Time series models 2007
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1 Norwegia Uiversity of Sciece ad Techology Departmet of Mathematical Scieces Solutios to problem sheet 1, 2007 Exercise 1.1 a Let Sc = E[Y c 2 ]. The This gives Sc = EY 2 2cEY + c 2 ds dc = 2EY + 2c = 0 for c = EY, which leads to a global miimum sice d2 S dc 2 = 2 > 0 for all c. b E[ Y fx 2 X] = E[ Y EY X + EY X fx 2 X] = E[ Y EY X 2 X] + 2E[ Y EY X EY X fx X] + E[ EY X fx 2 X] = E[ Y EY X 2 X] + 2 X] 2 EY X fx E[ Y EY X X] + E[ EY X fx = E[ Y EY X 2 X] + 2 X] 2 X] E[ EY X fx E[ Y EY X because EY X is a fuctio of X ad EgXY X = gxey X for ay fuctio g such that EgXY exists. It follows that E[ Y EY X 2 X] E[ Y fx 2 X] for ay fuctio f. Hece E[ Y fx 2 X] is miimized whe fx = EY X. c Sice E[ Y EY X 2 ] = E E[ Y EY X 2 X] E E[ Y fx 2 X] = E[ Y fx 2 ] it follows immediately that the radom variable fx that miimizes E[ Y fx 2 ] is fx = EY X. problemsheet1 August 30, 2007 Side 1
2 Exercise 1.2 a Let X = X 1, X 2,..., X. The E[ X +1 fx 2 X] = E[ X+1 EX +1 X + EX +1 X fx 2 X] = E[ X +1 EX +1 X 2 X] + 2E[ X+1 EX +1 X EX +1 X fx X] + E[ EX +1 X fx 2 X] = E[ X +1 EX +1 X 2 X] + 2 EX+1 X fx E[ X +1 EX +1 X X] + E[ EX +1 X fx 2 X] = E[ X +1 EX +1 X 2 X] + E[ EX+1 X fx 2 X] E[ X+1 EX +1 X 2 X] because EX +1 X is a fuctio of X ad EgXX +1 X = gxex +1 X for ay fuctio g such that EgXX +1 exists. It follows that E[ X +1 EX +1 X 2 X] E[ X+1 fx 2 X] for ay fuctio f. Hece E[ X +1 EX +1 X 2 X] is miimized whe fx = EX+1 X. b Sice E[ X +1 EX +1 X 2 ] = E E[ X+1 EX +1 X 2 X] E E[ X +1 fx 2 X] = E[ X+1 fx 2 ] it follows immediately that the radom variable fx that miimizes E[ X +1 fx 2 ] is agai fx = EX +1 X. c By b the miimum mea-squared error predictor of X +1 i terms of X = X 1, X 2,..., X whe X t IIDµ, σ 2 is EX +1 X = EX +1 = µ d Suppose that i=1 α ix i is a ubiased estimator for µ, that is, i=1 α i = 1. The E[ α i X i µ 2 ] = E[ α i X i X 2 ] + 2E[ α i X i X X µ ] + E[ X µ 2 2] ] E[ X µ i=1 i=1 i=1 sice the secod term is zero: E[ i=1 α ix i X X µ ] = Cov i=1 α ix i X, X = Cov i=1 α ix i, i=1 1 X i Cov i=1 1 X i, i=1 1 X i = i=1 α i σ2 i=1 1 σ 2 = 0. 2 problemsheet1 August 30, 2007 Side 2
3 e Agai, suppose that i=1 α ix i is a ubiased estimator for µ, that is, i=1 α i = 1. The E[ X +1 2] α i X i = E[ X+1 X 2 ] + 2E[ X+1 X X i=1 E[ X +1 X 2 ] 2] α i X i ] + E[ X α i X i sice the secod term is zero: Cov X +1 X, X i=1 α ix i = CovX, X+CovX, i=1 α ix i = 0 as i d. i=1 i=1 f E S +1 S 1,..., S = E S + X +1 S 1,..., S = S + E X +1 S 1,..., S = S + µ sice X +1 is idepedet of S 