Week 2 Flashcards

1
Q

AR(p)

A

Autoregressive Model of order p
Stochastic process of below form
XT is stationary Φ1,…,Φp are real constants and Φp != 0

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2
Q

Backshift operator

A

Used to make writing down AR model cleaner

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3
Q

Stationarity from associated polynomial of AR(p)

A

If all roots are larger in size than 1 then model is stationary, otherwise non stationary

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4
Q

MA(q)
Backshift Operayor
Associated polynomial

A

Moving Average model of order q

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5
Q

MA(q) models are always

A

Stationary

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6
Q

MA(1)

A
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7
Q

MA(2)

A
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8
Q

For AR(1) if |Φ1 | < 1

A

Stationary solution of AR(1) model

‘we know that’ through an application of the geometric series to the operator

therefore we can rewrite as a linear model

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9
Q

Autocovariance of stationary solution of AR(1) model

A
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10
Q

for AR(1), if Φ1 > 0 ?

A

Expo decrease

If < 0 this => oscillatory decrease

As γ(h) = Φ 1h

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11
Q

What are Yule Walker equations

A

Set of linear equations formed from γ(h) and ρ(h)
Used to estimate parameters of AR model

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12
Q

Using Yule Walker equations

A
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13
Q

ARMA(1, 1)

A
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14
Q

Causality and invertibility of ARMA(p, q)

A
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15
Q

Check causality of AR(1)

A

If φ(z) != 0 for |z| <= 1
We must have

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16
Q

AR(2) model is causal if

A
17
Q

Rewrite 2nd order polynomial

A
18
Q

Parameter redundancy

A
19
Q

For AR(1) ?

A
20
Q

Result of Yule Walker Estimators for AR(2)

A
21
Q

Method of moments for MA(1)

A
22
Q

Important equality for YW equations

A

ρ(0)=1

ρ(h) = ρ(-h)

23
Q

Whenevr asked to show that something equals something else

A

State definitions as this will often reveal method

24
Q

Conditions for ARMA model to be stationary

A

Roots of characteristic polynomial for AR part given by φ must be outside unit circle

MA part is always stationary