Models for Time Series

Question 1

White Noise

Definition

Answer 1

-time series {Xt} is called white noise if the Xt are i.i.d. with E(Xt)=0 for all t and variance is a finite constant:
Var(Xt) = σ² < ∞
-to check this in practice we would compare the residuals of our model with the residuals we expect for white noise

Question 2

White Noise

Mean

Answer 2

μ(t) = E(Xt) = 0

Question 3

White Noise

Autocovariance

Answer 3

γk = cov(Xt, Xt+k) = {σ², k=0 and 0, else

-since for white noise we model the Xt as independent

Question 4

White Noise

Autocorrelation

Answer 4

ρk = {1, k=0 and 0, else

-since Xt are independent

Question 5

White Noise

Remarks

Answer 5

1) often we assume Xt~N(0,σ²)
2) white noise is often used to model the residuals of more complicated time series
3) we usually denote a white noise process as {εt} with variance σε²
4) we can use correlogram to distinguish between white noise and processes with dependence

Question 6

Bartlett’s Theorem

Answer 6

-if {Xt} is white noise, then for large n the distribution of ρk^, k≠0 is approximately N(0,1/n)

Question 7

Identifying White Noise on a Correlogram

Answer 7

• if a time series is white noise, 95% of the lines on the correlogram should lie between 1.96/+√n and 1.96/-√n
• i.e. value |ρk^| > 1.96/√n are significant at the 5% level

Question 8

Moving Average Model

Definition

Answer 8

-a stochastic process {Xt} is called a moving average process of order q (or an MA(q) process) if:
Xt = Σ βk * εt-k
-sum from k=0 to k=q
-where βo,…,βq ∈ R and {εt} is white noise
-i.e. Xt can be written as the weighted average of near past white noise
-near because we expect q to be small since more distant events are unlikely to have impact on more current events

Question 9

Moving Average Model

Remarks

Answer 9

1) without loss of generality, we can assume βo=1 (since we can choose σε²=Var(εt)
2) since {εt} is stationary, {Xt} is stationary if q is finite (q

Question 10

MA Process

Mean

Answer 10

μ(t) = E(Xt) = 0

Question 11

MA Process

γo

Answer 11

γo = (βo²+…+βq²) σε²

Question 12

MA Process

γk

Answer 12

γk = {Σ βi βi+k σε² , 0≤k≤q and 0, else

-sum between i=0 and i=q-k

Question 13

MA(0)

Answer 13

-an MA(0) process is white noise

Question 14

Moving Average Model

Finding β

Answer 14

• in practice, when applying a moving average model to data we don’t know what the β values should be
• to estimate the βs we can write β=β(ρ)
• since we can estimate ρ from the data, this allows us to estimate β as well
• if square root gives two values of β remember that since we don’t expect data to depends too much on the past we can choose the root that gives the smallest magnitude coefficient

Question 15

Autoregressive Model

Definition

Answer 15

-a stochastic process {Xt} is an autoregressive process of order p, AR(p), if:
Xt = Σ αk*Xt-k + εt, for all t
-sum from k=1 to k=p
-where α1,…,αp∈R, {εt} is white noise and εt is independent of Xs for all s

Question 16

Autoregressive Model

Remarks

Answer 16

• when constructing process {Xt} the first p values need to be specified as an initial condition
• we shall see that whether or not {Xt} is stationary depends on α1,…,αp and the initial conditions

Question 17

Random Walk

Answer 17

-an AR(1) process:

Xt = α*Xt-1 + εt

Question 18

Expectation of an AR(1) Process

Answer 18

Xt = α*Xt-1 + εt
- since E(εt)=0 :
E(Xt) = α E(Xt-1) = α^t E(X0)

Question 19

Variance of an AR(1) Process

Answer 19

Xt = α*Xt-1 + εt
Var(Xt) = α²Var(Xt-1) + σε²

Question 20

When is a general AR(1) process weakly stationary?

Answer 20

Xt = α*Xt-1 + εt

• a general AR(1) process is weakly stationary when:
  1) |α|<1
  2) E(Xt) = 0 for all t
  3) Var(Xt) = σε² / [1-α²] for all t including t=0

Question 21

Stationarity of AR(p) Proposition

Answer 21

-an AR(p) process is stationary if and only if all the roots y1,…,yp of the equation:
α(y) = 1 - α1y - … - αpy^p
-are such that |yi|>1

Question 22

The Backshift Operator

Definition

Answer 22

-the backshift operator, B, is defined by:

B Xt = Xt-1, for all t

Question 23

Step 1 - AR(p) Process in Terms of the Backshift Operator

Answer 23

Xt = α1Xt-1 + … + αpXt-p + εt

= α1*B(Xt) + ... + α1*p*B^p(Xt) + εt
=>
(1 - α1*B - ... - α1*p*B^p)Xt = εt
=>
Xt = [1 - Σ αk*B^k]^(-1) εt
-sum from k=1 to k=0
-apply a binomial expansion to the bracket

Question 24

Step 2 - Define and Find ck

Answer 24

-we have 
Xt = [1 - Σ αk*B^k]^(-1) εt
-let:
α(B) = εt Σ ck*B^k
-then
Xt = α(B)^-1 εt
-to get rid of the inverse, we shall find numbers ck such that 
1/α(y) = Σ ck*y^k
-for an AR(p) process,
ck = A1/y1^k + ... + Ap/yp^l