1,..., S. Exercise 1.3 i EX t is idepedet of t sice the distributio of X t is idepedet of t ad EX t exists. ii Sice E[X t+h X t ] 2 E[X 2 t+h ]E[X2 t ] for all itegers t, h, ad the joit distributio of X t+h ad X t is idepedet of t, it follows that E[X t+h X t ] exists ad is idepedet of t for every iteger h. Combiig i ad ii it follows that X t is weakly statioary. Exercise 1.4 a EX t = a is idepedet of t. b Cov 2 + c 2 σ 2 ; h = 0 0 ; h = ±1 X t+h, X t = bcσ 2 ; h = ±2 0 ; h > 2 which is idepedet of t. That is, X t is statioary. b problemsheet1 August 30, 2007 Side 3
4 EX t = 0 is idepedet of t. Cov X t+h, X t = Cov Z1 cos ct + h + Z 2 si ct + h, Z 1 cos ct + Z 2 si ct = σ 2 cos ct + h cos ct + si ct + h si ct = σ 2 cos ch which is idepedet of t. That is, X t is statioary. c EX t = 0 is idepedet of t. Cov X t+1, X t = σ 2 cos ct + 1 si ct which is ot idepedet of t. That is, X t is ot statioary except i the special case whe c is a iteger multiple of 2π. d EX t = a is idepedet of t. Cov X t+h, X t = b 2 σ 2 which is idepedet of t. That is, X t is statioary. e EX t = 0 is idepedet of t. Cov X t+h, X t = σ 2 cos ct + h cos ct which is ot idepedet of t. That is, X t is ot statioary except i the special case whe c is a iteger multiple of 2π. f EX t = 0 is idepedet of t. Cov X t+h, X t = E[Xt+h X t ] = E[Z t+h Z t+h 1 Z t Z t 1 ] = { σ 4 ; h = 0 0 ; h > 0 which is idepedet of t. That is, X t is statioary, ad it is see that i fact X t W N0, σ 4. problemsheet1 August 30, 2007 Side 4
5 Exercise 1.5 a The autocovariace fuctio The autocorrelatio fuctio For θ = 0.8 it is obtaied that 1 + θ 2 ; h = 0 γ X h = θ ; h = ±2 0 ; otherwise 1 ; h = 0 θ ρ X h = ; h = ±2 1+θ 2 0 ; otherwise 1.64 ; h = 0 γ X h = 0.8 ; h = ±2 0 ; otherwise 1 ; h = 0 ρ X h = ; h = ±2 0 ; otherwise b Let X 4 = 1 4 X X 4. The V arx 4 = Cov X 4, X 4 = i=1 i=1 4 Cov X i, X j = 1 γx 0 + γ X 2 = = c V arx 4 = Cov 1 X 4, X 4 = γx 0 + γ X 2 4 = = The egative lag 2 correlatio i c meas that positive deviatios of X t from zero ted to be followed two time uits later by a compesatig egative deviatio, resultig i smaller variability i the sample mea tha i b ad also smaller tha if the time series X t were IID0, 1.64 i which case V arx 4 = problemsheet1 August 30, 2007 Side 5
