Fs Chapter 6 Timeseries

Imperial CollegeLondonBusiness School

FINANCIAL STATISTICSLEC 6: LINEAR TIME SERIES

Paolo Zaffaroni

October, 2014

Preliminaries ARMA 1 / 66

AGENDA

1 Preliminaries

2 ARMALinear processMA processesAR processesARMA(p, q)NonstationarityRegression-based tests of non-stationarityPrediction of ARMA

Linear Time Series: stationarity and lag operator

When observing (y1, ..., yT ) we are now considering yt as partof a stochastic process, which means a collection of rv, onefor each t.

0 50 100 150 200 250 300

6.81−month interest rate UK

When observing (y1, ..., yT ) we are now considering yt as partof a stochastic process, which means a collection of rv, onefor each t.

0 50 100 150 200 250 300

6.81−month interest rate UK

This means that we have one observation per randomvariable: how can we estimate parameters?

Useful to define covariance stationary stochastic process:{yt, t = 0,±1, ...} is covariance stationary if for any t, s

Eyt ≡ µt = µ <∞,cov(yt, ys) ≡ γ(t, s) = γ(t− s) with | γ(t− s) |<∞.

This implies var(yt) = γ(0) <∞, constant and finite.

Graph of (u, γ(u)) defines autocovariance function, hereafterACF.

Often one uses the autocorrelation function (scale free):

ρ(u) = γ(u)γ(0) , u = 0,±1, ... where −1 ≤ ρ(u) ≤ 1.

When (yt1 , ...yts) has a multivariate normal distribution forany integer s and any t1, ..., ts then Gaussian stochasticprocess.

Example. yt = x (constant in time) for some r.v. x iscovariance stationary if Ex2 <∞.

Example. yt = a+ bt (a, b constant numbers) not covariancestationary.

Example. yt satisfying for any t

Eyt = 0, γ(0) = σ2 <∞, γ(t− s) = 0 t 6= s,

make a covariance stationary process defined white noise .

Eyt = 0, γ(0) = σ2 <∞, γ(t− s) = 0 t 6= s,

We have defined the population quantities. Sample analoguesare, for given sample (y1, ..., yT ),

T∑t=1

yt sample mean,

γ(0) =1

T∑t=1

(yt − µ)2 =1

T∑t=1

y2t − µ2 sample variance,

γ(u) =1

T−u∑t=1

(yt − µ)(yt+u − µ) sample autocovariance,

for u = 1, .., T − 1, setting γ(−u) = γ(u), an even function oflag u.

T∑t=1

yt sample mean,

γ(0) =1

T∑t=1

(yt − µ)2 =1

T∑t=1

γ(u) =1

T−u∑t=1

T∑t=1

yt sample mean,

γ(0) =1

T∑t=1

(yt − µ)2 =1

T∑t=1

γ(u) =1

T−u∑t=1

Lag operator: for any time series (need not be stochastic) thelag operator L defined by:

Lyt = yt−1.

In general for m ≥ 1 integer, applying L for m times,

Lmyt = yt−m.

When applying L to a constant has no effect, for instanceL 2 = 2.

Lyt = yt−1.

Lmyt = yt−m.

Lyt = yt−1.

Lmyt = yt−m.

Lag operator properties: commutative with the multiplication(by constant) operator and additive over summation operator.

L(2yt) = 2Lyt = 2yt−1,

L(yt + xt) = Lyt + Lxt = yt−1 + xt−1.

For example, for constant λ1, λ2

(1− λ1L)(1− λ2L)yt = (1 + λ1λ2L2 − λ1L− λ2L)yt

= (1− (λ1 + λ2)L+ λ1λ2L2)yt

= yt − (λ1 + λ2)yt−1 + λ1λ2yt−2.

An expression like (for constants λ0, λ1, λ2)

λ0 + λ1L+ λ2L2

is referred to as a polynomial in the lag operator (in this caseof order 2).

= (1− (λ1 + λ2)L+ λ1λ2L2)yt

= yt − (λ1 + λ2)yt−1 + λ1λ2yt−2.

λ0 + λ1L+ λ2L2

= (1− (λ1 + λ2)L+ λ1λ2L2)yt

= yt − (λ1 + λ2)yt−1 + λ1λ2yt−2.

λ0 + λ1L+ λ2L2

= (1− (λ1 + λ2)L+ λ1λ2L2)yt

= yt − (λ1 + λ2)yt−1 + λ1λ2yt−2.

λ0 + λ1L+ λ2L2

AGENDA

1 Preliminaries

2 ARMALinear processMA processesAR processesARMA(p, q)NonstationarityRegression-based tests of non-stationarityPrediction of ARMA

Linear Time Series: linear process

Important class of stochastic processes made by linear process:

yt = µ+ εt + Ψ1εt−1 + Ψ2εt−2 + ... = µ+

∞∑i=0

Ψiεt−i,

In this case we say that yt represents a moving average of theinnovations εt with coefficients Ψi.

Sequence of non-random coefficients {Ψi} must satisfy∑∞i=1 Ψ2

i <∞.

µ is constant coefficient εt are i.i.d. white noise.

yt = µ+ εt + Ψ1εt−1 + Ψ2εt−2 + ... = µ+

∞∑i=0

Ψiεt−i,

i <∞.

yt = µ+ εt + Ψ1εt−1 + Ψ2εt−2 + ... = µ+

∞∑i=0

Ψiεt−i,

i <∞.

yt = µ+ εt + Ψ1εt−1 + Ψ2εt−2 + ... = µ+

∞∑i=0

Ψiεt−i,

i <∞.

Is linear process a covariance stationary process? Let us check.

Mean: by linearity Eyt = µ+∑∞

i=0 ΨiEεt−i = µ+ 0 = µ soµ is the mean.

