# Time series analysis. Jan Grandell

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1 Time series analysis Jan Grandell

2 2

3 Preface The course Time series analysis is based on the book [7] and replaces our previous course Stationary stochastic processes which was based on [6]. The books, and by that the courses, differ in many respects, the most obvious is that [7] is more applied that [6]. The word applied is partly a fine word for elementary. A very important part of the course consists in looking at and thinking through the examples in [7] and to play around with the enclosed computer package ITSM or with Matlab. Jan Enger has constructed a number of M-files in order to facilitate use of Matlab within this course. Those who want to use Matlab later in connection with time series can use the toolbox System Identification by Lennart Ljung, which contains an extensive library of stochastic models related to time series and control theory. The main reason for the change in the courses is that half of our intermediate course Probability theory treats stationary processes from a theoretical point of view. A second reason is that a course in time series analysis is useful also for students more interested in applications than in the underlying theory. There are many references to [6] in [7] and the best recommendation to give a student interested in the subject also from a more theoretical point of view is to buy both books. However, due to the price of books, this recommendation might be unrealistic. A cheaper recommendation to those students is to read this lecture notes, where many parts from our previous course, i.e. in reality from [6], are included. These parts are given in the Appendix and in inserted paragraphs. The inserted paragraphs are written in this style. I am most grateful for all kind of criticism, from serious mathematical mistakes to trivial misprints and language errors. Jan Grandell i

4 ii PREFACE

5 Contents Preface i Lecture Introduction Stationarity Trends and Seasonal Components No Seasonal Component Trend and Seasonality Lecture The autocovariance of a stationary time series Strict stationarity The spectral density Time series models Lecture Estimation of the mean and the autocovariance Estimation of µ Estimation of γ( ) and ρ( ) Prediction A short course in inference Prediction of random variables Lecture Prediction Prediction of random variables Prediction for stationary time series Lecture The Wold decomposition Partial correlation Partial autocorrelation ARMA processes Calculation of the ACVF Prediction of an ARMA Process iii

6 iv CONTENTS Lecture Spectral analysis The spectral distribution Spectral representation of a time series Prediction in the frequency domain Interpolation and detection The Itô integral Lecture Estimation of the spectral density The periodogram Smoothing the periodogram Linear filters ARMA processes Lecture Estimation for ARMA models Yule-Walker estimation Burg s algorithm The innovations algorithm The Hannan Rissanen algorithm Maximum Likelihood and Least Square estimation Order selection Lecture Unit roots Multivariate time series Lecture Financial time series ARCH processes GARCH processes Further extensions of the ARCH process Literature about financial time series Lecture Kalman filtering State-Space representations Prediction of multivariate random variables The Kalman recursions Appendix 111 A.1 Stochastic processes A.2 Hilbert spaces References 119

7 CONTENTS v Index 120

8 vi CONTENTS

9 Lecture Introduction A time series is a set of observations x t, each one being recorded at a specific time t. Definition 1.1 A time series model for the observed data {x t } is a specification of the joint distributions (or possibly only the means and covariances) of a sequence of random variables {X t } of which {x t } is postulated to be a realization. In reality we can only observe the time series at a finite number of times, and in that case the underlying sequence of random variables (X 1, X 2,..., X n ) is just a an n-dimensional random variable (or random vector). Often, however, it is convenient to allow the number of observations to be infinite. In that case {X t, t = 1, 2,...} is called a stochastic process. In order to specify its statistical properties we then need to consider all n-dimensional distributions P [X 1 x 1,..., X n x n ] for all n = 1, 2,..., cf. Section A.1 on page 111 for details. Example 1.1 (A binary process) A very simple example of a stochastic process is the binary process {X t, t = 1, 2,... } of independent random variables with P (X t = 1) = P (X t = 1) = 1 2. In this case we have P (X 1 = i 1, X 2 = i 2,..., X n = i n ) = 2 n where i k = 1 or 1. In Example A.2 on page 113 it is shown that the binary process is well-defined. Definition 1.2 (IID noise) A process {X t, t Z} is said to be an IID noise with mean 0 and variance σ 2, written {X t } IID(0, σ 2 ), if the random variables X t EX t = 0 and Var(X t ) = σ 2. are independent and identically distributed with 1