6 Exercise 1.15 a Sice s t has period 12 so that The E[W t ] = 0 ad 12 X t = 12 a + bt + s t + Y t = 12b + Y t Y t 12 W t := 12 X t = Y t Y t 1 Y t 12 Y t 13. Cov[W t+h, W t ] = Cov[Y t+h Y t+h 1 Y t+h 12 Y t+h 13, Y t Y t 1 Y t 12 Y t 13 ] = 4γh 2γh 1 2γh γh 11 + γh γh 12 2γh γh γh 13 where γ is the ACVF of Y t. Sice E[W t ] ad Cov[W t+h, W t ] are idepedet of t, W t is statioary. Also ote that 12 X t is statioary. b Usig X t = a + bts t + Y t it is obtaied that 12 X t = bts t bt 12s t 12 + Y t Y t 12 = 12bs t 12 + Y t Y t 12. Now let U t = 2 12 X t = Y t 2Y t 12 + Y t 24. The E[U t ] = 0 ad Cov[U t+h, U t ] = Cov[Y t+h 2Y t+h 12 + Y t+h 24, Y t 2Y t 12 + Y t 24 ] = 6γh 4γh γh 12 + γh γh 24, which is idepedet of t. Hece U t is statioary. Exercise 2.1 Sa, b = E[X +h ax b 2 ] to be miimized wrt a ad b. Now Sa, b = E[ X +h µ ax µ b aµ + µ ] = γ0 + a 2 γ0 + b + aµ µ 2 2aγh This gives S = 2aγ0 + 2µb + aµ µ 2γh a S = 2b + aµ µ b S is clearly miimized wrt b whe for b = µ1 a. Substitutig this value ito S a ad equatig to zero leads to the result a = γh γ0 = ρh Hece, Sa, b is miimized whe a = ρh, b = µ1 ρh The BLP best liear predictor of X +h i terms of X is therefore µ + ρhx µ. problemsheet1 August 30, 2007 Side 6
7 Exercise 2.3 a X t = Z t + 0.3Z t 1 0.4Z t 2 γ0 = = 1.25 γ1 = = 0.18 γ2 = 0.4 γh = 0, h > 2 γ h = γh b Y t = Z t 1.2 Z t Z t 2 γ0 = = 1.25 γ1 = = 0.18 γ2 = = 0.4 γh = 0, h > 2 γ h = γh That is, we obtai the same ACVF as i a. Exercise 2.5 j=1 θj X j coverges absolutely with probability 1 sice E[ θ j X j ] j=1 θ j E[ X j ] j=1 θ j γ0 + µ 2 j=1 < sice θ < 1 by Cauchy-Schwartz iequality That is, j=1 θ j X j < with probability 1. Mea square covergece of S m = m j=1 θj X j as m ca be veried by ivokig Cauchy's criterio. For m > k E[ S m S k 2 ] = E[ θ j X j 2 ] = j=k+1 i=k+1 j=k+1 θ i+j E[X i X j ] problemsheet1 August 30, 2007 Side 7
8 E[ S m S k 2 ] = E[ = j=k+1 i=k+1 j=k+1 i=k+1 j=k+1 θ j X j 2 ] = 0 as k, m i=k+1 j=k+1 θ i+j γi j + µ 2 θ i+j E[X i X j ] θ i+j γ0 + µ 2 = γ0 + µ 2 m j=k+1 θ j 2 sice j=1 θ j <. Hece, by Cauchy's mutual covergece criterio, mea square covergece is guarateed. Exercise 2.7 sice φz > φz = 1 φz 1 1 φz = φz φz + 1 φz = φz j j=1 Exercise 2.8 X t = φx t 1 + Z t X t = φx t 1 + Z t = Z t + φz t 1 + φx t 2 =... = Z t + φz t φ Z t + φ +1 X t 1 That is X t φ +1 X t 1 = Z t + φz t φ Z t First we calculate VarX t φ +1 X t 1 = γ01 + φ 2+2 2φ +1 γ + 1 γ01 + φ φ +1 = 4γ0 problemsheet1 August 30, 2007 Side 8
9 if X t is statioary ad φ = 1 Next we calculate VarZ t + φz t φ Z t = σ 2 if φ = 1 Sice clearly σ 2 > 4γ0 for sucietly large, we have reached a cotradictio. Hece X t caot be statioary if φ = 1. Exercise 2.10 X t φx t 1 = Z t + θz t 1 where φ = θ = 0.5 Accordig to Sectio 2.3, equatio 2.3.3, we obtai that X t = ψ j Z t j j=0 where ψ 0 = 1, ψ j = φ + θφ j 1 = 0.5 j 1 for j = 1, 2,.... >From Sectio 2.3, equatio 2.3.5, we get Z t = π j X t j where π 0 = 1, π j = φ + θ θ j 1 = 0.5 j 1 for j = 1, 2,.... Agrees with the results from ITSM. j=0 Exercise 2.12 The give MA1-model is where Z t WN0, 1. Observed that x 100 = The variace of x 100 : X t = Z t 0.6Z t 1 Var[x 100 ] = 1 1 h γh h= = 1 99 γ γ1 = = problemsheet1 August 30, 2007 Side 9