Variance: by independence (why?)

var(yt) = var(µ) +∞∑i=0

Ψ2i var(εt−i) = 0 + σ2

∞∑i=0

Ψ2i <∞

Do you see why the Ψ2i must be summable? Examples...

var(yt) = var(µ) +∞∑i=0

∞∑i=0

Ψ2i <∞

var(yt) = var(µ) +

∞∑i=0

Ψ2i <∞

var(yt) = var(µ) +

∞∑i=0

Ψ2i <∞

Autocovariance (ACF): for u > 0.

E(yt − µ)(yt+u − µ) =

∞∑i=0

∞∑j=0

ΨjEεt−jεt+u−i

= σ2∞∑j=0

ΨjΨj+u ≡ γ(u),

because

Eεt−jεt+u−i =

{Eε2t−j = σ2, t− j = t+ u− i,

0 t− j 6= t+ u− i,

where t− j = t+ u− i equivalent to i = j + u.

Very general expression valid for any Ψi!

E(yt − µ)(yt+u − µ) =

∞∑i=0

∞∑j=0

= σ2∞∑j=0

ΨjΨj+u ≡ γ(u),

because

Eεt−jεt+u−i =

{Eε2t−j = σ2, t− j = t+ u− i,

0 t− j 6= t+ u− i,

E(yt − µ)(yt+u − µ) =

∞∑i=0

∞∑j=0

= σ2∞∑j=0

ΨjΨj+u ≡ γ(u),

because

Eεt−jεt+u−i =

{Eε2t−j = σ2, t− j = t+ u− i,

0 t− j 6= t+ u− i,

Linear Time Series: empirical example

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15−0.05

0.05Sample autocorrelation coefficients returns S & P 500

k−values

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15−0.1

0.3Sample autocorrelation coefficients returnsS & P 500 square

k−values

Linear Time Series: Moving Average (MA)

A Moving Average of order q (MA(q)) with integer q:

yt = µ+ εt + α1εt−1 + α2εt−2 + ...+ αqεt−q,

where the α1, ..., αq are constant coefficients.

Special case of linear process by setting Ψ0 = 1, Ψi = αi for1 ≤ i ≤ q and Ψi = 0 for i > q.

Square summability satisfied and mean constant (zero) soMA(q) is stationary. Use general formula for autocovariancefunction (ACF).

Mean: from linearityE(yt) = µ+ E(εt + α1εt−1 + α2εt−2 + ...+ αqεt−q) = µ.

Variance: γ(0) = σ2(1 + α21 + ...+ α2

ACF: for 1 ≤ u ≤ q

γ(u) = σ2∞∑j=0

ΨjΨj+u = σ2q−u∑j=0

αjαj+u,

and when u > q, γ(u) = 0. (Why?)

ACF is truncated: MA have little memory.

Stationarity achieved for any value of coefficients α1, ..., αq,even equal to 1m!

γ(u) = σ2∞∑j=0

αjαj+u,

γ(u) = σ2∞∑j=0

αjαj+u,

Important case is MA(1):

yt = µ+ εt + α1εt−1.

ACF: ρ(1) = α1

1+α21

and ρ(u) = 0 u = ±2,±3, ....Limited

memory!

Positive autocorrelation for α1 > 0 and negativeautocorrelation for α1 < 0.

Replacing α1 by 1/α1 in ρ(1) we get

1 + 1/α21

=1/α1

α21+1

α21 + 1

= ρ(1)

so no difference!

ACF: ρ(1) = α1

1+α21

and ρ(u) = 0 u = ±2,±3, ....Limited

memory!

1 + 1/α21

=1/α1

α21+1

α21 + 1

= ρ(1)

so no difference!

ACF: ρ(1) = α1

1+α21

and ρ(u) = 0 u = ±2,±3, ....Limited

memory!

1 + 1/α21

=1/α1

α21+1

α21 + 1

= ρ(1)

so no difference!

ACF: ρ(1) = α1

1+α21

and ρ(u) = 0 u = ±2,±3, ....Limited

memory!

1 + 1/α21

=1/α1

α21+1

α21 + 1

= ρ(1)

so no difference!

Linear Time Series: invertibility of Moving Average (MA)

If we are to estimate α1 from sample ACF ρ(u), which oneshall we choose: the big one or the small one?

This is related to the issue of invertibility condition, meaningthe possibility to ‘invert’ the process and be able to write ytas a linear function of past values yt−j with j = 1, 2, ....

Invertibility: writing yt = (1 + α1L)εt, we can ask when theexpression

1 + α1L

makes sense.

This requires (treat L as a variable now!) | α1L |< 1 and asufficient condition for this is | α1 |< 1 taking | L |≤ 1.

1 + α1L

makes sense.

1 + α1L

makes sense.

1 + α1L

makes sense.

In this case expansion

1 + α1L=

∞∑i=0

(−α1L)i = 1− α1L+ α21L

2 − α31L

3 + ....

Then 11+α1L

yt = yt − α1yt−1 + α21yt−2 − .... = εt, rewritten as

yt = α1yt−1 − α21yt−2 + ....+ εt.

This is the invertibility .

Of course, to every MA(1) with | α1 |< it corresponds anotherMA(1) with the same ACF but with coefficient 1/ | α1 |> 1,which is not invertible.

1 + α1L=

∞∑i=0

(−α1L)i = 1− α1L+ α21L

2 − α31L

3 + ....

Then 11+α1L

yt = α1yt−1 − α21yt−2 + ....+ εt.

1 + α1L=

∞∑i=0

(−α1L)i = 1− α1L+ α21L

2 − α31L

3 + ....

Then 11+α1L

yt = α1yt−1 − α21yt−2 + ....+ εt.

Extension to MA(q) is based on looking at roots of polynomial

α(L) = 1 + α1L+ ...+ αqLq = 0.