10 2 LECTURE 1 The binary process is obviously an IID(0, 1)-noise. In most situations to be considered in this course, we will not need the full specification of the underlying stochastic process. The methods will generally rely only on its means and covariances and sometimes on some more or less general assumptions. Consider a stochastic process {X t, t T }, where T is called the index or parameter set. Important examples of index sets are Z = {0, ±1, ±2,... }, {0, 1, 2,... }, (, ) and [0, ). A stochastic process with T Z is often called a time series. Definition 1.3 Let {X t, t T } be a stochastic process with Var(X t ) <. The mean function of {X t } is The covariance function of {X t } is µ X (t) def = E(X t ), t T. γ X (r, s) = Cov(X r, X s ), r, s T. Example 1.2 (Standard Brownian motion) A standard Brownian motion, or a standard Wiener process, {B(t), t 0} is a stochastic process with B(0) = 0, independent increments, and B(t) B(s) N(0, t s) for t s, see Definition A.5 on page 114 for details. The notation N(0, t s) means, contrary to the notation used in [1], that the variance is t s. We have, for r s γ B (r, s) = Cov(B(r), B(s)) = Cov(B(r), B(s) B(r) + B(r)) = Cov(B(r), B(s) B(r)) + Cov(B(r), B(r)) = 0 + r = r and thus, if nothing is said about the relation between r and s γ B (r, s) = min(r, s). 1.2 Stationarity Loosely speaking, a stochastic process is stationary, if its statistical properties do not change with time. Since, as mentioned, we will generally rely only on properties defined by the means and covariances, we are led to the following definition. Definition 1.4 The time series {X t, t Z} is said to be (weakly) stationary if (i) Var(X t ) < for all t Z, (ii) µ X (t) = µ for all t Z, (iii) γ X (r, s) = γ X (r + t, s + t) for all r, s, t Z.

11 1.3. TRENDS AND SEASONAL COMPONENTS 3 (iii) implies that γ X (r, s) is a function of r s, and it is convenient to define γ X (h) def = γ X (h, 0). The value h is referred to as the lag. Definition 1.5 Let {X t, t Z} be a stationary time series. The autocovariance function (ACVF) of {X t } is γ X (h) = Cov(X t+h, X t ). The autocorrelation function (ACF) is ρ X (h) def = γ X(h) γ X (0). A simple example of a stationary process is the white noise, which may be looked a upon as the correspondence to the IID noise when only the means and the covariances are taken into account. Definition 1.6 (White noise) A process {X t, t Z} is said to be a white noise with mean µ and variance σ 2, written if EX t = µ and γ(h) = {X t } WN(µ, σ 2 ), { σ 2 if h = 0, 0 if h 0. Warning: In some literature white noise means IID. 1.3 Trends and Seasonal Components Consider the classical decomposition model where X t = m t + s t + Y t, m t is a slowly changing function (the trend component ); s t is a function with known period d (the seasonal component ); Y t is a stationary time series. Our aim is to estimate and extract the deterministic components m t and s t in hope that the residual component Y t will turn out to be a stationary time series.

12 4 LECTURE No Seasonal Component Assume that X t = m t + Y t, where, without loss of generality, EY t = 0. Method 1 (Least Squares estimation of m t ) t = 1,..., n If we assume that m t = a 0 + a 1 t + a 2 t 2 we choose â k to minimize n (x t a 0 a 1 t a 2 t 2 ) 2. t=1 Method 2 (Smoothing by means of a moving average) Let q be a non-negative integer and consider W t = 1 q X t+j, q + 1 t n q. 2q + 1 Then provided W t = 1 2q + 1 j= q q j= q m t+j + 1 2q + 1 q Y t+j m t, j= q q is so small that m t is approximately linear over [t q, t + q] and q is so large that 1 2q+1 q j= q Y t+j 0. For t q and t > n q some modification is necessary, e.g. and n t m t = α(1 α) j X t+j for t = 1,..., q j=0 t 1 m t = α(1 α) j X t j for t = n q + 1,..., n. j=0 The two requirements on q may be difficult to fulfill in the same time. Let us therefore consider a linear filter m t = a j X t+j, where a j = 1 and a j = a j. Such a filter will allow a linear trend to pass without distortion since aj (a + b(t + j)) = (a + bt) a j + b a j j = (a + bt) In the above example we have a j = { 1 2q+1 for j q, 0 for j > q.

