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Multivariate Time Series Analysis. Definition :. Let { x t : t T } be a Multivariate time series. m ( t ) = mean value function of { x t : t T } = E [ x t ] for t T . - PowerPoint PPT Presentation
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Multivariate Time Series Analysis
Let {xt : t T} be a Multivariate time series.
Definition:
(t) = mean value function of {xt : t T}
= E[xt] for t T.
(t,s) = Lagged covariance matrix of {xt : t T} = E{[ xt - (t)][ xs - (s)]'} for t,s T
Definition:
The time series {xt : t T} is stationary if the joint distribution of
is the same as the joint distribution of
for all finite subsets t1, t2, ... , tk of T and all choices of h.
kttt xxx ,,,21
hththt k xxx ,,,21
In this case then for t T.
and(t,s) = E{[ xt - ][ xs - ]'}
= E{[ xt+h - ][ xs+h - ]'}
= E{[ xt-s - ][ x0 - ]'}
= (t - s) for t,s T.
μμ )()( itxEt
Definition:The time series {xt : t T} is weakly stationary if :
for t T.and
(t,s) = (t - s) for t, s T.
μμ )(t
In this case
(h) = E{[ xt+h - ][ xs - ]'}
= Cov(xt+h,xt )
is called the Lagged covariance matrix of the process {xt : t T}
The Cross Correlation Function and the Cross Spectrum
Note: ij(h) = (i,j)th element of (h),
and is called the cross covariance function of
jht
it xx ,cov
. and js
it xx
00 jjii
ijij
hh
is called the cross correlation function of . and j
si
t xx
Definitions:
. and js
it xxis called the cross spectrum of
ki
kijij ekf
21i)
Note: since ij(k) ≠ ij(-k) then fij() is complex.
If fij() = cij() - i qij() then cij() is called the Cospectrum (Coincident spectral density) and qij() is called the quadrature spectrum
ii)
If fij() = Aij() exp{iij()} then Aij() is called the Cross Amplitude Spectrum and ij() is called the Phase Spectrum.
iii)
Definition:
is called the Spectral Matrix
pppp
p
p
ijpp
fff
ffffff
f
21
22221
11211
F
The Multivariate Wiener-Khinchin Relations (p-variate)
and
h
hi
ppppeh
ΣF
21
dehpp
hi
ppFΣ
Lemma:
i) Positive semidefinite:a*F()a ≥ 0 if a*a ≥ 0, where a is any complex vector.
ii) Hermitian:F() = F*() = the Adjoint of F() = the complex conjugate transpose of F(). i.e.fij() = .
Assume that
||
hij h
Then F() is:
Corrollary:The fact that F() is positive semidefinite also means that all square submatrices along the diagonal have a positive determinant
0
jjji
ijii
ffffHence
jiijjjii ffff and
jjiiijijij fffff 2*or
Definition:
= Squared Coherency function
jjii
ijij ff
fK
2
2
12 ijKNote:
Definition:
ii
ijij f
f
. and with associated jt
it xxfunctionTransfer
Applications and Examples of Multivariate Spectral Analysis
Example I - Linear Filters
denote a bivariate time series with zero mean.
Let
t = ..., -2, -1, 0, 1, 2, ...
Tt
yx
t
t :
sstst xay
Suppose that the time series {yt : t T} is constructed as follows:
The time series {yt : t T} is said to be constructed from {xt : t T} by means of a Linear Filter.
httyy yyEh
'''
sshts
ssts xaxaE
ssht
sstss xxaaE '
''
s s
shtstss xxEaa'
''
continuing hyy
s sss sshaa
'' '
s s
xxsshi
ss dfeaa'
''
s s
xxsshi
ss dfeaa'
''
s s
xxsisi
sshi dfeeaae
'
''
dfeaeae xxs
sis
s
sis
hi
'
''
continuing hyy
dfeae xxs
sis
hi2
dfAe xxhi
2
Thus the spectral density of the time series {yt : t T} is:
xxxx
s
sisyy fAfeaf 2
2
Comment A:
is called the Transfer function of the linear filter.
is called the Gain of the filter while
is called the Phase Shift of the filter.
s
siseaA
A
Aarg
Also httxy yxEh
sshtst xaxE
s
shtts xxEa
sxxs sha
continuing
hxy
dfeas
xxshi
s
dfea xxs
shis
dfAe xxhi
Thus cross spectrum of the bivariate time series
Tt
yx
t
t :
is:
xx
sxx
sisxy fAfeaf
Comment B:
= Squared Coherency function.
yyxx
xyxy ff
fK
2
2
1 2
22
xxxx
xx
fAf
fA
Example II - Linear Filterswith additive noise at the output
denote a bivariate time series with zero mean.
Let
t = ..., -2, -1, 0, 1, 2, ...
