t  T} denote a white noise time series with
variance s2.
– independent
– mean 0, variance s2.
Let {xt|t  T} be defined by the equation.
xt  b1xt 1  b2 xt 2   b p xt  p    ut
Then {xt|t  T} is called a Autoregressive time series of
order p. AR(p)
Consider the polynomial
p


b x  1 b1x  b p x
 x 
x  
x 
 1  1    1 
 r 
r
r
1 
2 
p 


with roots r1, r2 , … , rp
then {xt|t T} is stationary if |ri| > 1 for all i.
If |ri| < 1 for at least one i then {xt|t T} exhibits
deterministic behaviour.
If |ri| ≥ 1 and |ri| = 1 for at least one i then {xt|t  T}
exhibits non-stationary random behaviour.
Special Cases: The AR(1) time
Let {xt|t  T} be defined by the equation.
xt  b1 xt 1    ut
Consider the polynomial
 x
b x   1  b1 x  1  
 r1 
with root r1= 1/b1
1. {xt|t T} is stationary if |r1| > 1 or |b1| < 1 .
2. If |ri| < 1 or |b1| > 1 then {xt|t T} exhibits
deterministic behaviour.
3. If |ri| = 1 or |b1| = 1 then {xt|t T} exhibits nonstationary random behaviour.
Special Cases: The AR(2) time
Let {xt|t T} be defined by the equation.
xt  b1xt 1  b2 xt 2    ut
Consider the polynomial
 x 
x
b x  1  b1x  b2 x  1  1  
 r1  r2 
where r1 and r2 are the roots of b(x)
1. {xt|t T} is stationary if |r1| > 1 and |r2| > 1 .
This is true if b1+b2 < 1 , b2 –b1 < 1 and b2 > -1.
2
These inequalities define a triangular region for
b1 and b2.
2. If |ri| < 1 or |b1| > 1 then {xt|t T} exhibits
deterministic behaviour.
3. If |ri| ≤ 1 for i = 1,2 and |ri| = 1 for at least on i then
{xt|t T} exhibits non-stationary random behaviour.
Patterns of the ACF and PACF of AR(2) Time Series
In the shaded region the roots of the AR operator are complex
b2