13 1.3. TRENDS AND SEASONAL COMPONENTS 5 It is possible to choose the weights {a j } so that a larger class of trend functions pass without distortion. Such an example is the Spencer 15-point moving average where [a 0, a ±1,..., a ±7 ] = [74, 67, 46, 21, 3, 5, 6, 3] and a j = 0 for j > 7. Applied to m t = at 3 + bt 2 + ct + d we get m t = a j X t+j = a j m t+j + a j Y t+j a j m t+j = see problem 1.12 in [7] = m t. Method 3 (Differencing to generate stationarity) Define the difference operator by X t = X t X t 1 = (1 B)X t, where B is the backward shift operator, i.e. (BX) t = X t 1, and its powers As an example we get k X t = ( k 1 X) t. 2 X t = X t X t 1 = (X t X t 1 ) (X t 1 X t 2 ) = X t 2X t 1 + X t 2. As an illustration of the calculus of operators we give a different proof : 2 X t = (1 B) 2 X t = (1 2B + B 2 )X t = X t 2X t 1 + X t 2. If m t = a + bt we get and X t = m t + Y t = a + bt a b(t 1) + Y t = b + Y t Cov[ Y t, Y s ] = Cov[Y t, Y s ] Cov[Y t 1, Y s ] Cov[Y t, Y s 1 ] + Cov[Y t 1, Y s 1 ] = γ Y (t s) γ Y (t s 1) γ Y (t s + 1) + γ Y (t s) = 2γ Y (t s) γ Y (t s + 1) γ Y (t s 1). Thus X t is stationary. Generally, if m t = k j=0 c jt j we get k X t = k!c k + k Y t, which is stationary, cf. problem 1.10 in [7]. Thus is tempting to try to get stationarity by differencing. In practice often k = 1 or 2 is enough.

14 6 LECTURE Trend and Seasonality Let us go back to X t = m t + s t + Y t, where EY t = 0, s t+d = s t and d k=1 s k = 0. For simplicity we assume that n/d is an integer. Typical values of d are: 24 for period: day and time-unit: hours; 7 for period: week and time-unit: days; 12 for period: year and time-unit: months. Sometimes it is convenient to index the data by period and time-unit x j,k = x k+d(j 1), k = 1,..., d, j = 1,..., n d, i.e. x j,k is the observation at the k:th time-unit of the j:th period. Method S1 (Small trends) It is natural to regard the trend as constant during each period, which means that we consider the model Natural estimates are X j,k = m j + s k + Y j,k. m j = 1 d d x j,k and ŝ k = d n/d (x j,k m j ). n k=1 j=1 Method S2 (Moving average estimation) First we apply a moving average in order to eliminate the seasonal variation and to dampen the noise. For d even we use q = d/2 and the estimate m t = 0.5x t q + x t q x t+q x t+q d and for d odd we use q = (d 1)/2 and the estimate provided q + 1 t n q. m t = x t q + x t q x t+q 1 + x t+q d In order to estimate s k we first form the natural estimates w k = 1 number of summands q k d <j n q k d (x k+jd m k+jd ).

15 1.3. TRENDS AND SEASONAL COMPONENTS 7 (Note that it is only the end-effects that force us to this formally complicated estimate. What we really are doing is to take the average of those x k+jd :s where the m k+jd :s can be estimated.) In order to achieve d k=1 ŝk = 0 we form the estimates ŝ k = w k 1 d d w i, k = 1,..., d. i=1 Method S3 (Differencing at lag d) Define the lag-d difference operator d by d X t = X t X t d = (1 B d )X t. Then d X t = d m t + d Y t which has no seasonal component, and methods 1 3 above can be applied.

16 8 LECTURE 1

17 Lecture The autocovariance of a stationary time series Recall from Definition 1.4 on page 2 that a time series {X t, t Z} is (weakly) stationary if (i) Var(X t ) < for all t Z, (ii) µ X (t) = µ for all t Z, (iii) γ X (t, s) = γ(t s) for all s, t Z, where γ(h) is the autocovariance function (ACVF). Notice that we have suppressed the dependence on X in the ACVF, which we will do when there is no risk for misunderstandings. It is more or less obvious that γ(0) 0, γ(h) γ(0) for all h Z, γ(h) = γ( h) for all h Z. We shall now give a characterization of the autocovariance function. Definition 2.1 A function κ : Z R is said to be non-negative definite, or positive semi-definite, if n a i κ(t i t j )a j 0 i,j=1 for all n and all vectors a R n and t Z n. Theorem 2.1 A real-valued even function defined on Z is non-negative definite if and only if it is the autocovariance function of a stationary time series. Proof of the if part: Let γ( ) be the autocovariance function of a stationary time series X t. It is also the autocovariance function of Z t = X t m X. Then γ(t 1 t 1 )... γ(t 1 t n ) Γ n =. is the covariance matrix of Z t = γ(t n t 1 )... γ(t n t n ) 9 Z t1. Z tn

