γ 0 = Var(X i ) = Var(φ 1 X i 1 +W i ) = φ 2 1γ 0 +σ 2, which implies that we must have φ 1 < 1, and γ 0 = σ2 . 1 φ 2 1 We may also calculate for j 1

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1 4.2 Autoregressive (AR) Moving average models are causal linear processes by definition. There is another class of models, based on a recursive formulation similar to the exponentially weighted moving average. Definition 4.11 (Autoregressive AR(p)). Suppose φ 1,...,φ p R are constants and(w i ) WN(σ 2 ).TheAR(p)processwithparametersσ 2,φ 1,...,φ p isdefined through p X i = W i + φ j X i j, (3) j=1 whenever such stationary process (X i ) exists. Remark The process in Definition 4.11 is sometimes called a stationary AR(p) process. It is possible to consider a non-stationary AR(p) process for any φ 1,...,φ p satisfying (3) for i 0 by letting for example X i = 0 for i [ p+1,0]. Example 4.13 (Variance and autocorrelation of AR(1) process). For the AR(1) process, whenever it exits, we must have γ 0 = Var(X i ) = Var(φ 1 X i 1 +W i ) = φ 2 1γ 0 +σ 2, which implies that we must have φ 1 < 1, and We may also calculate for j 1 γ 0 = σ2. 1 φ 2 1 γ j = E[X i X i j ] = E[(φ 1 X i 1 +W i )X i j ] = φ 1 E[X i 1 X i j ] = φ j 1γ 0, which gives that ρ j = φ j 1. Example Simulation of an AR(1) process. phi_1 <- 0.7 x <- arima.sim(model=list(ar=phi_1), 140) # This is the explicit simulation: gamma_0 <- 1/(1-phi_1^2) x_0 <- rnorm(1)*sqrt(gamma_0) x <- filter(rnorm(140), phi_1, method = "r", init = x_0) Example Consider a stationary AR(1) process. We may write n 1 X i = φ 1 X i 1 +W i = = φ n 1X i n + φ j 1W i j. 29

2 Simulated values ACF Figure 23: Simulation of AR(1) process in Example phi_1 = 0.9 phi_1 = phi_1 = 0.5 phi_1 = Figure 24: Autocorrelations of AR(1) with different parameters. 30

3 Define the causal linear process Y i = φj 1W i j, then we may write (detailed proof not examinable) ( E Xi Y i 2) ( 1/2 ) = E φ n 1X i n φ j 2 1/2 1W i j j=n φ 1 n( ) EXi n 2 1/2 + φ 1 j( ) EWi j 2 1/2 = φ 1 n (σ X + j=n σ ) n 0, 1 φ 1 where σ 2 X = EX2 1. This implies X i = Y i (almost surely). We may write the autoregressive process also in terms of the backshift operator, as p X i φ j B j X i = W i, (4) or φ(b)x i = W i, where j=1 Definition 4.16 (Characteristic polynomial of AR(p)). φ(z) := 1 p φ j z j. Remark Note the minus sign in the AR polynomial, contrary to the plus in the MA polynomial. In some contexts (esp. signal processing), the AR coefficients are often defined φ i = φ i, so that the AR polynomial will look exactly like the MA polynomial. Theorem The (stationary) AR(p) process exists and can be written as a causal linear process if and only if j=1 φ(z) 0 for all z C with z 1, that is, the roots of the complex polynomial φ(z) lie strictly outside the unit disc. For full proof, see for example Theorem of Brockwell and Davis. However, to get the idea, we may write informally X i = φ(b) 1 W i, and we may write the reciprocal of the characteristic function as 1 φ(z) = c j z j, for z 1+ǫ, 31

4 This means that we may write the AR(p) as a causal linear process X i = c j W i j, where the coefficients satisfy 10 c j K(1+ǫ/2) j. Remark ThisjustifiesviewingAR(p)asa MA( ) withcoefficients(c j ) j 1. This also implies that we may apporximate AR(p) with arbitrary precision by MA(q) with large enough q. 4.3 Invertibility of MA Example Let θ 1 (0,1) and σ 2 > 0 be some parameters, and consider two MA(1) models, X i = W i +θ 1 W i 1, X i = W i + θ 1 Wi 1, where θ 1 = 1/θ 1 and σ 2 = σ 2 θ 2 1. We have What do you observe? γ 0 = σ 2 (1+θ 2 1), γ 1 = σ 2 θ 1 γ 0 = σ 2 (1+ θ 2 1) γ 1 = σ 2 θ 1. (W n ) i.i.d. N(0,σ 2 ) ( W n ) i.i.d. N(0, σ 2 ), It turns out that the following invertibility condition resolves the MA(q) identifiability problem, and therefore it is standard that the roots of the characteristic polynomial are assumed to lie outside the unit disc. Theorem If the roots of the characteristic polynomial of MA(q) are strictly outside the unit circle, the MA(q) is invertible in the sense that it satisfies W i = β j X i j, where the constants satisfy β 0 = 1 and β j K(1 + ǫ) j for some constants K < and ǫ > 0. that As with Theorem 4.18, we may write symbolically, from X i = θ(b)w i, W i = 1 θ(b) X i = β j X i j, where the constants β j are uniquely determined by 1/θ(z) = β jz j, as the roots of θ(z) lie outside the unit disc. 10. Because c j (1+ǫ/2) j 0 as j. 32

