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Unformatted text preview: Durbin Watson statistics close to 0 3 look at autocorrelation function of Xt 4 look at the Augmented Dickey Fuller test statistic 20 / 52 Introduction Stationary Processes Nonstationary Processes Spurious Regressions Testing for Nonstationarity Cointegration
Autocorrelation function ADF Testing for Nonstationarity There are a few ways of detecting nonstationarity of which were
covered in the problem sets.
1 look at the the time series for Xt 2 look for Durbin Watson statistics close to 0 3 look at autocorrelation function of Xt 4 look at the Augmented Dickey Fuller test statistic 21 / 52 Introduction Stationary Processes Nonstationary Processes Spurious Regressions Testing for Nonstationarity Cointegration
Autocorrelation function ADF Testing for Nonstationarity There are a few ways of detecting nonstationarity of which were
covered in the problem sets.
1 look at the the time series for Xt 2 look for Durbin Watson statistics close to 0 3 look at autocorrelation function of Xt 4 look at the Augmented Dickey Fuller test statistic 22 / 52 Introduction Stationary Processes Nonstationary Processes Spurious Regressions Testing for Nonstationarity Cointegration
Autocorrelation function ADF Autocorrelation function
The autocorrelation function is a useful graphical tool for
detecting nonstationarity. Suppose we have the following
model:
Xt = β Xt −1 + εt
Then
corr (Xt , Xt +s ) = β s
So we can plot the sample autocorrelation function given by
ρ(s) =
ˆ ¯
¯
(Xt − X )(Xt +s − X )
¯
(Xt − X )2 ¯
(Xt +s − X )2 If the β  < 1 then this quantity approximates the true
autocorrelation function.
23 / 52 Introduction Stationary Processes Nonstationary Processes Spurious Regressions Testing for Nonstationarity Cointegration
Autocorrelation function ADF Autocorrelation function
The autocorrelation function is a useful graphical tool for
detecting nonstationarity. Suppose we have the following
model:
Xt = β Xt −1 + εt
Then
corr (Xt , Xt +s ) = β s
So we can plot the sample autocorrelation function given by
ρ(s) =
ˆ ¯
¯
(Xt − X )(Xt +s − X )
¯
(Xt − X )2 ¯
(Xt +s − X )2 If the β  < 1 then this quantity approximates the true
autocorrelation function.
24 / 52 Introduction Stationary Processes Nonstationary Processes Spurious Regressions Testing for Nonstationarity Cointegration
Autocorrelation function ADF Autocorrelation function
The autocorrelation function is a useful graphical tool for
detecting nonstationarity. Suppose we have the following
model:
Xt = β Xt −1 + εt
Then
corr (Xt , Xt +s ) = β s
So we can plot the sample autocorrelation function given by
ρ(s) =
ˆ ¯
¯
(Xt − X )(Xt +s − X )
¯
(Xt − X )2 ¯
(Xt +s − X )2 If the β  < 1 then this quantity approximates the true
autocorrelation function.
25 / 52 Introduction Stationary Processes Nonstationary Processes Spurious Regressions Testing for Nonstationarity Cointegration
Autocorrelation function ADF Autocorrelation function
The autocorrelation function is a useful graphical tool for
detecting nonstationarity. Suppose we have the following
model:
Xt = β Xt −1 + εt
Then
corr (Xt , Xt +s ) = β s
So we can plot the sample autocorr...
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This document was uploaded on 03/12/2014 for the course ECON 202 at University of London University of London International Programmes (Distance Learning).
 Spring '13
 ChristopherDougherty
 Econometrics

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