18 10 LECTURE 2 and we have n 0 Var(a Z t ) = a E(Z t Z t)a = a Γ n a = a i γ(t i t j )a j. i,j=1 Before we consider the only if if part, we need some supplementary results. Definition 2.2 A n n matrix Σ is non-negative definite if b Σb 0 for all b R n. Lemma 2.1 A symmetric non-negative definite n n matrix Σ has non-negative eigenvalues λ 1,..., λ n. Proof of Lemma 2.1: Let e k be an eigenvector corresponding to the eigenvalue λ k. Since Σ is symmetric e k is both a left and a right eigenvector. Then we have Σe k = λ k e k 0 e kσe k = λ k e ke k λ k 0. Lemma 2.2 A symmetric non-negative definite n n matrix Σ has the representation Σ = BB, where B is an n n matrix. Proof: Let e k and λ k be the eigenvectors and the eigenvalues of Σ. It is well known that {e k } can be chosen orthogonal, i.e. such that e k e j = 0 for k j. Put p k = e k e k e k and P = (p 1,..., p n ) which implies that P is orthonormal, i.e. P 1 = P. Then, for Λ = diag(λ 1,..., λ n ) ΣP = P Λ ΣP P = P ΛP Σ = P ΛP. The matrices B = P Λ 1/2 or B = P Λ 1/2 P, where Λ 1/2 = diag(λ 1/2 1,..., λ 1/2 n ), work. The matrix P Λ 1/2 P is the natural definition of Σ 1/2 since it is non-negative definite and symmetric. Thus, for any µ and any symmetric non-negative definite n n matrix Σ there exists a unique normal distribution with mean µ and covariance matrix Σ. Proof of the only if part of Theorem 2.1: Let κ : Z R be an even non-negative definite function. For each n and t Z n κ(t 1 t 1 )... κ(t 1 t n ) K t =. κ(t n t 1 )... κ(t n t n ) is the covariance matrix for a normal variable with, say, mean 0. Thus and it is easy to check that φ t (u) = exp( 1 2 u K t u) lim φ t(u) = φ t(i) (u(i)) = exp( 1 u i 0 2 u(i) K t(i) u(i)) holds. Recall that t(i) = (t 1,..., t i 1, t i+1,..., t n ) and u(i) = (u 1,..., u i 1, u i+1,..., u n ). Thus the theorem follows from Kolmogorov s existence theorem.

19 2.1. THE AUTOCOVARIANCE OF A STATIONARY TIME SERIES Strict stationarity Definition 2.3 The time series {X t, t Z} is said to be strictly stationary if the distributions of (X t1,..., X tk ) and (X t1 +h,..., X tk +h) are the same for all k, and all t 1,..., t k, h Z. Let B be the backward shift operator, i.e. (BX) t = X t 1. In the obvious way we define powers of B; (B j X) t = X t j. Then strict stationarity means that B h X has the same distribution for all h Z. A strictly stationary time series {X t, t Z} with Var(X t ) < is stationary. A stationary time series {X t, t Z} does not need to be strictly stationary: {X t } is a sequence of independent variables and { Exp(1) if t is odd, X t N(1, 1) if t is even. Thus {X t } is WN(1, 1) but not IID(1, 1). Definition 2.4 (Gaussian time series) The time series {X t, t Z} is said to be a Gaussian time series if all finite-dimensional distributions are normal. A stationary Gaussian time series {X t, t Z} is strictly stationary, since the normal distribution is determined by its mean and its covariance The spectral density Suppose that h= γ(h) <. The function f given by f(λ) = 1 2π is well-defined. We have π π h= e ihλ f(λ) dλ = = 1 2π k= e ihλ γ(h), π λ π, (2.1) π π 1 2π k= e i(h k)λ γ(k) dλ π γ(k) e i(h k)λ dλ = γ(h). π The function f defined by (2.1) on this page is called the spectral density of the time series {X t, t Z}. The spectral density will turn out to be a very useful notion, to which we will return. Here we only notice that f(0) = 1 2π h= γ(h).

