Linear Time Series Models for Stationary data - Feweb

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Identification / Data Analysis The sample characteristics of a time series should match the (theoretical) population characteristics of the statistical time series model. In the identification stage we choose a (unique) model corresponding to the observed characteristics. <strong>Time</strong> series plots and correlograms are the main tools <strong>for</strong> analysing the empirical properties of time series. It is often illuminating to consider (partial) scatter plots of yt versus yt−k in the context of the linear, infinite order autoregressive, (one-step-ahead) prediction decomposition of a time series process. See also §5.5, where similar techniques were applied as diagnostic tools on residuals after a first model was estimated. Chapter 7.1 Heij et al, TI Econometrics II 2006/2007 – p. 12/24
Scatter plot log weekly Dow Jones vs. lagged value Log Dow Jones Index Industrials Weekly 1896-2004. ’Autoregression’ scatterplot. LOG(DJIND) 10 9 8 7 6 5 4 3 3 4 5 6 7 8 9 10 LOG(DJIND(-1)) Chapter 7.1 Heij et al, TI Econometrics II 2006/2007 – p. 13/24
Page 1 and 2: Econometrics II - Heij et al. Chapt
Page 3 and 4: Introduction Macroeconometrics and
Page 5 and 6: Key concepts of linear time series
Page 7 and 8: Key concept: SACF, White Noise (WN)
Page 9 and 10: Log Dow Jones: Nonstationary series
Page 11: Autocovariance, -correlation, corre
Page 15 and 16: AR(∞) representation A simple ide
Page 17 and 18: AR(20) (+trend) Log Dow Jones Log D
Page 19 and 20: Practical purpose of linear time se
Page 21 and 22: Lag polynomials Examples of lag pol
Page 23 and 24: Linear Stochastic processes, Wold r

Linear Time Series Models for Stationary data - Feweb

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