Linear Time Series Models for Stationary data - Feweb

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Key concept: Random Walk (RW) Best known economic example of a weakly nonstationary series is a Random Walk Process (RW). RW is the partial sum process of a WN sequence: t εj t = 1, 2, . . .. ut = j=1 The variance (conditional on starting values) of ut increases (linearly) with t, even if t → ∞, contradicting stationarity condition for variance. Exercise (1) Derive this result. Chapter 7.1 Heij et al, TI Econometrics II 2006/2007 – p. 8/24
Log Dow Jones: Nonstationary series Log transformed Dow Jones Index Industrials Weekly 1896-2004. Exercise (2): approximate mean weekly growth using the information from this graph. Does the outcome make sense?. 10 9 8 7 6 5 4 3 1900 1925 1950 1975 2000 LOG(DJIND) Chapter 7.1 Heij et al, TI Econometrics II 2006/2007 – p. 9/24
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: Key concept: SACF, White Noise (WN)
Page 11 and 12: Autocovariance, -correlation, corre
Page 13 and 14: Scatter plot log weekly Dow Jones v
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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