20 12 LECTURE Time series models The simplest time series model is certainly the white noise. A first generalization of the white noise is the moving average. Definition 2.5 (The MA(q) process) The process {X t, t Z} is said to be a moving average of order q if X t = Z t + θ 1 Z t θ q Z t q, {Z t } WN(0, σ 2 ), (2.2) where θ 1,..., θ q are constants. We will now extend MA(q) processes to linear processes. Definition 2.6 (Linear processes) The process {X t, t Z} is said to be a linear process if it has the representation X t = where j= ψ j <. j= ψ j Z t j, {Z t } WN(0, σ 2 ), (2.3) Warning: In some literature a linear process means that {Z t, t Z} is a sequence of independent and identically distributed variables, and not only WN, cf. Definition 3.3 on page 20. Several questions come naturally in connection with the representation (2.3) on this page, like: How to interpret an infinite sum of random variables? Is X t well-defined? In order to answer these questions we need some facts about convergence of random variables, for which we refer to Section A.2 on page 115. Here we only m.s. notice that X n X means that E(X n X) 2 0 as n. m.s. stands for mean-square convergence and the notion requires that X, X 1, X 2,... have finite second moment. An important property of meansquare convergence is that Cauchy-sequences do converge. More precisely this means that if X 1, X 2,... have finite second moment and if E(X n X k ) 2 0 as n, k, then there exists a random variable X with finite second moment such that m.s. X. Another way to express this is: X n The space of square integrable random variables is complete under mean-square convergence. The existence of the sum in (2.3) on this page is no problem, due to the following lemma.

21 2.2. TIME SERIES MODELS 13 Lemma 2.3 If {X t } is any sequence of random variables such that sup t E X t <, and if j= ψ j <, then the series ψ j X t j, j= converges absolutely with probability one. If in addition sup t E X t 2 < the series converges in mean-square to the same limit. Proof of mean-square convergence: Assume that sup t E X t 2 <. Then 2 E ψ j X t j = ψ j ψ k E(X t j X t k ) m< j <n sup t Completeness does the rest! E X t 2 ( m< j <n m< k <n m< j <n ψ j ) 2 0 as m, n. Theorem 2.2 A linear process {X t, t Z} with representation (2.3) on the preceding page is stationary with mean 0, autocovariance function and spectral density where ψ(z) = j= ψ jz j. γ X (h) = j= Proof: We have ( ) E(X t ) = E ψ j Z t j = ψ j ψ j+h σ 2, (2.4) f X (λ) = σ2 2π ψ(e iλ ) 2, (2.5) j= j= ψ j E(Z t j ) = 0 and [( γ X (h) = E(X t+h X t ) = E j= ψ j Z t+h j )( k= ψ k Z t k )] = j= k= = ψ j ψ k E(Z t+h j Z t k ) = j= k= ψ h+j ψ k E(Z k j Z 0 ) = and (2.4) follows since E(Z 0 Z 0 ) = σ 2. Using (2.1) on page 11 we get f X (λ) = 1 2π h= e ihλ γ h (h) = σ2 2π j= k= j= h= ψ j ψ k E(Z h+k j Z 0 ) ψ h+j ψ j E(Z 0 Z 0 ), e ihλ j= ψ j ψ j+h