Question 25

Step 3 - assume Xt is weakly stationary

Answer 25

Var(Xt) = (Σ ck²) σε² -for this to exist, i.e. be a finite constant, we need: Σ ck² < ∞ Σ (A1/y1^k + ... + Ap/yp^l)² < ∞ -an AR(p) process is stationary <=> |yi|>1 for all i=1,...,p

Question 26

Stationarity of AR(2)

Answer 26

-for an AR(2) process, we have: α(y) = 1 - α1*y - α2*y² -roots given by the quadratic formula -if α1²+4α2>0 we have two real roots -if α1²+4α2<0, we have two complex roots -if α2>-1 AND α2<1-α1 AND α2<1+α1 then the process is stationary

Question 27

Stationarity of AR(p), p>2

Answer 27

-for AR(p) with p>2, use computer to find the roots

Question 28

Autocovariance of AR(p)

Answer 28

γk = Σ αj * γk-j - for all k≥1 - sum from j=1 to j=p

Question 29

Autocorrelation of AR(p)

Answer 29

ρk = Σ αj * ρk-j | -sum from j=1 to j=p

Question 30

Yule Walker Equations

Answer 30

-used to determine α1,α2,...,αp from ρ1,ρ2,...,ρp for AR(p) models ρ1 = 1*α1 + ρ1*α2 + ... + ρp-1*αp ρ2 = ρ1*α1 + 1*α2 + ... + ρp-2*αp ... ρp = ρp-1*α1 + ρp-2*α2 + ... + 1*αp -from the data we can estimate the ρs and then use these equations to calculate the αs

Question 31

AR(p) Model Fitting Steps

Answer 31

- to fit an AR(p) model to data {Xt}=(X1,...,Xn): 1) subtract the trend and the seasonal effects from {Xt} to obtain residuals {Yt} 2) estimate the acf of Y to obtain ρ1^,ρ2^,...,ρp^ 3) solve Yule-Walker equations for α1^,...,αp^ 4) consider the residuals Zt = Yt - α1^*Yt-1 - ... - αp^*Yt-p use Bartlett bands to check if {Zt} are (approx.) white noise, else the model is not a good fit for the data 5) use sample variance of {Zt} to estimate σε² 6) add trend and seasonal effects back on to conclusions about {Yt} to get conclusions for {Xt}

Question 32

ARMA Model | Definition

Answer 32

-an ARMA(p,q) model satisfies: Xt = Σ αi*Xt-i + εt + Σ βj*εt-j -with εt independent of Xt-1,Xt-2,... -sum from i=1 to i=p and j=1 to j=q

Question 33

ARMA Model | In Terms of the Backshift Operator

Answer 33

``` α(B) Xt = β(B) εt -where: α(y) = 1 - Σ αi*y^i -sum from i=1 to i=p -and: β(y) = 1 + Σ βj*y^j -sum from j=1 to j=q ```

Question 34

ARMA Model | Weak Stationarity

Answer 34

``` -as for AR(p), can write: Xt = α(B)^(-1) β(B) εt -weakly stationary if: Xt = Σ λk*β(B)*εt-k = Σ λk~*εt-k -with Σ λk~ < ∞ -sums from k=0 to k=∞ -equivalent to the roots of α() lying outside the complex unit circle ```

Question 35

ARMA Model | Stationarity and Expectation

Answer 35

-if stationary, we have: E(Xt) = Σ λk~*E(εt-k) = 0 -sum from k=0 to k=∞

Question 36

ARMA Model | Reconstruct Noise as a Function of the Data and Invertibility

Answer 36

εt = β(B)^(-1) * α(B) * Xt = Σ δk*Xt-k - sum from k=0 to k=∞ - if Σ δk² < ∞, then the process is invertible - equivalently, {Xt} is invertible if the roots of β lie outside the complex unit circle

Question 37

ARMA Model | Autocovariance

Answer 37

-autocovariance of an ARMA(p,q) process is given by: γk = Σ αi * γk-i , k>q -sum from i=0 to i=p -recall that an AR(p) process satisfies the same relation but for all k>0, so AR and ARMA model show the same behaviour for k>q

Question 38

Difference Operator | Definition

Answer 38

∇(Xt) = Xt - Xt-1 , for all t | -where ∇ is the difference operator

Question 39

Difference Operator | Backshift Operator and Stationarity

Answer 39

∇ = 1 - B | -if {Xt} has stationary increments then ∇X is stationary

Question 40

Difference Operator | Constant Mean and Linear Trend

Answer 40

-applying the difference operator to a series with constant mean removes the mean: ∇(Xt + μ) = Xt + μ - (Xt-1 + μ) = Xt - Xt-1 = ∇Xt -applying the difference operator to a linear trend converts it to a constant mean: ∇(Xt + a + bt) = Xt + a + bt - (Xt-1 + a + bt) = Xt - Xt-1 + b = ∇Xt + b

Question 41

ARIMA Model | Definition

Answer 41

-autoregressive integrated moving average process | {Xt} is an ARIMA(p,d,q) process if ∇^d Xt is stationary ARMA(p,q) process

Question 42

ARIMA Model | In Terms of ARMA Model

Answer 42

-an ARIMA(p,d,q) process {Xt} can be written as an ARMA(p+d,q) process which has a unit root and hence is non-stationary for d>0