Four-step iterative procedures 1)Model Identification Parameter estimation 234 Diagnostic Checking Forecasting
2 Four-step iterative procedures 1) Model Identification 2) Parameter Estimation 3) Diagnostic Checking 4) Forecasting
Model identification I. Stationarit I. Theoretical autocorrelation function (TAC II. Theoretical Partial autocorrelation Function(TPAc IV. Sample partial Autocorrelation Function (SPAC V. Sample autocorrelation Function (SAC
4 Model Identification I. Stationarity II. Theoretical Autocorrelation Function (TAC) III. Theoretical Partial Autocorrelation Function (TPAC) IV. Sample Partial Autocorrelation Function (SPAC) V. Sample Autocorrelation Function (SAC)
Stationarity (D A sequence of jointly dependent random variables -oo<t<o is called a time series
5 Stationarity (I) A sequence of jointly dependent random variables is called a time series {y : − t } t
Stationarity(l) ◆ Stationary process Properties (1)E(=u, for all t (2)ar(y)=E(y,-1)]=a2ral (3)Cov(,, yk=r for all t
6 Stationarity (II) Stationary process Properties : (3) ( , ) . (2) ( ) [( ) ] . (1) ( ) . 2 2 Cov y y for all t Var y E y u for all t E y u for all t t t k k t t y y t y = = − = = +
Stationarity (ir) Example: The white noise series &G/ .Es are id as n(o, o ). Note that ()E(e=0 for all t (2)Var(&)=E()=0f for all t (3)CoV(e, Es=0 for all t
7 Stationarity (III) Example: The white noise series {et } ◼ e’s are iid as N(0,e 2 ). Note that (3) Cov( , ) 0 for all t. (2) Var( ) E( ) for all t. (1) E( ) 0 for all t. t t s 2 2 t t t = = = = + e e e e e e
Stationarity (Iv) Three basic box-Jenkins models for a stationary time series t,/ (1)Autoregressive model of order p(ar(p) y1=O+y1+n2V12+…+ p.t-p 1. e,, y, depends on its p previous values (2) Moving Average model of order g malg) y 6+E.-6 t-2 t-9 1. e, y, depends on g previous random error terms
8 Stationarity (IV) Three basic Box-Jenkins models for a stationary time series {yt } : (1) Autoregressive model of order p (AR(p)) i.e., yt depends on its p previous values (2) Moving Average model of order q (MA(q)) i.e., yt depends on q previous random error terms, t 1 t 1 2 t 2 p t p t y = + y + y + + y +e − − − , t t 1 t 1 2 t 2 q t q y = + − − − − − − − e e e e
Stationarity(v) Three basic box-Jenkins models for a stationary time series ty (3) Autoregressive-moving average model of order p and g (arma(p, q) y=+y1+n2y2+…+yp +6-661-62E12-…-6 g t-g i.e., y, depends on its p previous values and g previous random error terms
9 Stationarity (V) Three basic Box-Jenkins models for a stationary time series {yt } : (3) Autoregressive-moving average model of order p and q (ARMA(p,q)) i.e., yt depends on its p previous values and q previous random error terms , 1 1 2 2 1 1 2 2 t t t q t q t t t p t p y y y y − − − − − − + − − − − = + + + + e e e e
AR(1)(I) Simple ar(i) process without drift Py+E(where at is white noise y, =O,Ly, +E(where L is the back-shift operator) 0(L)=(1-Ly or y 1-如L E1(1+L+:L2+…) =E+1+2+ 10
10 AR(1) (I) Simple AR(1) process without drift . (1 L L ) 1 L y or (L) (1 L)y y Ly (where L is the back shift operator) y y (where is white noise ) t 2 2 t 1 t 1 1 2 2 t 1 1 1 t t 1 t t t 1 t t t 1 t 1 t t = + + + = + + + − = = − = = + − = + − − − e e e e e e e e