Chapter 3.1 Flashcards

1
Q

Formula for k-th order covariance

A

γ_{k} = E[(y_{t} - E(y_{t}) (y_{t-k} - E(y_{t-k})]

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2
Q

Formula for k-th order autocorrelation

A

ρ^{k} = γ^{k} / γ^_{0}

ρ_{0} = 1, ρ_{-k} = ρ_{k}

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3
Q

What is the empirical autocorrelation function?

A

The set of all autocorrelations ρ^_{k} for k = 1,2,…

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4
Q

What do non-zero autocorrelations imply?

A

That y_{t-k} may be a useful variable to include in the regression

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5
Q

What are the 3 properties of a white noise series?

A
  1. E(ε_{t}) = 0
  2. E(ε_{t}^2) = σ^2
  3. E(ε_{s}ε_{t}) = 0

Residuals being a while noise process is a minimum requirement for a time series model to qualify as “adequate”

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6
Q

How to rewrite AR(1) model in terms of shocks?

A

y_{t} = ϕ_{1}^t y_{0} + Σ{i=0, t-1} ϕ_{1}^i ε_{t-i}

The effect of the shock is determined by ϕ_{1}^i

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7
Q

What does the shock have a transitory effect? What is a transitory effect?

A

When |ϕ_{1}| < 1, ϕ_{1}^i -> 0 as i increases

Effect of shock decays and dissapears

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8
Q

What does the shock have an explosive effect? What is an explosive effect?

A

When |ϕ_{1}| > 1, the effect of shocks ε_{t-i} on y_{t} increases with i

Effect of shock grows with time - typically exclude explosive case in TSA

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9
Q

What is the effect of the shock when ϕ_{1} = 1?

A

AR model simplifies to y_{t} = y_{0} + Σ{i=0, t-1} ε_{t-i}
Shock ε_{t-i} has same impact on all observations y_{t-i+h}
Shock is said to have permanent effects

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10
Q

Conditional mean formula

A

E(y_{t} | Υ_{t-1})

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11
Q

What is a necessary and sufficient condition (<=>) for stationarity in the AR(1) model?

A

|ϕ_{1}| < 1

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12
Q

What are the correlations between y and ε? (Important in derivations)

A
  • E(y_{t}ε_{t}) = σ^2
  • E(y_{t}ε_{t+j}) = 0 (current observation not correlated with future shocks)
  • E(y_{t}ε_{t-k}) ≠ 0 (current observation is correlated with past shocks)
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13
Q

What is a property of the unconditional variance in AR(1) models?

A

The unconditional variance is constant:

E[(y_{t} - E(y_{t})^2] = E[(y_{t-1} - E(y_{t-1})^2]

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14
Q

What is the conditional variance of an AR(1) model?

A

E(y_{t} | Y_{t-1}) = σ^2

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15
Q

What is the trend in the autocorrelations of an AR(1) model with |ϕ_{1}| < 1?

A

They decline exponentially towards zero

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16
Q

What is the autocovariance in the case of an AR(1) model with a unit root (ϕ=1)?

A
γ_{0,t} = E[y_{t}^2] = tσ^2
γ_{1,t} = E[y_{t} y{t-1}] = (t-1)σ^2

ρ_{1,t} = (t-1)/t, k>0

When t becomes large, all (theoretical) autocorrelations ρ_{k,t} = 1

17
Q

Limitations of the AR(1) model

A
  • If ϕ > 0, all correlations are positive and decline monotonically towards zero
  • If ϕ < 0, all even autocorrelations are positive, all odd autocorrelations are negative, and decline monotonically towards zero

Hence, the AR(1) model cannot capture EACF patterns

18
Q

Lag operator definition

A

L^k y_{t} = y_{t-k}

19
Q

How to write AR(p) model using lag operator?

A

ϕ_{p}(L) y_{t} = ε_{t},

where ϕ_{p}(L) = 1 - ϕ_{1} L - … - ϕ_{p} L^p

20
Q

What is the characteristic polynomial?

A

The lag polynomial ϕ_{p}(L) but considered as a function of z:
ϕ_{p}(z) = 1 - ϕ_{1}z - … - ϕ_{p}z^p

Its roots are the solutions to ϕ_{p}(z) = 0, and they determine whether the effects of shocks are transitory or permanent

21
Q

Interpretation of roots

A

Note: Root is z = …

  • When ϕ_{1} = 1, the AR(1) polynomial is said to have a unit root (and shocks have permanent effects)
  • When |ϕ_{1}| < 1, the root exceeds 1 (and shocks have transitory effects)

The solution is said to be outside the unit circle when |ϕ_{1}| < 1

22
Q

How to rewrite an AR(1) model as MA model (simple, no lag operator)?

A

y_{t} = ε_{t} + π_{1} ε_{t-1} + π_{2} ε_{t-2} + …

23
Q

How to rewrite an AR(p) model as MA model (with lag operator)?

A

y_{t} = ϕ_{p}(L)^{-1} ε_{t}

24
Q

What are the autocovariances of an MA(q) model?

A

γ_{k} = Σ{i=0, q-k} θ_{i}θ_{i+k} σ^2, for k = 0, 1, …, q
= 0, for k > q

with θ_{0} = 1

25
Q

Lag versions of AR(p) and MA(q) models

A

AR(p): ϕ_{p}(L) = 1 - ϕ_{1}L - … - ϕ_{p}L^{p}

MA(q): θ_{q}(L) = 1 + θ_{1}L + … + θ_{q}L^{q}

26
Q

Combine AR(p) and MA(q) (using lag operators)

A

ϕ_{p}(L) y_{t} = θ_{q}(L) ε_{t}

27
Q

How to write ARMA(p,q) model as “pure” AR or “pure” MA?

A

“Pure” AR: [ϕ_{p}(L) / θ_{q}(L)] y_{t} = ε_{t}

“Pure” MA: y_{t} = [θ_{q}(L) / ϕ_{p}(L)] ε_{t}

28
Q

True or false: A non-zero kth order autocorrelation implies that we need to include y_{t-k} in a time series model for y{t}

A

False

29
Q

What is k-th order partial autocorrelation?

A

It measures the dependence or correlation between y_{t} and y_{t-k}, after their common dependence on the intermediate observations y_{t-1}, …, y_{t-k+1} has been removed

It can be defined (and obtained) as the coefficient ψ_{k} in the regression:
y_{t} = η_{1} y_{t-1} + η_{2} y_{t-2} + … + η_{k-1} y_{t-k+1} + ψ_{k} y_{t-k} + u_{t}

30
Q

Values for PAC (ψ_{k}) in AR and MA models

A
  • AR(p) model: ψ_{k} = 0 for all k > p
  • MA(q) model: ψ_{k} declines exponentially towards zero
    (follows from writing MA(q) as θ_{q}(L)^{-1} y_{t} = ε_{t}