Linear Time Series Models for Stationary data - Feweb
Linear Time Series Models for Stationary data - Feweb
Linear Time Series Models for Stationary data - Feweb
Create successful ePaper yourself
Turn your PDF publications into a flip-book with our unique Google optimized e-Paper software.
Key concept: SACF, White Noise (WN)<br />
Consistent estimates <strong>for</strong> mean, variance and covariances <strong>for</strong> weakly<br />
stationary processes are obtained as follows:<br />
µ = ¯y, γk = 1<br />
n<br />
n<br />
t=k+1<br />
(yt − ¯y)(yt−k − ¯y).<br />
γk, k = 0, 1, 2, . . . is the sample autocovariance function (SACF).<br />
Simplest example of a stationary series is a White Noise process<br />
(WN) which we denote as εt. WN is a sequence of uncorrelated<br />
random variables with constant mean and variance:<br />
γ0 = σ 2 ε, γk = 0, k = 1, 2, . . ..<br />
Chapter 7.1 Heij et al, TI Econometrics II 2006/2007 – p. 7/24
Change language