Transcription of AR, MA and ARMA models - Hedibert
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AR, MA and ARMA models - Hedibert
StationarityACFL jung-BoxtestWhite noiseAR modelsExamplePACFAIC/BICF orecastingMA modelsSummaryAR, MA and ARMA models1 Stationarity2 ACF3 Ljung-Box test4 White noise5AR models6 Example7 PACF8 AIC/BIC9 Forecasting10MA models11 Summary1 / 40 StationarityACFL jung-BoxtestWhite noiseAR modelsExamplePACFAIC/BICF orecastingMA modelsSummaryLinear Time Series Analysisand Its Applications1 For basic concepts of linear time series analysis see Box, Jenkins, and Reinsel (1994, Chapters 2-3), and Brockwell and Davis (1996, Chapters 1-3)The theories of linear time series discussed include stationarity dynamic dependence autocorrelation function modeling forecasting1 Tsay (2010), Chapter / 40 StationarityACFL jung-BoxtestWhite noiseAR modelsExamplePACFAIC/BICF orecastingMA modelsSummaryThe econometric models introduced include(a) simple autoregressive models ,(b) simple moving-average models ,(b) mixed autoregressive moving-average models ,(c) seasonal models ,(d) unit-root nonstationarity,(e) regression models wit
1) , one can rewrite a stationary AR(1) model as r t= ˚ 0 + ˚ 1r t 1 + a t; such that ˚ 1 measures the persistence of the dynamic dependence of an AR(1) time series. The ACF of the AR(1) is l= ˚ 1 l 1 l>0; where 0 = ˚ 1 1 + ˙ a 2 and l= . Also, ˆ l= ˚l 1; i.e., the ACF of a weakly stationary AR(1) series decays exponentially with rate ...
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