5 4.4 Autoregressive moving average (ARMA) Definition 4.22 (Autoregressive moving average ARMA(p,q) process). Suppose φ 1,...,φ p R are coefficients of a (stationary) AR(p) process and θ 1,...,θ q R, and(w i ) WN(σ 2 ).The(stationary)ARMA(p,q)processwiththeseparameters is a process satisfying X i = p q φ j X i j + θ j W i j, (5) j=1 with the convention θ 0 = 1 and where the first sum vanishes if p = 0. Remark AR(p) is ARMA(p,0) and MA(q) is ARMA(0,q). We may write ARMA(p,q) briefly with the characteristic polynomials of the AR and MA and the backshift operator as φ(b)x i = θ(b)w i. Simulation of a general ARMA(p,q) model is not straightforward exactly, but we can approximately simulate it by setting X p+1 = = X 0 = 0 (say) and then following (5). Then, X b,x b+1,...,x b+n is an approximate sample of a stationary ARMA(p,q) if b is large enough. This is what R function arima.sim does; the parameter n.start is b above. Example Simulation of ARMA(2,1) model with φ 1 = 0.3, φ 2 = 0.4, θ 1 = 0.8. x <- arima.sim(list(ma = c(-0.8), ar=c(.3,-.4)), 140, n.start = 1e5) This is the same as q <- 2; n <- 140; n.start <- 1e5 z <- filter(rnorm(n.start+n), c(1, -0.8), sides=1) z <- tail(z, n.start+n-q) x <- tail(filter(z, c(.3,-.4), method="r"), n) (The latter may sometimes be necessary, because arima.sim checks the stability of the AR part by calculating the roots of φ(z) numerically, which is notoriously unstable if the order of φ is large. Sometimes arima.sim refuses to simulate a stable ARMA...) Remark If the characteristic polynomials θ(z) and φ(z) of an ARMA(p,q) share a (complex) root, say x 1 = y 1, then θ(z) φ(z) = (z x 1)(z x 2 ) (z x q ) (z y 1 )(z y 2 ) (z y p ) 33

6 Simulateed values ACF Figure 25: Simulation of ARMA(2,1) in Example = (z x 2) (z x q ) (z y 2 ) (z y p ) = θ(z) φ(z), where θ(z) is of order q 1 and φ(z) is of order p 1, and it turns out that φ(b)x i = θ(b)w i, which means that the model reduces to ARMA(p 1,q 1). Condition 4.26 (Regularity conditions for ARMA). In what follows, we shall assume the following: (a) The roots of the AR characteristic polynomial are strictly outside the unit disc (cf Theorem 4.18). (b) The roots of the MA characteristic polynomial are strictly outside the unit disc (cf. Theorem 4.21). (c) The AR and MA characteristic polynomials do not have common roots (cf. Remark 4.25). 34

7 Theorem A stationary ARMA(p,q) model satisfying Condition 4.26 exists, is invertible and can be written as a causal linear process X i = ξ j W i j, W i = β j X i j, where the constants ξ j and β j satisfy ξ j z j = θ(z) φ(z) and β j z j = φ(z) θ(z). In addition, β 0 = 1 and there exist constants K < and ǫ > 0 such that max{ ξ j, β j } K(1+ǫ) j for all j 0. Remark In fact, the coefficients ξ j (or β j ) related to any ARMA(p,q) can be calculated numerically from the parameters easily. Also the autocovariance can be calculated numerically up to any lag in a straightforward way; cf. Brockwell and Davis p In R, the autocorrelation coefficients can be calculated with ARMAacf. 4.5 Integrated models Autoregressive moving average models are pretty flexible models for stationary series. However, in many practical time series, it might be more useful to consider the differenced series (Definition 2.10). This brings us to the general notion of Definition 4.29 (Difference operator). Suppose (X i ) is a stochastic process. Its d:th order difference process is defined as ( d X i ), where the d:th order difference operator may be written in terms of the backshift operator as d = (1 B) d for d 1. Definition 4.30 (Autoregressive integrated moving average ARIMA(p,d,q) process). If the d-th difference of the process ( d X i ) follows ARMA(p,q), then we say (X i ) is ARIMA(p,d,q). Remark Suppose that ( d X i ) is a stationary ARMA(p, q). (i) The ARIMA(p,d,q) process (X i ) is not unique (why?). (ii) The ARIMA(p,d,q) process (X i ) is not, in general, stationary. The process (X i ) (or the data x 1,...,x n ) is said to be difference stationary. Example Simple random walk is an ARIMA(0,1,0). X i = X i 1 +W i 35