22 14 LECTURE 2 = σ2 2π = σ2 2π h= j= j= k= e ihλ ψ j ψ j+h = σ2 2π h= j= e ijλ ψ j e i(j+h)λ ψ j+h e ijλ ψ j e ikλ ψ k = σ2 2π ψ(eiλ )ψ(e iλ ) = σ2 2π ψ(e iλ ) 2. Definition 2.7 (The ARMA(p, q) process) The process {X t, t Z} is said to be an ARMA(p, q) process if it is stationary and if X t φ 1 X t 1... φ p X t p = Z t + θ 1 Z t θ q Z t q, (2.6) where {Z t } WN(0, σ 2 ). We say that {X t } is an ARMA(p, q) process with mean µ if {X t µ} is an ARMA(p, q) process. Equations (2.6) can be written as where φ(b)x t = θ(b)z t, t Z, φ(z) = 1 φ 1 z... φ p z p, θ(z) = 1 + θ 1 z θ q z q, and B as usual is the backward shift operator, i.e. (B j X) t = X t j. The polynomials φ( ) and θ( ) are called generating polynomials. Warning: In some literature the generating polynomials are somewhat differently defined, which may imply that roots are differently placed in relation to the unit circle. If p = 0, i.e. φ(z) = 1 we have a MA(q) process. An even more important special case is when q = 0, i.e. when θ(z) = 1. Definition 2.8 (The AR(p) process) The process {X t, t Z} is said to be an AR(p) autoregressive process of order p if it is stationary and if X t φ 1 X t 1... φ p X t p = Z t, {Z t } WN(0, σ 2 ). (2.7) We say that {X t } is an AR(p) process with mean µ if {X t µ} is an AR(p) process. Definition 2.9 An ARMA(p, q) process defined by the equations φ(b)x t = θ(b)z t {Z t } WN(0, σ 2 ), is said to be causal if there exists constants {ψ j } such that j=0 ψ j < and X t = ψ j Z t j, t Z. (2.8) j=0

23 2.2. TIME SERIES MODELS 15 Another way to express the notion of causality is to require that Cov(X t, Z t+j ) = 0 for j = 1, 2,... (2.9) Causality is not a property of {X t } alone but rather of the relationship between {X t } and {Z t }. Theorem 2.3 Let {X t } be an ARMA(p, q) for which φ( ) and θ( ) have no common zeros. Then {X t } is causal if and only if φ(z) 0 for all z 1. The coefficients {ψ j } in (2.8) are determined by the relation ψ(z) = j=0 ψ j z j = θ(z), z 1. φ(z) Idea of proof: Assume φ(z) 0 if z 1. Then there exits ε > 0 such that 1 φ(z) = ξ j z j = ξ(z), z < 1 + ε. j=0 Due to this we may apply the operator ξ(b) to both sides of φ(b)x t = θ(b)z t, and we obtain X t = ξ(b)θ(b)z t. If φ(b)x t = θ(b)z t and if φ(z) = 0 for some z with z = 1 there exists no stationary solution. Example 2.1 (AR(1) process) Let {X t } be an AR(1) process, i.e. X t = Z t + φx t 1 or φ(z) = 1 φz. (2.10) Since 1 φz = 0 gives z = 1/φ it follows that X t is causal if φ < 1. In that case X t = Z t + φx t 1 = Z t + φ(z t 1 + φx t 2 ) = Z t + φz t 1 + φ 2 X t 2 =... = Z t + φz t 1 + φ 2 Z t 2 + φ 3 Z t Using Theorem 2.2 on page 13 we get immediately γ X (h) = j=0 φ 2j+ h σ 2 = σ2 φ h 1 φ 2. (2.11) We will show an alternative derivation of (2.11) which uses (2.9) on this page. We have and, for h > 0, γ X (0) = E(X 2 t ) = E[(Z t + φx t 1 ) 2 ] = σ 2 + φ 2 γ X (0) γ X (0) = γ X (h) = E(X t X t+h ) = E[X t (Z t+h + φx t+h 1 )] = φγ X (h 1). σ2 1 φ 2.

24 16 LECTURE 2 Thus (2.11) follows. If φ > 1 we can rewrite (2.10) on the previous page: φ 1 X t = φ 1 Z t + X t 1 or X t = φ 1 Z t+1 + φ 1 X t+1. Thus X t has the representation X t = φ 1 Z t+1 φ 2 Z t+2 φ 3 Z t If φ = 1 there exists no stationary solution. Definition 2.10 An ARMA(p, q) process defined by the equations φ(b)x t = θ(b)z t, {Z t } WN(0, σ 2 ), is said to be invertible if there exists constants {π j } such that j=0 π j < and Z t = π j X t j, t Z. (2.12) j=0 Theorem 2.4 Let {X t } be an ARMA(p, q) for which φ( ) and θ( ) have no common zeros. Then {X t } is invertible if and only if θ(z) 0 for all z 1. The coefficients {π j } in (2.12) are determined by the relation π(z) = j=0 π j z j = φ(z), z 1. θ(z) Example 2.2 (MA(1) process) Let {X t } be a MA(1) process, i.e. X t = Z t + θz t 1 or θ(z) = 1 + θz. Since 1 + θz = 0 gives z = 1/θ it follows that X t is invertible if θ < 1. In that case Z t = X t θz t 1 = X t θ(x t 1 θz t 2 ) =... = X t θx t 1 + θ 2 X t 2 θ 3 X t By (2.4) on page 13, with ψ 0 = 1, ψ 1 = φ and ψ j = 0 for j 0, 1, we get (1 + θ 2 )σ 2 if h = 0, γ(h) = θσ 2 if h = 1, 0 if h > 1. (2.13) Example 2.3 (ARMA(1, 1) process) Let {X t } be a ARMA(1, 1) process, i.e. X t φx t 1 = Z t + θz t 1 or φ(b)x t = θ(b)z t,

