ARMA Model Identification by ByoungSeon Choi (auth.)

By ByoungSeon Choi (auth.)

During the final twenty years, huge growth has been made in statistical time sequence research. the purpose of this publication is to offer a survey of 1 of the main energetic parts during this box: the identity of autoregressive moving-average types, i.e., picking their orders. Readers are assumed to have already taken one direction on time sequence research as may be provided in a graduate direction, yet another way this account is self-contained. the most subject matters coated contain: Box-Jenkins' procedure, inverse autocorrelation features, penalty functionality identity reminiscent of AIC, BIC options and Hannan and Quinn's procedure, instrumental regression, and a variety of development id tools. instead of disguise the entire equipment intimately, the emphasis is on exploring the basic principles underlying them. broad references are given to the study literature and for that reason, all these engaged in learn during this topic will locate this a useful reduction to their work.

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164). To utilize the PACF for model identification, it is necessary to study the asymptotic distribution of the YW estimate of the PACF. 1. The Asymptotic Distribution of the PACF Let {Ytl be the AR(p) process. Then y'T~P+l'P+l' y'T~p+2,p+2' ... , are asymptotically independent random variables with means 0 and variances 1. 3. If the underlying process is from an AR(p) model, then

U {(p,i) I i = q,q + 1, ... }, then ~j~~) = 4Jj + Op ( Jr), j = 1, ... ,k. The term Op(l/VT) becomes Op(l/T) if the process is purely nonstationary and k = p. 6 we anticipate that the EYW estimates and the ILS estimates would be asymptotically equivalent as long as the underlying ARMA process is stationary. 7. , ... 1). Then, for j = i + 1, ... ,i + k, Using Cramer's rule we can derive the following. 1. 1). If (k,i) E {(k,q) I k =p,p+1, ... }U {(p,i) I i = q, q + 1, ... _ J - 1, ... ,k. 1 means the EYW estimates of the AR parameters are asymptotically equivalent to the ILS estimates if the underlying process is stationary.

For the case of an AR(I) model, he has defined 1 7rh and has shown that 1 Ey'fYt+h 2 ,h > 0, EYtYt-h = I> 1 if 1cPl 1< 1, 17rh 1< 1 if 1cPl I> l. d=l Yt Yt+h T 2 2::t=h+l Yt Yt-h as a test statistic. Recently some econometricians have paid attention to utilizing ARMA processes with unit roots of cP(z) = o. Such processes have appeared in stock market and commodity market data as well as aggregated time series. Phillips and Perron (1988) provided a brief history of the use of the unit root models in economics.

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