Dates for term tests

1 / 72

# Dates for term tests - PowerPoint PPT Presentation

Dates for term tests. Friday, February 5 Friday, March 5 Friday, March 26. Let { x t | t  T } be defined by the equation. The Moving Average Time series of order q, MA(q). where { u t | t  T } denote a white noise time series with variance s 2.

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

## PowerPoint Slideshow about ' Dates for term tests' - lillian-kerr

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Dates for term tests
• Friday, February 5
• Friday, March 5
• Friday, March 26

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

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

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))

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

Comment

The autocorrelation function for an MA(q) time series

“cuts off” to zero after lag q.

q

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

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

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))

The mean value of a stationary AR(p) series

The Autocovariance function s(h) of a stationary AR(p) series

Satisfies the equations:

Satisfies the equations:

with

for h > p

and

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).

### Conditions for stationarity

Autoregressive Time series of order p, AR(p)

If b1 = 1 and d = 0.

The value of xt increases in magnitude and ut eventually becomes negligible.

The time series {xt|t T} satisfies the equation:

The time series {xt|t T} exhibits deterministic behaviour.

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.

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.

Special Cases: The AR(1) time

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

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.

Special Cases: The AR(2) time

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

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.

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

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

b2

The MixedAutoregressive Moving Average

Time Series of order p,q

The ARMA(p,q) series

Let b1, b2, … bp , a1, a2, … ap , d denote p + q +1 numbers (parameters).

The MixedAutoregressive Moving Average Time Series of order p, ARMA(p,q)

• 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.

### Mean value, variance, autocovariance function, autocorrelation function of anARMA(p,q) series

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 - … - bpxp

satisfy | ri| > 1 for all i.

Let m = E(xt). Then

or

The Autocovariance function, s(h), of a stationary mixed autoregressive-moving average time series{xt|t T} be determined by the equation:

Thus

The autocovariance function s(h) satisfies:

For h = 0, 1. … , q:

for h > q:

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.

For h = 0, 1:

or

for h > 1:

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

or

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

### The Backshift Operator B

Consider the time series {xt : tT} and Let Mdenote 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).

Mis a vector space

Let B be an operator on M defined by:

Bxt = xt-1.

B is called the backshift operator.

Note:
• We can also define the operator Bk with

Bkxt = B(B(...Bxt)) = xt-k.

• The polynomial operator

p(B) = c0I + c1B + c2B2 + ... + ckBk

can also be defined by the equation.

p(B)xt = (c0I + c1B + c2B2 + ... + ckBk)xt .

= c0Ixt + c1Bxt + c2B2xt + ... + ckBkxt

= c0xt + c1xt-1 + c2xt-2 + ... + ckxt-k

The power series operator

p(B) = c0I + c1B + c2B2 + ...

can also be defined by the equation.

p(B)xt= (c0I + c1B + c2B2 + ... )xt

= c0Ixt + c1Bxt + c2B2xt + ...

= c0xt + c1xt-1 + c2xt-2 + ...

• If p(B) = c0I + c1B + c2B2 + ... and q(B) = b0I + b1B + b2B2 + ... 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.

Other operators closely related to B:
• F = B-1 ,the forward shift operator, defined by Fxt = B-1xt = xt+1and
• D = I - B ,the first difference operator, defined by Dxt = (I - B)xt = xt - xt-1 .
The Equation for a MA(q) time series

xt= a0ut + a1ut-1 +a2ut-2 +... +aqut-q+ m

can be written

xt= a(B)ut + m

where

a(B)= a0I + a1B +a2B2 +... +aqBq

The Equation for a AR(p) time series

xt= b1xt-1 +b2xt-2 +... +bpxt-p+ d +ut

can be written

b(B)xt= d + ut

where

b(B)= I - b1B - b2B2 -... - bpBp

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

xt= b1xt-1 +b2xt-2 +... +bpxt-p+ d +ut + a1ut-1 +a2ut-2 +... +aqut-q

can be written

b(B)xt= a(B)ut + m

where

a(B)= a0I + a1B +a2B2 +... +aqBq

and

b(B)= I - b1B - b2B2 -... - bpBp

• 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;

### The partial autocorrelation function

A useful tool in time series analysis

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) + b2 + ... +bprx(p-2)

...

rx(p) = b1rx(p-1)+ b2rx(p-2) + ... +bp.

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.

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:

Comment:

The partial auto correlation function, Fkk is determined from the auto correlation function, r(h)

• 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

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

Let

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

and

The matrix A reverses order

The equations may be written

Multiplying the first equations by

or

Hence

and

or

### Some Examples

Example 1: MA(1) time series

Suppose that {xt|t  T} satisfies the following equation:

xt = 12.0 + ut + 0.5 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.
Solution

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

xt = 12.0 + ut + 0.5 ut – 1

Thus:

• The mean of the series,

m= 12.0

The autocovariance function for an MA(1) is

Thus:
• The variance of the series,

s(0)= 1.5125

and

• The autocorrelation function is:
Example 2: AR(2) time series

Suppose that {xt|t  T} satisfies the following equation:

xt = 0.4 xt – 1+ 0.1 xt – 2+ 1.2 + 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.
The mean of the series
• The autocorrelation function.
• Satisfies the Yule Walker equations
the variance of the series
• The partial autocorrelation function.
Example 3: ARMA(1, 2) time series

Suppose that {xt|t  T} satisfies the following equation:

xt = 0.4 xt – 1+ 3.2 + ut + 0.3 ut – 1 + 0.2 ut – 1

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.