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**Dates for term tests Friday, February 07 Friday, March 07**

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**The Moving Average Time series of order q, MA(q)**

Let {xt|t T} be defined by the equation. where {ut|t T} denote a white noise time series with variance s2. Then {xt|t T} is called a Moving Average time series of order q. (denoted by MA(q))

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**The mean value for an MA(q) time series**

The autocovariance function for an MA(q) time series The autocorrelation function for an MA(q) time series

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Comment The autocorrelation function for an MA(q) time series “cuts off” to zero after lag q. q

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**The Autoregressive Time series of order p, AR(p)**

Let {xt|t T} be defined by the equation. where {ut|t T} is a white noise time series with variance s2. Then {xt|t T} is called a Autoregressive time series of order p. (denoted by AR(p))

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**The mean value of a stationary AR(p) series**

The Autocovariance function s(h) of a stationary AR(p) series Satisfies the equations:

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**Satisfies the equations:**

The Autocorrelation function r(h) of a stationary AR(p) series Satisfies the equations: with for h > p and

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or: where r1, r2, … , rp are the roots of the polynomial and c1, c2, … , cp are determined by using the starting values of the sequence r(h).

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**Conditions for stationarity**

Autoregressive Time series of order p, AR(p)

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**For a AR(p) time series, consider the polynomial**

with roots r1, r2 , … , rp then {xt|t T} is stationary if |ri| > 1 for all i. If |ri| < 1 for at least one i then {xt|t T} exhibits deterministic behaviour. If |ri| ≥ 1 and |ri| = 1 for at least one i then {xt|t T} exhibits non-stationary random behaviour.

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since: and |r1 |>1, |r2 |>1, … , | rp | > 1 for a stationary AR(p) series then i.e. the autocorrelation function, r(h), of a stationary AR(p) series “tails off” to zero.

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**Special Cases: The AR(1) time**

Let {xt|t T} be defined by the equation.

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**Consider the polynomial**

with root r1= 1/b1 {xt|t T} is stationary if |r1| > 1 or |b1| < 1 . If |ri| < 1 or |b1| > 1 then {xt|t T} exhibits deterministic behaviour. If |ri| = 1 or |b1| = 1 then {xt|t T} exhibits non-stationary random behaviour.

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**Special Cases: The AR(2) time**

Let {xt|t T} be defined by the equation.

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**Consider the polynomial**

where r1 and r2 are the roots of b(x) {xt|t T} is stationary if |r1| > 1 and |r2| > 1 . This is true if b1+b2 < 1 , b2 –b1 < 1 and b2 > -1. These inequalities define a triangular region for b1 and b2. If |ri| < 1 or |b1| > 1 then {xt|t T} exhibits deterministic behaviour. If |ri| ≤ 1 for i = 1,2 and |ri| = 1 for at least on i then {xt|t T} exhibits non-stationary random behaviour.

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**Patterns of the ACF and PACF of AR(2) Time Series **

In the shaded region the roots of the AR operator are complex b2

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**The Mixed Autoregressive Moving Average**

Time Series of order p,q The ARMA(p,q) series

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**The Mixed Autoregressive Moving Average Time Series of order p, ARMA(p,q)**

Let b1, b2, … bp , a1, a2, … ap , d denote p + q +1 numbers (parameters). Let {ut|t T} denote a white noise time series with variance s2. independent mean 0, variance s2. Let {xt|t T} be defined by the equation. Then {xt|t T} is called a Mixed Autoregressive- Moving Average time series - ARMA(p,q) series.

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**Mean value, variance, autocovariance function, autocorrelation function of an ARMA(p,q) series**

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Similar to an AR(p) time series, for certain values of the parameters b1, …, bp an ARMA(p,q) time series may not be stationary. An ARMA(p,q) time series is stationary if the roots (r1, r2, … , rp ) of the polynomial b(x) = 1 – b1x – b2x2 - … - bp xp satisfy | ri| > 1 for all i.

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**Assume that the ARMA(p,q) time series {xt|t T} is stationary:**

Let m = E(xt). Then or

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**The Autocovariance function, s(h), of a stationary mixed autoregressive-moving average time series**

{xt|t T} be determined by the equation: Thus

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Hence

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We need to calculate:

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h sux(h) -1 -2 -3

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**The autocovariance function s(h) satisfies:**

For h = 0, 1. … , q: for h > q:

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**We then use the first (p + 1) equations to determine:**

s(0), s(1), s(2), … , s(p) We use the subsequent equations to determine: s(h) for h > p.

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**Example:The autocovariance function, s(h), for an ARMA(1,1) time series:**

For h = 0, 1: or for h > 1:

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**Substituting s(0) into the second equation we get:**

or Substituting s(1) into the first equation we get:

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for h > 1:

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**The Backshift Operator B**

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Consider the time series {xt : t T} and Let M denote the linear space spanned by the set of random variables {xt : t T} (i.e. all linear combinations of elements of {xt : t T} and their limits in mean square). M is a vector space Let B be an operator on M defined by: Bxt = xt-1. B is called the backshift operator.

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**Note: We can also define the operator Bk with**

Bkxt = B(B(...Bxt)) = xt-k. The polynomial operator p(B) = c0I + c1B + c2B ckBk can also be defined by the equation. p(B)xt = (c0I + c1B + c2B ckBk)xt . = c0Ixt + c1Bxt + c2B2xt ckBkxt = c0xt + c1xt-1 + c2xt ckxt-k

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**The power series operator p(B) = c0I + c1B + c2B2 + ... **

can also be defined by the equation. p(B)xt = (c0I + c1B + c2B )xt = c0Ixt + c1Bxt + c2B2xt + ... = c0xt + c1xt-1 + c2xt If p(B) = c0I + c1B + c2B and q(B) = b0I + b1B + b2B are such that p(B)q(B) = I i.e. p(B)q(B)xt = Ixt = xt than q(B) is denoted by [p(B)]-1.