25 2.2. TIME SERIES MODELS 17 where φ(z) = 1 φz and θ(z) = 1 + θz. Let φ < 1 and θ < 1 so that X t is causal and invertible. Then we have X t = ψ(b)z t, where ψ(z) = θ(z) φ(z) = 1 + θz 1 φz = (1 + θz)φ j z j = 1 + By (2.4) on page 13 we get and, for h > 0, γ(h) = σ 2 γ(0) = σ 2 j=0 j=0 ψ 2 j = σ 2 (1 + ( ) = σ (φ + θ) 2 φ 2j j=0 = σ 2 φ h 1 (φ + θ + j=0 (φ + θ)φ j 1 z j. j=1 ) (φ + θ) 2 φ 2(j 1) j=1 = σ 2 ( 1 + ψ j ψ j+h = σ 2 ((φ + θ)φ h 1 + ) (φ + θ) 2 φ 2j j=0 ) (φ + θ)2 1 φ 2 ) (φ + θ) 2 φ 2(j 1)+h j=1 = σ 2 φ h 1 ( φ + θ + ) (φ + θ)2. 1 φ 2 Naturally we can use the ACVF above together with (2.1) on page 11 in order to find the spectral density. However, it is simpler to use (2.5) on page 13. Then we get f X (λ) = σ2 θ(e iλ ) 2 2π φ(e iλ ) = σ2 1 + θ e iλ 2 2 2π 1 φ e iλ 2 = σ2 2π 1 + θ 2 + 2θ cos(λ) 1 + φ 2 2φ cos(λ), π λ π. (2.14)

26 18 LECTURE 2

27 Lecture Estimation of the mean and the autocovariance Let {X t } be a stationary time series with mean µ, autocovariance function γ( ), and when it exists spectral density f( ). We will consider estimation of µ, γ( ) and ρ( ) = γ( )/γ(0) from observations of X 1, X 2,..., X n. However, we will consider this estimation in slightly more details than done in [7] and therefore we first give the following two definitions about asymptotic normality. Definition 3.1 Let Y 1, Y 2,... be a sequence of random variables. Y n AN(µ n, σn) 2 means that ( ) lim P Yn µ n x = Φ(x). n σ n Definition 3.1 is, despite the notation AN, exactly the same as used in the general course in connection with the central limit theorem. Definition 3.2 Let Y 1, Y 2,... be a sequence of random k-vectors. Y n AN(µ n, Σ n ) means that (a) Σ 1, Σ 2,... have no zero diagonal elements; (b) λ Y n AN(λ µ n, λ Σ n λ) for every λ R k such that λ Σ n λ > 0 for all sufficiently large n Estimation of µ Consider X n = 1 n n X j, j=1 which is a natural unbiased estimate of µ. Theorem 3.1 If {X t } is a stationary time series with mean µ and autocovariance function γ( ), then as n, Var(X n ) = E(X n µ) 2 0 if γ(n) 0, 19