MA invertible when all the roots are greater than one inmodulus: 2q MA(q) models sharing the same AC but one onlyinvertible

What do we take? Invertibility used as a selecting criterion.

Extension to MA(q) is based on looking at roots of polynomial

α(L) = 1 + α1L+ ...+ αqLq = 0.

MA invertible when all the roots are greater than one inmodulus: 2q MA(q) models sharing the same AC but one onlyinvertible

What do we take? Invertibility used as a selecting criterion.

Linear Time Series: empirical example of MA(1)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15−0.2

−0.1

0.5Sample autocorrelation coefficients MA(1)

k−values

Linear Time Series: autoregressive (AR)

Autoregressive process of order p (AR(p)) with integer p:

yt = µ+ φ1yt−1 + ....+ φpyt−p + εt.

with φ1, ..., φp are constant autoregressive coefficients.

AR(p) is stochastic difference equation of order p (whathappens if εt = 0?)

Simplest case is AR(1):

yt = µ+ φ1yt−1 + εt.

yt = µ+ φ1yt−1 + ....+ φpyt−p + εt.

To derive statistical properties one needs solution of thedifference equation: use lag operator!

Writing yt(1− φ1L) = µ+ εt. When | φ1 |< 1 we can write1

1−φ1L = 1 + φ1L+ φ21L

2 + .... =∑∞

i=0 φi1L

i also equal to

yt =µ

1− φ1L+∞∑i=0

φi1εt−i = µ′ +∞∑i=0

φi1εt−i

setting µ′ = µ/(1− φ1).

To derive statistical properties one needs solution of thedifference equation: use lag operator!

Writing yt(1− φ1L) = µ+ εt. When | φ1 |< 1 we can write1

1−φ1L = 1 + φ1L+ φ21L

2 + .... =∑∞

i=0 φi1L

i also equal to

yt =µ

1− φ1L+∞∑i=0

φi1εt−i = µ′ +∞∑i=0

φi1εt−i

setting µ′ = µ/(1− φ1).

Particular case of a linear process: setting Ψi = φi1 i = 0, 1, ...and note that

∑∞i=0 φ

2i1 = 1

1−φ21<∞.

Mean: Eyt = µ′ +∑∞

i=0 φi1Eεt−i = µ′.

Variance: γ(0) = σ2∑∞

i=0 φ2i1 = σ2/(1− φ2

1) <∞.

ACF: for u > 0

γ(u) = σ2∞∑i=0

φi1φi+u1 =

σ2φu11− φ2

and autocorrelation function ρ(u) = φu1 between −1 and 1.

∑∞i=0 φ

2i1 = 1

1−φ21<∞.

i=0 φ2i1 = σ2/(1− φ2

1) <∞.

ACF: for u > 0

γ(u) = σ2∞∑i=0

φi1φi+u1 =

σ2φu11− φ2

∑∞i=0 φ

2i1 = 1

1−φ21<∞.

i=0 φ2i1 = σ2/(1− φ2

1) <∞.

ACF: for u > 0

γ(u) = σ2∞∑i=0

φi1φi+u1 =

σ2φu11− φ2

∑∞i=0 φ

2i1 = 1

1−φ21<∞.

i=0 φ2i1 = σ2/(1− φ2

1) <∞.

ACF: for u > 0

γ(u) = σ2∞∑i=0

φi1φi+u1 =

σ2φu11− φ2

ACF is never zero for a finite lag but it decays to zeroexponentially fast.

Positive autocorrelation at all lags for φ1 > 0 and negativeautocorrelation for φ1 < 0.

Since −1 ≤ ρ(1) ≤ 1 any level of memory can be attained.

Condition | φ1 |< 1 is the stationarity condition.

Linear Time Series: simulated AR stationary (red) andnon-stationary (blue)

0 50 100 150 200 250−20

Stationary and Non Stationary AR(1) process

Non StationaryStationary

Linear Time Series: ACF of AR with positive φ1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15−0.2

−0.1

0.8Sample autocorrelation coefficients AR(1) with positive coefficient

k−values

Linear Time Series: ACF of AR with negative φ1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15−0.8

−0.6

−0.4

−0.2

0.8Sample autocorrelation coefficients AR(1) with negative coefficient

k−values

Generalizations to higher-order AR(p) cumbersome.

Let us consider AR(2)

yt = µ+ φ1yt−1 + φ2yt−2 + εt. (1)

Re-write it as (1− φ1L− φ2L2)yt = µ+ εt.

The statistical properties of yt determined by the nature ofthe roots of the equation in L

(1− φ1L− φ2L2) = 0.

yt = µ+ φ1yt−1 + φ2yt−2 + εt. (1)

(1− φ1L− φ2L2) = 0.

yt = µ+ φ1yt−1 + φ2yt−2 + εt. (1)

(1− φ1L− φ2L2) = 0.

yt = µ+ φ1yt−1 + φ2yt−2 + εt. (1)

(1− φ1L− φ2L2) = 0.

Let ρ1, ρ2 be the inverse of these roots of that second-orderequation.

Then rewrite (1− φ1L− φ2L2) = (1− ρ1L)(1− ρ2L), where

three possibilities arise: ρ1, ρ2 real and distinct, complexconjugates and real but equal.

Stationarity condition is | ρ1 |< 1, | ρ2 |< 1.

In terms of the coefficients this condition is

φ1 + φ2 < 1,

−φ1 + φ2 < 1,

| φ2 |< 1.

φ1 + φ2 < 1,

−φ1 + φ2 < 1,

| φ2 |< 1.

φ1 + φ2 < 1,

−φ1 + φ2 < 1,

| φ2 |< 1.

φ1 + φ2 < 1,

−φ1 + φ2 < 1,

| φ2 |< 1.