Tt
yx
t
t :
Suppose that the time series {yt : t T} is constructed as follows:
ts
stst vxay
The noise {vt : t T} is independent of the series {xt : t T} (may be white)
httyy yyEh
shtshts
ststs vxavxaE
s
htstss s
shtstss vxEaxxEaa'
''
thts
tshts vvEvxEa
'''
hsshaa vvs s
xxss
'' '
dfedfeae vvhi
xxs
sis
hi
2
continuing
hyy
dffAe vvxxhi 2
s
siseaA where
Thus the spectral density of the time series {yt : t T} is:
vvxxyy ffAf 2
Also httxy yxEh
shtshtst vxaxE
htts
shtts vxExxEa
sxxs sha
continuing
hxy
dfeas
xxshi
s
dfea xxs
shis
dfAe xxhi
Thus cross spectrum of the bivariate time series
Tt
yx
t
t :
is:
xx
sxx
sisxy fAfeaf
Thus
= Squared Coherency function.
yyxx
xyxy ff
fK
2
2
vvxxxx
xx
ffAf
fA
2
22
11
1
1 2
xx
vv
fAf
Noise to Signal Ratio
Estimation of the Cross Spectrum
Let
T
T
yx
yx
yx
,,,2
2
1
1
denote T observations on a bivariate time series with zero mean.If the series has non-zero mean one uses
in place of
yyxx
t
t
t
t
yx
Again assume that T = 2m +1 is odd.
Then define:
and
with k = 2k/T and k = 0, 1, 2, ... , m.
T
tkt
xk
T
tkt
xk tx
Tbtx
Ta
11
)sin(2,)cos(2
T
tkt
yk
T
tkt
yk ty
Tbty
Ta
11
)sin(2,)cos(2
Also
and
for k = 0, 1, 2, ... , m.
T
tkt
xk
xkk tix
TibaX
1
exp2
T
tkt
yk
ykk tiy
TibaY
1
exp2
The Periodogram &
the Cross-Periodogram
Also
and
for k = 0, 1, 2, ... , m.
2
1
2
1
)cos()sin(2 T
tkt
T
tktk
xxT txtx
TI
222
222 kkkxk
xk XTXXTbaT
2
1
2
1
)cos()sin(2 T
tkt
T
tktk
yyT tyty
TI
222
222 kkkyk
yk YTYYTbaT
Finally
yk
yk
xk
xkkkk
xyT ibaibaTYXTI
22
yk
xk
yk
xk
yk
xk
yk
xk baabibbaaT
2
xk
xk
yk
ykkkk
yxT ibaibaTXYTI
22
yk
xk
yk
xk
yk
xk
yk
xk baabibbaaT
2
kxy
TI of conjugatecomplex
Note:
and
1
1 1
)exp(2 T
Thkht
hT
ttk
xxT hixx
TI
1
1
)exp(2T
Thkxx hihC
1
1 1
)exp(2 T
Thkht
hT
ttk
yyT hiyy
TI
1
1
)exp(2T
Thkyy hihC
Also
and
1
1 1
)exp(2 T
Thkht
hT
ttk
xyT hiyx
TI
1
1
)exp(2T
Thkxy hihC
1
1 1
)exp(2 T
Thkht
hT
ttk
yxT hixy
TI
1
1
)exp(2T
Thkyx hihC
The sample cross-spectrum, cospectrum
& quadrature spectrum
Recall that the periodogram
2
1
2
1
)cos()sin(2 T
tkt
T
tktk
xxT txtx
TI
has asymptotic expectation 4fxx().
kxy
TI Similarly the asymptotic expectation of
is 4fxy().
An asymptotic unbiased estimator of fxy() can be obtained by dividing by 4. k
xyTI
The sample cross spectrum
1
1
)exp(21
21ˆ
T
Thkxyk
xyTkxy hihCIf
The sample cospectrum
1
1
)cos(21ˆReˆ
T
Thkxykxykxy hihCfc
The sample quadrature spectrum
1
1
)sin(21ˆImˆ
T
Thkxykxykxy hihCfq
The sample Cross amplitude spectrum,
Phase spectrum &
Squared Coherency
Recall
22 xyxyxy qcSpectrumAmplitudeCrossA
xy
xyxy c
qSpectrumPhase 1tan
functionCoherencySquared
ffqc
ff
fK
yyxx
xyxy
yyxx
xyxy
222
2
Thus their sample counter parts can be defined in a similar manner. Namely
\ sample ˆ SpectrumAmplitudeCrossAxy
xy
xyxy c
qSpectrumPhase
ˆˆ
tan sample ˆ 1
functionCoherencySquared
ff
qc
ff
fK
yyxx
xyxy
yyxx
xy
xy
sample
ˆˆˆˆ
ˆˆ
ˆˆ
222
2
22 ˆˆ xyxy qc
Consistent Estimation of the Cross-spectrum fxy()
Daniell Estimator
= The Daniell Estimator of the Cospectrum
d
drrkk
xyTd c
dc ˆ
121ˆ ,
= The Daniell Estimator of the quadrature spectrum
d
drrkk
xyTd q
dq ˆ
121ˆ ,
Weighted Covariance Estimator
hhChwc kh
xymkxy
mTw
cos21ˆ ,,
hhChwq kh
xymkxy
mTw
sin21ˆ ,,
of sequence a are ,2,1,0: where hhwm
such that weights
100 i) mm whw
hwhw mm ii)
.for 0 iii) mhhwm
Again once the Cospectrum and Quadrature Spectrum have been estimated,The Cross spectrum, Amplitude Spectrum, Phase Spectrum and Coherency can be estimated generally as follows using either the
a) Daniell Estimator or b) the weighted covariance estimator
of cxy() and qxy():
Namely
22 ˆˆˆxyxyxy qcA
xy
xyxy c
qˆˆ
tanˆ 1
yyxx
xyxy
yyxx
xy
xy ff
qc
ff
fK ˆˆ
ˆˆˆˆ
ˆˆ
222
2
xyxyxy qicf ˆˆˆ