10 That is, 95% codece bouds for µ are approximately x 100 ± = ± = ± = 0.076, Reject H 0 : µ = 0 i favour of the alterative hypothesis H 1 : µ 0 at sigicace level 0.05 sice the 95% bouds for µ do ot iclude the value 0. Note: The coclusio would dier if the time series X t IID0, Exercise 2.13 a Assume a AR1-model X t = φx t 1 + Z t Sice ρh = φ h h > 0 for a AR1-model, ad it has bee observed that ρ2 ˆ = 0.145, we shall assume that φ 2 << 1. Usig Bartlett's formula, the followig approximate relatios are obtaied: ad Var[ ρ1] ˆ 1 1 φ 2 Var[ ρ2] ˆ 1 1 φ φ 2 That is, 95% codece bouds for ρ1 are approximately ρ1 ˆ ± φ 2 Correspodigly, 95% codece bouds for ρ2 are approximately ρ2 ˆ ± φ φ 2 With φ = ˆφ = ρ1, ˆ = 100, ρ1 ˆ = 0.438, ρ2 ˆ = 0.145, these bouds become for ρ1: 0.262, 0.614, ad for ρ2: , These values are ot cosistet with φ = 0.8, sice both ρ1 = 0.8 ad ρ2 = 0.64 are outside these bouds. b Assume a MA1-model X t = Z t + θz t 1 Bartlett's formula gives the followig approximate relatios Var[ ρ1] ˆ 1 1 3ρ ρ1 4 problemsheet1 August 30, 2007 Side 10
11 ad Var[ ρ2] ˆ ρ1 2 That is, 95% codece bouds for ρ1 are approximately ρ1 ˆ ± ρ ρ1 4 Correspodigly, 95% codece bouds for ρ2 are approximately ρ2 ˆ ± ρ1 2 With the umbers as i a, it is ow obtaied that these bouds become for ρ1: 0.290, 0.586, ad for ρ2: , θ = 0.6 leads to ρ1 = θ = , ρ2 = 0. It follows that the codece bouds are cosistet 1+θ 2 with these two values, ad the data are therefore cosistet with the MA1-model X t = Z t + 0.6Z t 1 Exercise 2.14 X t = A cosωt + B siωt, t Z where A ad B are ucorrelated radom variables with zero mea ad variace 1. statioary with ACF ρh = cosωh. This process is a P 1 X 2 = φ 11 X 1 where γ0φ 11 = γ1, which gives φ 11 = ρ1 = cos ω. Hece Also P 1 X 2 = cosωx 1 E[X 2 P 1 X 2 2 ] = γ0 φ 11 γ1 = γ01 cos 2 ω = si 2 ω Note: 2.14 is a example i which the matrix Γ i the equatio Γ φ = γ is sigular for 3. This is because X 3 = 2 cos ωx 2 X 1. b where P 2 X 3 = φ 21 X 2 + φ 22 X 1 γ0φ 21 + γ1φ 22 = γ1 γ1φ 21 + γ0φ 22 = γ2 problemsheet1 August 30, 2007 Side 11