Several ways of getting the solution of AR(2). One is considergeometric expansion (see the analogy with AR(1) case), whenroots are real :

yt = µ′ +1

(1− ρ1L)(1− ρ2L)εt,

setting

µ′ =µ

(1− φ1 − φ2).

Several ways of getting the solution of AR(2). One is considergeometric expansion (see the analogy with AR(1) case), whenroots are real :

yt = µ′ +1

(1− ρ1L)(1− ρ2L)εt,

setting

µ′ =µ

(1− φ1 − φ2).

When roots are real and distinct :1

(1− ρ1L)(1− ρ2L)

=∞∑

ρi1ρj2L

i+j =∞∑u=0

(u∑i=0

ρi1ρu−i2

=∞∑u=0

ρu21− (ρ1/ρ2)u+1

1− (ρ1/ρ2)Lu =

∞∑u=0

ρu+12 − ρu+1

ρ2 − ρ1Lu

∞∑u=0

ΨuLu.

so AR(2) is linear process with coefficients Ψu =ρu+12 −ρu+1

1ρ2−ρ1 .

(1− ρ1L)(1− ρ2L)

∞∑i,j=0

ρi1ρj2L

∞∑u=0

(u∑i=0

ρi1ρu−i2

=∞∑u=0

ρu21− (ρ1/ρ2)u+1

1− (ρ1/ρ2)Lu =

∞∑u=0

ρu+12 − ρu+1

ρ2 − ρ1Lu

∞∑u=0

ΨuLu.

1ρ2−ρ1 .

(1− ρ1L)(1− ρ2L)

∞∑i,j=0

ρi1ρj2L

∞∑u=0

(u∑i=0

ρi1ρu−i2

∞∑u=0

ρu21− (ρ1/ρ2)u+1

1− (ρ1/ρ2)Lu =

∞∑u=0

ρu+12 − ρu+1

ρ2 − ρ1Lu

∞∑u=0

ΨuLu.

1ρ2−ρ1 .

(1− ρ1L)(1− ρ2L)

∞∑i,j=0

ρi1ρj2L

∞∑u=0

(u∑i=0

ρi1ρu−i2

∞∑u=0

ρu21− (ρ1/ρ2)u+1

1− (ρ1/ρ2)Lu =

∞∑u=0

ρu+12 − ρu+1

ρ2 − ρ1Lu

∞∑u=0

ΨuLu.

1ρ2−ρ1 .

When coincident roots one gets (ρ1 = ρ2 = ρ)

u∑j=0

ρjρu−j

= (u+ 1)ρu, u = 0, 1, ...

Just use Hopital’s theorem to previous formula.

When coincident roots one gets (ρ1 = ρ2 = ρ)

u∑j=0

ρjρu−j

= (u+ 1)ρu, u = 0, 1, ...

Just use Hopital’s theorem to previous formula.

When complex conjugates roots ρ1 = ρeiθ, ρ2 = ρe−iθ, onegets (i is the complex unit such that i =

√−1)

u∑j=0

ρj1ρu−j2

= ρue−iθu1− e2iθ(u+1)

1− e2iθ

= ρue−iθ(u+1) − eiθ(u+1)

e−iθ − eiθ= ρu

sin(u+ 1)θ

sin θu = 0, 1, ..

sinx =1

2i(eix − e−ix).

In case complex roots oscillatory behaviour of the Ψi.

√−1)

u∑j=0

ρj1ρu−j2

1− e2iθ

sin(u+ 1)θ

sin θu = 0, 1, ..

sinx =1

2i(eix − e−ix).

√−1)

u∑j=0

ρj1ρu−j2

1− e2iθ

sin(u+ 1)θ

sin θu = 0, 1, ..

sinx =1

2i(eix − e−ix).

√−1)

u∑j=0

ρj1ρu−j2

1− e2iθ

sin(u+ 1)θ

sin θu = 0, 1, ..

sinx =1

2i(eix − e−ix).

Linear Time Series: mean, variance and ACF ofautoregressive (AR)

Eyt = µ′ +

∞∑u=0

ΨuEεt−u = µ′.

ACF: instead of using general formula for linear processes,rewrite

yt − µ′ = φ1(yt−1 − µ′) + φ2(yt−2 − µ′) + εt.

Pre-multiplying both terms by yt−j − µ′ with j ≥ 1 andtaking expectations

E(yt − µ′)(yt−j − µ′) = φ1E(yt−1 − µ′)(yt−j − µ′)+φ2E(yt−2 − µ′)(yt−j − µ′) + Eεt(yt−j − µ′).

Eyt = µ′ +

∞∑u=0

yt − µ′ = φ1(yt−1 − µ′) + φ2(yt−2 − µ′) + εt.

E(yt − µ′)(yt−j − µ′) = φ1E(yt−1 − µ′)(yt−j − µ′)+φ2E(yt−2 − µ′)(yt−j − µ′) + Eεt(yt−j − µ′).

Eyt = µ′ +

∞∑u=0

yt − µ′ = φ1(yt−1 − µ′) + φ2(yt−2 − µ′) + εt.

E(yt − µ′)(yt−j − µ′) = φ1E(yt−1 − µ′)(yt−j − µ′)+φ2E(yt−2 − µ′)(yt−j − µ′) + Eεt(yt−j − µ′).Preliminaries ARMA 35 / 66

Assuming stationarity

γ(j) = φ1γ(j − 1) + φ2γ(j − 2), j = 0, 1, ...

so ACF satisfies 2nd difference equation. Need two initialconditions.

In terms of autocorrelation (divide all terms by γ(0)), we getthe so-called Yule-Walker equations:

ρ(j) = φ1ρ(j − 1) + φ2ρ(j − 2).

Here for given φ1, φ2 we determine ρ(j)!

γ(j) = φ1γ(j − 1) + φ2γ(j − 2), j = 0, 1, ...

