lecture 2
DESCRIPTION
Lecture 2. ARMA models 2012 International Finance CYCU. White noise?. About color? About sounds? Remember the statistical definition!. White noise. Def: { t } is a white-noise process if each value in the series: zero mean constant variance no autocorrelation In statistical sense: - PowerPoint PPT PresentationTRANSCRIPT
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White noise• Def: {t} is a white-noise process if each val
ue in the series:– zero mean– constant variance– no autocorrelation
• In statistical sense:– E(t) = 0, for all t– var(t) = 2 , for all t– cov(t t-k ) = cov(t-j t-k-j ) = 0, for all j, k, jk
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White noise• w.n. ~ iid (0, 2 )
iid: independently identical distribution
• white noise is a statistical “random” variable in time series
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The AR(1) model (with w.n.)• yt = a0 + a1 yt-1 + t • Solution by iterations• yt = a0 + a1 yt-1 + t
• yt-1 = a0 + a1 yt-2 + t-1
• yt-2 = a0 + a1 yt-3 + t-2
• • y1 = a0 + a1 y0 + 1
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General form of AR(1)
• Taking E(.) for both sides of the eq.
it
1t
0i
i10
t1
1t
0i
i10t ayaaay
it
1t
0i
i10
t1
1t
0i
i10t aEyaEaaE)y(E
0t1
1t
0i
i10t yaaa)y(E
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Compare AR(1) models• Math. AR(1)
0t
11
t1
0t y)a(a1a1ay
• “true” AR(1) in time series
0t
11
t1
0t y)a(a1a1a)yE(
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Infinite population {yt}• If yt is an infinite DGP, E(yt) implies
constant) a :(note )a1(
aylim1
0tt
• Why? If |a1| < 1
0t
11
t1
0t y)a(a1a1ay
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Stationarity in TS• In strong form
– f(y|t) is a distribution function in time t– f(.) is strongly stationary if
f(y|t) = f(y|t-j) for all j• In weak form
– constant mean– constant variance– constant autocorrelation
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Weakly Stationarity in TSAlso called “Covariance-stationarity”• Three key features
– constant mean– constant variance– constant autocorrelation
• In statistical sense: if {yt} is weakly stationary, – E(yt) = a constant, for all t– var(yt) = 2 (a constant), for all t– cov(yt yt-k ) = cov(yt-j yt-k-j ) =a constant,
for all j, k, jk
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AR(p) models
– where t ~ w. n.
• For example: AR(2)– yt = a0 + a1 yt-1 + a2 yt-2 + t
• EX. please write down the AR(5) model
tit
p
1ii0t yaay
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Stationarity Restrictions for ARMA(p,q)
• Enders, p.60-61.• Sufficient condition
• Necessary condition
1|a|p
1ii
1ap
1ii
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The MA(2) model• Make sure you can write down MA(2) as...
2t21t1t0t εbεbεay • Ex. Write down the MA(5) model...
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ARMA(p,q) models• ARMA=AR+MA, i.e.
– general form
• ARMA=AR+MA, i.e.– ARMA(1,1) = AR(1) + MA(1)
it
q
1iitit
p
1ii0t εbεyaay
itititi0t εbεyaay