12 that is φ 21 + cos ωφ 22 = cos ω cos ωφ 21 + φ 22 = cos 2ω Solvig these equatios give φ 22 cos 2 ω 1 = cos 2 ω 2 cos 2 ω + 1 = cos 2 ω + 1, that is, φ 22 = 1, ad the, φ 21 = cos ω φ 22 cos ω = 2 cos ω. Hece ad P 2 X 3 = 2 cos ωx 2 X 1 E[X 3 P 2 X 3 2 ] = γ0 φ 2 γ 2 = 1 2 cos ω, 1cos ω, cos 2ω = 1 2 cos 2 ω + cos 2ω = 0 c >From b ad statioarity, it follows that P X +1 X, X 1 = 2 cos ωx X 1 with MSE = 0. Sice 2 cos ωx X 1 is a liear combiatio of X s, < s, ad sice it is impossible to d a predictor of this form with smaller MSE, we coclude that P X +1 = 2 cos ωx X 1 with MSE = 0. Exercise 2.18 Give the MA1 process X t = Z t θz t 1 where θ < 1, ad Z t W N0, σ 2. Represeted as a AR process, it assumes the form Z t = X t + θx t 1 + θ 2 X t Settig t = + 1 i the last equatio ad applyig P to each side, leads to the result P X +1 = θ j X +1 j = θz Predictio error = X +1 P X +1 = Z +1. Hece, MSE = E[Z 2 +1 ] = σ2. j=1 problemsheet1 August 30, 2007 Side 12
13 Exercise 2.19 The give MA1-model is X t = Z t Z t 1 ; where Z t WN0, σ 2. The vector a = a 1,..., a of the coeciets that provide the best liear predictor BLP of X +1 i terms of X = X,..., X 1 satises the equatio Γ a = γ where the covariace matrix Γ = CovX, X ad γ = CovX +1, X = γ1,..., γ. Sice γ0 = 2σ 2, γ1 = σ 2, γh = 0 for h > 1, it follows that Γ = σ 2. t Z ad γ = σ 2 1, 0,..., 0. It ca be show, e.g. by iductio, that the equatios to be solved ca be rewritte as follows a a a 3 1 a 4 = a a 0 The solutio is foud to be give as follows a j = 1 j + 1 j + 1 Hece it is obtaied that P X +1 = j=1 1 j + 1 j + 1 X +1 j The mea square error is E[X +1 P X +1 2 ] = γ0 a γ = 2σ 2 + a 1 σ 2 = σ problemsheet1 August 30, 2007 Side 13
14 Exercise 2.20 We have to prove that CovX ˆX, X j = E[X ˆX X j ] = 0 for j = 1,..., 1. This follows from equatios for suitable values of ad h with a 0 = 0 sice we may assume that E[X ] = 0. This clearly implies that E[X ˆX X k ˆX k ] = 0 for k = 1,..., 1, sice ˆX k is a liear combiatio of X 1,..., X k 1. Exercise 2.21 I this exercise we shall determie the best liear predictor BLP P X 3 W α wrt three dieret vector variables W α, α = a, b, c. Let Γ α = CovW α, W α ad γ α = CovX 3, W α. The give MA1-model is X t = Z t + θz t 1 ; t Z where Z t WN0, σ 2. a I this case we have W a = W 1, W 2 = X 2, X 1. Hece Γ a = σ θ 2 θ θ 1 + θ 2 ad γ a = CovX 3, W a = γ1, γ2 = σ 2 θ, 0. The equatio Γ a a 1, a 2 = γ a, or has the solutio We obtai the BLP The mea square error 1 + θ 2 a 1 + θa 2 = θ θa θ 2 a 2 = 0 a 1 = θ1 + θ2 1 + θ 2 2 θ 2 a 2 = P X 3 X 2, X 1 = θ θ 2 2 θ 2 θ 1 + θ θ 2 2 θ 2 X 2 θx 1 E[X 3 P X 3 X 2, X 1 2 ] = VarX 3 a 1, a 2 γ a = σ θ 2 a 1 σ 2 θ = σ θ 2 θ θ 2 2 θ 2 b problemsheet1 August 30, 2007 Side 14