ρ(j) = φ1ρ(j − 1) + φ2ρ(j − 2).

γ(j) = φ1γ(j − 1) + φ2γ(j − 2), j = 0, 1, ...

ρ(j) = φ1ρ(j − 1) + φ2ρ(j − 2).

Linear Time Series: autoregressive moving average(ARMA)

Autoregressive moving average process of order p, q (AR(p, q))with integer p, q:

yt = µ+ φ1yt−1 + ....+ φpyt−p + εt + α1εt−1 + ...+ αqεt−q.

Stationarity if roots of

1− φ1L− ...− φpLp = 0

lie outside the unit circle in complex plane.Invertibility if roots of

1 + α1L+ ...+ αqLq = 0

lie outside the unit circle in complex plane.

1− φ1L− ...− φpLp = 0

Invertibility if roots of

1 + α1L+ ...+ αqLq = 0

1− φ1L− ...− φpLp = 0

lie outside the unit circle in complex plane.Invertibility if roots of

1 + α1L+ ...+ αqLq = 0

lie outside the unit circle in complex plane.Preliminaries ARMA 37 / 66

To get properties of ARMA(p, q) we need solution. Considerthe ARMA(1, 1):

yt = φ1yt−1 + εt + α1εt−1.

Rewrite it as

(1− φ1L)yt = (1 + α1L)εt.

Comparing it with yt = (∑∞

i=0 ΨiLi)εt yields

∞∑i=0

ΨiLi =

1 + α1L

1− φ1L= (1 + α1L)

∞∑i=0

φi1Li.

yt = φ1yt−1 + εt + α1εt−1.

Rewrite it as

(1− φ1L)yt = (1 + α1L)εt.

∞∑i=0

ΨiLi =

1 + α1L

1− φ1L= (1 + α1L)

∞∑i=0

φi1Li.

yt = φ1yt−1 + εt + α1εt−1.

Rewrite it as

(1− φ1L)yt = (1 + α1L)εt.

∞∑i=0

ΨiLi =

1 + α1L

1− φ1L= (1 + α1L)

∞∑i=0

φi1Li.

By equating the coefficients of the same power of L

Ψ0 = 1,

Ψ1 = φ1 + α1,... =

Ψj = (α1 + φ1)φj−11 .

We can then get the mean, the variance and ACF usinggeneral formula.

Ψ0 = 1,

Ψ1 = φ1 + α1,... =

Ψj = (α1 + φ1)φj−11 .

Ψ0 = 1,

Ψ1 = φ1 + α1,

... =...

Ψj = (α1 + φ1)φj−11 .

Ψ0 = 1,

Ψ1 = φ1 + α1,... =

Ψj = (α1 + φ1)φj−11 .

Ψ0 = 1,

Ψ1 = φ1 + α1,... =

Ψj = (α1 + φ1)φj−11 .

Ψ0 = 1,

Ψ1 = φ1 + α1,... =

Ψj = (α1 + φ1)φj−11 .

But for the ACF also use Yule-Walker equations: in general

γ(j) = φ1γ(j − 1) + Eεtyt−j + α1Eεt−1yt−j .

Thenγ(j) = φ1γ(j − 1) j ≥ 2,

ACF function of both AR and MA coefficients for lags 0 and 1but only dependent on the AR coefficients for lags greaterthan 1.

Same behaviour is observed for ARMA(p, q).

Thenγ(j) = φ1γ(j − 1) j ≥ 2,

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15−0.4

−0.2

1Sample autocorrelation coefficients ARMA(1,1)

k−values

Three steps:

1 Model selection (how to choose p and q).2 Estimation (in the most efficient way as possible).3 Diagnostic testing (to check that the result is satisfactory

using estimated residuals).

If diagnostic checking not satisfactory, start again with newchoice of p, q.

Three steps:1 Model selection (how to choose p and q).

2 Estimation (in the most efficient way as possible).3 Diagnostic testing (to check that the result is satisfactory

Three steps:1 Model selection (how to choose p and q).2 Estimation (in the most efficient way as possible).

3 Diagnostic testing (to check that the result is satisfactoryusing estimated residuals).

Three steps:1 Model selection (how to choose p and q).2 Estimation (in the most efficient way as possible).3 Diagnostic testing (to check that the result is satisfactory

Linear Time Series: model selection of ARMA

Model selection: choose p, q based on correlogram.

When ρ(u) = 0 for u > q,

T12 (ρ(u)− ρ(u))→d N(0, 1 + 2

q∑j=1

ρ2(j)), T →∞.

Works fine for MA but not for AR, where there is no cut-offpoint.

T12 (ρ(u)− ρ(u))→d N(0, 1 + 2

q∑j=1

ρ2(j)), T →∞.

T12 (ρ(u)− ρ(u))→d N(0, 1 + 2

q∑j=1

ρ2(j)), T →∞.

For AR, use partial ACF since in

yt = φ1yt−1 + ...+ φpyt−p + εt,

last coefficient φp defines the partial ACF at lag p. It is equalto zero for lags above p: cut off!

Estimated partial ACF π(u), for u > p, satisfies

T12 π(u)→d N(0, 1), T →∞.

whenever π(u) = 0 u > p.

For AR, use partial ACF since in

yt = φ1yt−1 + ...+ φpyt−p + εt,

last coefficient φp defines the partial ACF at lag p. It is equalto zero for lags above p: cut off!

Estimated partial ACF π(u), for u > p, satisfies

T12 π(u)→d N(0, 1), T →∞.

whenever π(u) = 0 u > p.

Linear Time Series: estimation of AR

Estimation: consider first the AR(p) case. Can be seen as adynamic regression (autoregression). This suggests OLS to

φ = argminφ∈Φ

T∑t=p+1

(yt − φ1yt−1 − ...− φpyt−p)2.

with φ = (φ1, ..., φp)′.

