a) AR(1) process
Transcript of a) AR(1) process
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30C00200 Econometrics
10 Time series econometrics
a) AR(1) process
Timo KuosmanenProfessor, Ph.D.
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Examples of time series
• Macroeconomic data:
– GDP
– Inflation rate
• Financial market data:
– Asset returns
– Stock prices
• Business Analytics: ”Big data” (new types of data from internet,
social media, etc.):
– Google Trends
– Facebook ”likes”2
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How time series and cross sections differ?
• Cross section i =1,…,n represents a randomly drawn sample from
the population
• Time series t = 1,…,T describes a (random) path of a variable in a
time window [1, T]
• Time series have a natural chronological order, whereas in a cross
section the ordering of observations does not matter
• Path dependence: correlations across time periods (t, t+1)
– Autocorrelation (serial correlation)
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Static vs. Dynamic time series models
• Static model
yt = β1 + β2xt + εt , t=1,…,T.
Allow for autocorrelation (Cov[εt, εs] ≠ 0) using AR(1) process.
• Dynamic models
– Lagged explanatory variable(s)
yt = β1 + β2xt + β3xt-1 + εt, t=1,…,T.
– Lagged dependent variable(s) forming AR(1) process
yt = γyt-1 + β1 + β2xt + εt , t=1,…,T.
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Autoregressive (AR) process
• First-order autoregressive AR(1) process of random variable v
vt = ρvt-1 + ut
• ρ is autocorrelation coefficient and
• ut is stationary, nonautocorrelated white noise process with
E[ut]=0, Cov[ut , ut-1]=0, Var[ut]=σu2
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Simulated AR(1) process, t = 1,…, 500
ρ = 0 (white noise)
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Simulated AR(1) process, t = 1,…, 500
ρ = 0.8 (positive autocorrelation)
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Simulated AR(1) process, t = 1,…, 500
ρ = -0.8 (negative autocorrelation)
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Higher-order autoregressive (AR) processes
• Second-order autoregression AR(2) process
vt = ρ1vt-1 + ρ2vt-2 + ut
• Pth-order autoregression AR(P) process
vt = ρ1vt-1 + ρ2vt-2 + … + ρPvt-P + ut
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AR(1) process
• Each variable vt embodies the entire past history of u
vt = ρvt-1 + ut
= ut + ρut-1 + ρ2ut-2 + ρ3ut-3 … + ρt-1u1
• Hence,
Var[vt]= σu2 + ρ2σu
2 + ρ4σu2 + ρ6σu
2 … + ρ(t -1)(t -1)σu2
• Process is stationary if ρ satisfy
|ρ| < 1
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Unit root
• The case where ρ = 1 is called the unit root
vt = vt-1 + ut
= ut + ut-1 + ut-2 + ut-3 … + u1
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Simulated AR(1) process, t = 1,…, 500
ρ = 1 (unit root)
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Simulated AR(1) process, t = 1,…, 500
ρ = 1.05
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Next topic
b) Static model with autocorrelation
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