Suppose we have the following model xt xt 1 t then

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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).

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