Here OLS called conditional sum of squares (CSS) estimator,as y1, ..., yp given.

Estimation: consider first the AR(p) case. Can be seen as adynamic regression (autoregression). This suggests OLS to

φ = argminφ∈Φ

T∑t=p+1

(yt − φ1yt−1 − ...− φpyt−p)2.

with φ = (φ1, ..., φp)′.

Here OLS called conditional sum of squares (CSS) estimator,as y1, ..., yp given.

Alternative estimator is Yule-Walker estimator based onYule-Walker equations:

1 ρ(1) . . . ρ(p− 1)ρ(1) 1 . . . ρ(p− 2)

......

ρ(p− 1) ρ(p− 2) . . . 1

φ2...φp

ρ(1)ρ(2)

...ρ(p)

Plugging in empirical ACF ρ(u) yields linear system of pequation in p unknown φ1, ..., φp. The opposite of how we useYule-Walker before!

Alternative estimator is Yule-Walker estimator based onYule-Walker equations:

1 ρ(1) . . . ρ(p− 1)ρ(1) 1 . . . ρ(p− 2)

......

ρ(p− 1) ρ(p− 2) . . . 1

φ2...φp

ρ(1)ρ(2)

...ρ(p)

Plugging in empirical ACF ρ(u) yields linear system of pequation in p unknown φ1, ..., φp. The opposite of how we useYule-Walker before!

It turns out that CSS and Yule-Walker estimatorsasymptotically equivalent, in turn equal to Gaussian MLE.

MLE: taking the AR(1) for simplicity, with Gaussian εt

p(yt | yt−1) =1

2πe−(yt−µ−φ1yt−1)/2σ2

, t ≥ 2,

p(y1) =

√1− φ2

2πe−(1−φ21)(yt−µ/(1−φ))/2σ2

Then log-likelihood: l(θ) = constants − T2 log σ2 + 1

2 log(1−φ2

1)− 1−φ212σ2 (y1 − µ/(1− φ1))2 − 1

∑Tt=2(yt − µ− φ1yt−1)2.

p(yt | yt−1) =1

, t ≥ 2,

p(y1) =

√1− φ2

2πe−(1−φ21)(yt−µ/(1−φ))/2σ2

2 log(1−φ2

1)− 1−φ212σ2 (y1 − µ/(1− φ1))2 − 1

∑Tt=2(yt − µ− φ1yt−1)2.

p(yt | yt−1) =1

, t ≥ 2,

p(y1) =

√1− φ2

2πe−(1−φ21)(yt−µ/(1−φ))/2σ2

2 log(1−φ2

1)− 1−φ212σ2 (y1 − µ/(1− φ1))2 − 1

∑Tt=2(yt − µ− φ1yt−1)2.

Linear Time Series: estimation of MA and ARMA

Can you estimate MA by OLS:

yt = µ+ εt + θ1εt−1?

We use MLE where loglikelihood, where θ = (σ2, µ, θ1),

l(θ) = const− T

2logσ2 − 1

T∑t=1

(yt − µ− θ1ε∗t−1(θ))2,

where use recursion ε∗t (θ) = yt − µ− θ1ε∗t−1(θ) starting from

ε∗0(θ) = 0.

No closed form but we know all about MLE!

For ARMA, same idea using recursion etc.

l(θ) = const− T

2logσ2 − 1

T∑t=1

(yt − µ− θ1ε∗t−1(θ))2,

ε∗0(θ) = 0.

l(θ) = const− T

2logσ2 − 1

T∑t=1

(yt − µ− θ1ε∗t−1(θ))2,

ε∗0(θ) = 0.

l(θ) = const− T

2logσ2 − 1

T∑t=1

(yt − µ− θ1ε∗t−1(θ))2,

ε∗0(θ) = 0.

Linear Time Series: empirical example on estimation ofARMA

Use Excel worksheet to:

estimate AR(1) by OLS. Apply to 100 samples and plothistogram of the estimates.

estimate MA(1) by MLE.Apply to 100 samples and plothistogram of the estimates.

Linear Time Series: diagnostic checking of ARMA

Diagnostic checking: having chosen p, q and estimated model,use residuals εt to check if appear white-noise and/or i.i.d..

Portmanteau test: look at first R sample ACF of residuals

R∑u=1

ρε(u)2 →d χ2R−p−q.

under the hypothesis of correct specification with large R, say√T , compared with p+ q.

Idea: if εt white noise then Q small.

If some ρε(u) non zero, Q large.

R∑u=1

Linear Time Series: nonstationarity

Simplest case: random walk (with no drift)

yt = yt−1 + εt.

By substitution yt = y0 +∑t−1

j=0 εt−j .

Set Yt the sigma-algebra induced by ys, s ≤ t. Then:

E(yt | Y0) = y0 (conditional mean),

var(yt | Y0) = tσ2. (conditional variance)

Notice that var(yt | Y0)→∞.

yt = yt−1 + εt.

j=0 εt−j .

yt = yt−1 + εt.

j=0 εt−j .

yt = yt−1 + εt.

j=0 εt−j .

In terms of ACF, for s > 0 one gets cov(yt+syt | Y0) = tσ2.

Random walk is nonstationary! However ∆yt = εt isstationary.

In general ARIMA(p, d, q) viz. autoregressive integratedmoving average process of order (p, d, q):

φ(L)∆dyt = θ(L)εt

withφ(L) = 1− φ1L− ...− φpLp, θ(L) = 1 + θ1L+ ...+ θqL

For d = 1, 2.. model is nonstationary.

Model selection of ARIMA proceeds as follows:

1 Plots data in levels and differences.2 Empirical correlogram: not declining fast for nonstationary.3 Formal tests of nonstationarity (see below).4 Goodness-of-fit statistic (Akaike): choose p, d, q for which

minimizedAIC = −2 lnL(Ψ) + 2(p+ q)

where L(Ψ) is likelihood function, T sample size, Ψ is MLE ofparameters.