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**Other operators closely related to B:**

F = B-1 ,the forward shift operator, defined by Fxt = B-1xt = xt+1 and D = I - B ,the first difference operator, defined by Dxt = (I - B)xt = xt - xt-1 .

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**The Equation for a MA(q) time series**

xt= a0ut + a1ut-1 +a2ut aqut-q + m can be written xt= a(B) ut + m where a(B) = a0I + a1B +a2B aqBq

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**The Equation for a AR(p) time series**

xt= b1xt-1 +b2xt bpxt-p + d + ut can be written b(B) xt= d + ut where b(B) = I - b1B - b2B bpBp

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**The Equation for a ARMA(p,q) time series**

xt= b1xt-1 +b2xt bpxt-p + d + ut + a1ut-1 +a2ut aqut-q can be written b(B) xt= a(B) ut + d where a(B) = a0I + a1B +a2B aqBq and b(B) = I - b1B - b2B bpBp

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**Some comments about the Backshift operator B**

It is a useful notational device, allowing us to write the equations for MA(q), AR(p) and ARMA(p, q) in a very compact form; It is also useful for making certain computations related to the time series described above;

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**The partial autocorrelation function**

A useful tool in time series analysis

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**The partial autocorrelation function**

Recall that the autocorrelation function of an AR(p) process satisfies the equation: rx(h) = b1rx(h-1) + b2rx(h-2) bprx(h-p) For 1 ≤ h ≤ p these equations (Yule-Walker) become: rx(1) = b1 + b2rx(1) bprx(p-1) rx(2) = b1rx(1) + b bprx(p-2) ... rx(p) = b1rx(p-1)+ b2rx(p-2) bp.

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In matrix notation: These equations can be used to find b1, b2, … , bp, if the time series is known to be AR(p) and the autocorrelation rx(h)function is known.

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If the time series is not autoregressive the equations can still be used to solve for b1, b2, … , bp, for any value of p ≥ 1. In this case are the values that minimizes the mean square error:

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**Definition: The partial auto correlation function at lag k is defined to be:**

Using Cramer’s Rule

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Comment: The partial auto correlation function, Fkk is determined from the auto correlation function, r(h) The partial auto correlation function at lag k, Fkk is the last auto-regressive parameter, if the series was assumed to be an AR(k) series. If the series is an AR(p) series then An AR(p) series is also an AR(k) series with k > p with the auto regressive parameters zero after p.

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Some more comments: The partial autocorrelation function at lag k, Fkk, can be interpreted as a corrected autocorrelation between xt and xt-k conditioning on the intervening variables xt-1, xt-2, ... ,xt-k+1 . If the time series is an AR(p) time series than Fkk = 0 for k > p If the time series is an MA(q) time series than rx(h) = 0 for h > q

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A General Recursive Formula for Autoregressive Parameters and the Partial Autocorrelation function (PACF)

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Let denote the autoregressive parameters of order k satisfying the Yule Walker equations:

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**Then it can be shown that:**

and

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Proof: The Yule Walker equations:

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In matrix form:

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The equations for

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and The matrix A reverses order

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**The equations may be written**

Multiplying the first equations by or

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**Substituting this into the second equation**

or and

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Hence and or

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Some Examples

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**Example 1: MA(1) time series**

Suppose that {xt|t T} satisfies the following equation: xt = ut ut – 1 where {ut|t T} is white noise with s = 1.1. Find: The mean of the series, The variance of the series, The autocorrelation function. The partial autocorrelation function.

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**Solution Now {xt|t T} satisfies the following equation:**

xt = ut ut – 1 Thus: The mean of the series, m = 12.0 The autocovariance function for an MA(1) is

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Thus: The variance of the series, s(0) = and The autocorrelation function is:

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**The partial auto correlation function at lag k is defined to be:**

Thus

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**Graph: Partial Autocorrelation function Fkk**

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**Exercise: Use the recursive method to calculate Fkk**

and

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**Exercise: Use the recursive method to calculate Fkk**

and

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**Example 2: AR(2) time series**

Suppose that {xt|t T} satisfies the following equation: xt = 0.4 xt – xt – ut where {ut|t T} is white noise with s = 2.1. Is the time series stationary? Find: The mean of the series, The variance of the series, The autocorrelation function. The partial autocorrelation function.

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The mean of the series The autocorrelation function. Satisfies the Yule Walker equations

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hence

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**the variance of the series**

The partial autocorrelation function.

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**The partial autocorrelation function of an AR(p) time series “cuts off” after p.**

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**Example 3: ARMA(1, 2) time series**

Suppose that {xt|t T} satisfies the following equation: xt = 0.4 xt – ut ut – ut – 2 where {ut|t T} is white noise with s = 1.6. Is the time series stationary? Find: The mean of the series, The variance of the series, The autocorrelation function. The partial autocorrelation function.

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xt = 0.4 xt – ut ut – ut – 1 white noise std. dev,. s = 1.6. Is the time series stationary? b(x) = 1 – b1x = 1 – 0.4x has root r1 =1/0.4 =2.5 Since |r1| > 1, the time series is stationary Find: The mean of the series.

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**The autocovariance function s(h) satisfies:**

For h = 0, 1, 2 for h > q: i.e. For h = 0, 1, 2 for h > q:

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etc. where

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**We use the first two equations to find s0 and s1**

Then we use the third equation to find s2

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**The autocovariance, autocorrelation functions**

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**Spectral Theory for a stationary time series**

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