28 20 LECTURE 3 and Proof: We have n Var(X n ) n Var(X n ) = 1 n If γ(n) 0 then 1 n h= γ(h) = 2πf(0) n Cov(X i, X j ) = i,j=1 h <n ( 1 h n h <n if h= γ(h) <. ( 1 h ) γ(h) γ(h). n h <n ) γ(h) 0 and thus Var(X n ) 0. If h= γ(h) < the result follows by dominated convergence, i.e. since max( 1 h ) n, 0 γ(h) γ(h) we have = lim n Var(X n) = lim n n = lim n h= lim max n h= ( ( max h <n 1 h n, 0 ( 1 h ) γ(h) n 1 h n, 0 ) γ(h) = ) γ(h) h= γ(h). Finally, the assumption h= γ(h) < implies h= γ(h) = 2πf(0). Definition 3.3 (Strictly linear time series) A stationary time series {X t } is called strictly linear if it has the representation X t = µ + ψ j Z t j, {Z t } IID(0, σ 2 ). j= Note: {Z t, t Z} is a sequence of independent and identically distributed variables, and not only WN as was the case for a linear process. Theorem 3.2 If {X t } is a strictly linear time series where j= ψ j < and j= ψ j 0, then ( X n AN µ, v ), n where v = h= γ(h) = σ2 ( j= ψ j) 2. It is natural to try to find a better estimate of µ than X n. If we restrict ourselves to linear unbiased estimates X n is, however, (almost) asymptotically effective. Strictly speaking, this means that if µ n is the best linear unbiased estimate and if the spectral density is piecewise continuous, then lim n n Var( µ n) = lim n n Var(X n ).

29 3.1. ESTIMATION OF THE MEAN AND THE AUTOCOVARIANCE Estimation of γ( ) and ρ( ) We will consider the estimates and γ(h) = 1 n h (X t X n )(X t+h X n ), 0 h n 1, n t=1 ρ(h) = γ(h) γ(0), respectively. The estimates are biased. The reason to use 1, and not 1 n the definition of γ(h), is that the matrix γ(0)... γ(n 1) Γ n =. γ(n 1)... γ(0) is non-negative definite. or 1 in n h n h 1 Theorem 3.3 If {X t } is a strictly linear time series where j= ψ j < and EZt 4 = ησ 4 <, then γ(0) γ(0). AN., n 1 V γ(h) γ(h), where V = (v ij ) i,j=0,...,h is the covariance matrix and v ij = (η 3)γ(i)γ(j) + {γ(k)γ(k i + j) + γ(k + j)γ(k i)}. k= Note: If {Z t, t Z} is Gaussian, then η = 3. Somewhat surprisingly, ρ( ) has nicer properties, in the sense that η disappears. Theorem 3.4 If {X t } is a strictly linear time series where j= ψ j < and EZt 4 <, then ρ(1) ρ(1). AN., n 1 W, ρ(h) ρ(h) where W = (w ij ) i,j=1,...,h is the covariance matrix and w ij = k= {ρ(k + i)ρ(k + j) + ρ(k i)ρ(k + j) + 2ρ(i)ρ(j)ρ 2 (k) 2ρ(i)ρ(k)ρ(k + j) 2ρ(j)ρ(k)ρ(k + i)}. (3.1)

30 22 LECTURE 3 The expression (3.1) is called Bartlett s formula. Simple algebra shows that w ij = {ρ(k + i) + ρ(k i) 2ρ(i)ρ(k)} k=1 {ρ(k + j) + ρ(k j) 2ρ(j)ρ(k)}, (3.2) which is more convenient for computational purposes. In the following theorem, the assumption EZt 4 < is relaxed at the expense of a slightly stronger assumption on the sequence {ψ j }. Theorem 3.5 If {X t } is a strictly linear time series where j= ψ j < and j= ψ2 j j <, then ρ(1) ρ(1). AN., n 1 W, ρ(h) ρ(h) where W is given by the previous theorem. Further, and this does not follow from the theorems, lim Corr( γ(i), γ(j)) = v n ij vii v jj and lim n Corr( ρ(i), ρ(j)) = w ij wii w jj. This implies that γ(0),..., γ(h) and ρ(1),..., ρ(h) may have a smooth appearance, which may give a false impression of the precision. 3.2 Prediction Prediction, or forecasting, means that we want to get knowledge of the outcome of some random variable by means of an observation of some other random variables. A typical and in this course the most important situation is that we have a stationary time series {X t, t Z} with known mean and ACVF, which we have observed for certain t-values, and that we want to say something about X t for some future time t. More precisely, assume that we are at time n, and have observed X 1,..., X n, and want to predict X n+h for some h > 0. For purely notational reasons it is sometimes better to say that we are standing at time 0, and to consider prediction of X h in terms of the observations X n+1,..., X 0. A very general formulation of the prediction problem is the following (too) general question: How to find a function X h ( ) of X n+1,..., X 0 which gives as good information as possible about X h?

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