Model selection of ARIMA proceeds as follows:1 Plots data in levels and differences.

2 Empirical correlogram: not declining fast for nonstationary.3 Formal tests of nonstationarity (see below).4 Goodness-of-fit statistic (Akaike): choose p, d, q for which

Model selection of ARIMA proceeds as follows:1 Plots data in levels and differences.2 Empirical correlogram: not declining fast for nonstationary.

3 Formal tests of nonstationarity (see below).4 Goodness-of-fit statistic (Akaike): choose p, d, q for which

Model selection of ARIMA proceeds as follows:1 Plots data in levels and differences.2 Empirical correlogram: not declining fast for nonstationary.3 Formal tests of nonstationarity (see below).

4 Goodness-of-fit statistic (Akaike): choose p, d, q for whichminimized

AIC = −2 lnL(Ψ) + 2(p+ q)

Model selection of ARIMA proceeds as follows:1 Plots data in levels and differences.2 Empirical correlogram: not declining fast for nonstationary.3 Formal tests of nonstationarity (see below).4 Goodness-of-fit statistic (Akaike): choose p, d, q for which

Linear Time Series: test of nonstationarity

Most basic approach: Dickey-Fuller test.

We need to see the consequences of non-stationarity when wedo not know if that is the case, otherwise just first-differencethe data.

For CSS estimator (sort of OLS right?) when | φ |< 1 then

T12 (φ− φ)→d N(0, 1− φ2).

Setting φ2 = 1 then suggests T12 (φ− φ)→d N(0, 0) = 0.

What is going on? φ converging to zero faster than 1/√T !

T12 (φ− φ)→d N(0, 1− φ2).

Fuller (1976) suggested that one can test for

H0 : φ = 1

againstH0 : φ < 1,

using the usual t-test

τ =φ− 1(

avar(φ))1/2

where avar(φ) = s2/(∑T

t=2 y2t−1

)and s2 sample variance of

(yt − φyt−1)2.

τ not converging to N(0, 1) but to Dickey-Fuller distribution .

Fuller (1976) suggested that one can test for

H0 : φ = 1

againstH0 : φ < 1,

using the usual t-test

τ =φ− 1(

avar(φ))1/2

where avar(φ) = s2/(∑T

t=2 y2t−1

)and s2 sample variance of

(yt − φyt−1)2.τ not converging to N(0, 1) but to Dickey-Fuller distribution .

Linear Time Series: prediction of ARMA

At time T + L

yT+L = φ1yT+L−1 + ...+ φpyT+L−p + εT+L

+α1εT+L−1 + ...+ αqεT+L−q.

Setting XT+L|T = E(XT+L | YT ) for any r.v. Xt withYT = {y1, ..., yT } get recursive expression:

yT+L|T = φ1yT+L−1|T + ...+ φpyT+L−p|T + εT+L|T

+α1εT+L−1|T + ...+ αq εT+L−q|T , L = 1, 2, ...

with yT+j|T = yT+j j ≤ 0 and εT+j|T =

{0 j > 0,

εT+j j ≤ 0.by

i.i.d.ness of εt.

At time T + L

yT+L = φ1yT+L−1 + ...+ φpyT+L−p + εT+L

+α1εT+L−1 + ...+ αqεT+L−q.

Setting XT+L|T = E(XT+L | YT ) for any r.v. Xt withYT = {y1, ..., yT } get recursive expression:

yT+L|T = φ1yT+L−1|T + ...+ φpyT+L−p|T + εT+L|T

+α1εT+L−1|T + ...+ αq εT+L−q|T , L = 1, 2, ...

with yT+j|T = yT+j j ≤ 0 and εT+j|T =

{0 j > 0,

εT+j j ≤ 0.by

i.i.d.ness of εt.

For AR(1) this simplifies to

yT+L|T = φ1yT+L−1|T , L = 1, 2, ...

Starting value yT |T = yT yields yT+L|T = φL1 yT , L = 1, 2, .

For MA(1) yT+1|T = α1εT and yT+L|T = 0 L ≥ 2, assumingwe observe εT (invertibility).

yT+L|T = φ1yT+L−1|T , L = 1, 2, ...

How to assess the precision of prediction?

Use mean square error . For any stationary ARMA

yT+L =L∑j=1

ΨL−jεT+j +∞∑j=0

Ψj+LεT−j .

For L ≥ 0

yT+L|T =∞∑j=0

Ψj+LεT−j .

Forecasting error made is

yT+L − yT+L|T =

L∑j=1

ΨL−jεT+j

How to assess the precision of prediction?Use mean square error . For any stationary ARMA

yT+L =

L∑j=1

ΨL−jεT+j +

∞∑j=0

Ψj+LεT−j .

For L ≥ 0

yT+L|T =∞∑j=0

Ψj+LεT−j .

yT+L − yT+L|T =

L∑j=1

ΨL−jεT+j

yT+L =

L∑j=1

ΨL−jεT+j +

∞∑j=0

Ψj+LεT−j .

For L ≥ 0

yT+L|T =

∞∑j=0

Ψj+LεT−j .

yT+L − yT+L|T =

L∑j=1

ΨL−jεT+j

yT+L =

L∑j=1

ΨL−jεT+j +

∞∑j=0

Ψj+LεT−j .

For L ≥ 0

yT+L|T =

∞∑j=0

Ψj+LεT−j .

yT+L − yT+L|T =L∑j=1

ΨL−jεT+j

The MSE is the variance of forecast error:

MSE(yT+L|T ) = σ2(1 + Ψ21 + ...+ Ψ2

L−1).