15 Here W b = W 1, W 2 = X 4, X 5. With this choice, it follows that Γ b = Γ a, ad γ b = γ a. It follows immediately that the BLP is give by P X 3 X 4, X 5 = Ad the mea square error is the same as i a E[X 3 P X 3 X 4, X 5 2 ] = σ θ 2 θ 1 + θ θ 2 2 θ 2 X 4 θx 5 θ θ 2 2 θ 2 c Now, W b = W 1, W 2, W 3, W 4 = X 2, X 1, X 4, X 5. It the follows that Γ c = σ 2 Γa 0 0 Γ a where 0 deotes a 2 2 zero-matrix. Also, γ c = γ a, γ a. Hece, it follows that the solutio to the equatio Γ c a 1,..., a 4 = γ c is give by a 3 = a 1 ad a 4 = a 2, where a 1 ad a 2 are as give i a or b. The BLP is therefore with mea square error P X 3 X 2, X 1, X 4, X 5 = θ 1 + θ θ 2 2 θ 2 [X 2 + X 4 ] θ[x 1 + X 5 ] E[X 3 P X 3 X 2, X 1, X 4, X 5 2 ] = V arx 3 a 1, a 2, a 3, a 4 γ c = σ θ 2 2a 1 σ 2 θ = σ θ 2 2θ θ 2 2 θ 2 d See above. Exercise 2.22 We shall determie the best liear predictor BLP P X 3 W α wrt three dieret vector variables W α, α = a, b, c. Let Γ α = CovW α, W α ad γ α = CovX 3, W α. The give causal statioary AR1-model is X t = φx t 1 + Z t ; t Z where Z t WN0, σ 2. Causality implies that φ < 1. Hece, the ACVF γh = σ 2 1 φ 2 1 φ h. a I this case we have W a = W 1, W 2 = X 2, X 1. Hece Γ a = σ2 1 φ 1 φ 2 φ 1 problemsheet1 August 30, 2007 Side 15
16 ad γ a = CovX 3, W a = γ1, γ2 = σ2 φ, φ 2. The equatio Γ 1 φ 2 a a 1, a 2 = γ a, or a 1 + φa 2 = φ has the solutio We obtai the BLP The mea square error φa 1 + a 2 = φ 2 a 1 = φ a 2 = 0 P X 3 X 2, X 1 = φx 2 E[X 3 P X 3 X 2, X 1 2 ] = VarX 3 a 1, a 2 γ a = σ2 1 φ 2 σ2 φ 2 1 φ 2 = σ2 b Here W b = W 1, W 2 = X 4, X 5. With this choice, it follows that Γ b = Γ a, ad γ b = γ a. It follows immediately that the BLP is give by Ad the mea square error is P X 3 X 4, X 5 = φx 4 E[X 3 P X 3 X 4, X 5 2 ] = V arx 3 a 1, a 2 γ b = σ2 1 φ 2 σ2 φ 2 1 φ 2 = σ2 c Now, W c = W 1, W 2, W 3, W 4 = X 2, X 1, X 4, X 5. It the follows that Γ c = σ2 1 φ 2 1 φ φ 2 φ 3 φ 1 φ 3 φ 4 φ 2 φ 3 1 φ φ 3 φ 4 φ 1 where γ c = γ a, γ a. Hece, the followig set of equatios is obtaied a 1 + φa 2 + φ 2 a 3 + φ 3 a 4 = φ φa 1 + a 2 + φ 3 a 3 + φ 4 a 4 = φ 2 φ 2 a 1 + φ 3 a 2 + a 3 + φa 4 = φ φ 3 a 1 + φ 4 a 2 + φa 3 + a 4 = φ 2 It is see that the rst two equatios give a 2 = 0, while the last two equatios give a 4 = 0. The it is foud that a 1 = a 3 = φ 1 + φ 2 problemsheet1 August 30, 2007 Side 16
17 The BLP is therefore with mea square error P X 3 X 2, X 1, X 4, X 5 = φ 1 + φ 2 [X 2 + X 4 ] E[X 3 P X 3 X 2, X 1, X 4, X 5 2 ] = V arx 3 a 1, a 2, a 3, a 4 γ c = = σ2 1 + φ 2 σ2 1 φ 2 σ2 2φ 2 1 φ φ 2 d See above. problemsheet1 August 30, 2007 Side 17
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