As L→∞ goes to unconditional variance!AR(1) example:

yT+L|T =

∞∑j=0

Ψj+L1 εT−j = φL1 yT and

MSE(yT+L|T ) = σ2 1− φ2L1

1− φ21

If εt Gaussian, CI of prediction:

yT+L|T ± 1.96σ(1 + Ψ21 + ...+ Ψ2

L−1)12 .

MSE(yT+L|T ) = σ2(1 + Ψ21 + ...+ Ψ2

L−1).

As L→∞ goes to unconditional variance!

AR(1) example:

yT+L|T =

∞∑j=0

1− φ21

yT+L|T ± 1.96σ(1 + Ψ21 + ...+ Ψ2

L−1)12 .

MSE(yT+L|T ) = σ2(1 + Ψ21 + ...+ Ψ2

L−1).

yT+L|T =

∞∑j=0

1− φ21

yT+L|T ± 1.96σ(1 + Ψ21 + ...+ Ψ2

L−1)12 .

MSE(yT+L|T ) = σ2(1 + Ψ21 + ...+ Ψ2

L−1).

yT+L|T =

∞∑j=0

1− φ21

yT+L|T ± 1.96σ(1 + Ψ21 + ...+ Ψ2

L−1)12 .Preliminaries ARMA 59 / 66

How to estimate the εt? We assumed we know εt for t ≤ Twe do not!

Use invertibility: recall that

εt = yt−η1yt−1−η2yt−2− ...−ηt−1y1−ηty0 − ηt+1y−1 − ......

Since we do not know all yt but only sample, approximate εtwith

εt = yt − η1yt−1 − η2yt−2 − ...− ηt−1y1.

MA(1) example:

yT+1|T = α1εT = α1(

∞∑j=0

(−α1)jyT−j)

εt = yt − α1yt−1 + α21yt−2 + ...+ (−α1)t−1y1,

MA(1) example:

yT+1|T = α1εT = α1(

∞∑j=0

(−α1)jyT−j)

εt = yt − α1yt−1 + α21yt−2 + ...+ (−α1)t−1y1,

Linear Time Series: empirical example of prediction ofAR(1)

5 10 15 20 25 30 35

Number of Observatioms

Out of Sample Forecasting with ARMA(1,1)

Actual12 step Ahead1 steps Ahead

Linear Time Series: analytical questions

1 Derive the ACF for the MA(2) scheme:

yt = εt + θ1εt−1 + θ2εt−2.

2 Derive the mean, variance and ACF of ∆yt where

yt = δ0 + δ1t+ ut,

withut = αut−1 + εt, 0 < α < 1.

1 Derive the ACF for the MA(2) scheme:

yt = εt + θ1εt−1 + θ2εt−2.

2 Derive the mean, variance and ACF of ∆yt where

yt = δ0 + δ1t+ ut,

withut = αut−1 + εt, 0 < α < 1.

3 A macroeconomist postulates that the log of US real GNPcan be represented by

A(L)(yt − δ0 − δ1t) = εt, A(L) = 1− α1L− α2L2.

An OLS fit yields

yt = −0.321 + 0.003t+ 1.335yt−1 − 0.401yt−2 + εt.

Derive the values of α1, α2, δ0, δ1.Compute the roots of the characteristic equation.What is the estimated value of A(1)?An alternative specification fitted to the same data yields

∆yt = 0.003 + 0.369∆yt−1 + vt.

What are the roots of this equation?

3 A macroeconomist postulates that the log of US real GNPcan be represented by

A(L)(yt − δ0 − δ1t) = εt, A(L) = 1− α1L− α2L2.

An OLS fit yields

yt = −0.321 + 0.003t+ 1.335yt−1 − 0.401yt−2 + εt.

Derive the values of α1, α2, δ0, δ1.Compute the roots of the characteristic equation.What is the estimated value of A(1)?An alternative specification fitted to the same data yields

∆yt = 0.003 + 0.369∆yt−1 + vt.

What are the roots of this equation?

4 Find the optimal MMSE prediction for ARMA(1, 1).

5 Derive the partial autocorrelation function for MA(1) and forAR(1).

4 Find the optimal MMSE prediction for ARMA(1, 1).

5 Derive the partial autocorrelation function for MA(1) and forAR(1).

Linear Time Series: Summary

Preliminaries.

ARMA: linear process.

ARMA: MA process.

ARMA: AR process.

ARMA(p, q) process.

ARMA: nonstationarity.

ARMA: prediction.

Fs Chapter 6 Timeseries

Documents

Transcript of Fs Chapter 6 Timeseries

SNB‘s new SDMX based Timeseries Repository

Chapter 1 - Fs Technical Study

Timeseries Presentation

TimeSeries Toolkit

Chapter 1 Cim Fs

Hierarchical TimeSeries Clustering for Data Streams

Timeseries Notes

Gaussian Processes for Timeseries Modelling

Fs Chapter 2 Ul

Cassandra 2.0 and timeseries

univaritae timeseries

Software Engineering College 6 -timeseries data

’timeSeries’ Package Vignette: Plotting ’timeSeries’ Objects · ’timeSeries’ Package Vignette: Plotting ’timeSeries’ Objects Diethelm Wurtz and Tobias Setz Econophysics

Fs Chapter 1

Foundation Skills (FS) Chapter 1

Timeseries 586

TSAR (TimeSeries AggregatoR) Tech Talk

Time Series Analysisrootzen/timeseries/timeseries... · 2016. 4. 25. · Time Series Analysis V.Fermanelli,M.Longﬁls April19,2016 V.Fermanelli,M.Longﬁls TimeSeriesAnalysis. NonFinancialTS

Kisters WATER TimeSeries Documentation

Analysis of fMRI Timeseries...1 Chapter 10 Analysis of fMRI Timeseries: Linear Time-Invariant Models, Event-related fMRI and Optimal Experimental Design Rik Henson The Wellcome Dept.