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Spectral density of a stationary ARMA solution

Statement

Assume Countable Choice (The Axiom of Countable Choice (ACω)).

Let Xt−μ be a real weakly stationary solution of the normalized coprime ARMA(p,q) equation ϕ(B)(Xt−μ)=θ(B)ϵt, where ϵ is centered white noise with variance σ2>0. If ϕ(z)≠0 for every ∣z∣=1, then X has a spectral density, under the convention γ(h)=∫TeihλF(dλ) and angular Lebesgue measure dλ of total mass 2π, given by f(λ)=σ22π∣θ(e−iλ)∣2∣ϕ(e−iλ)∣2,−π≤λ≤π. No causal or invertible root condition is required; zeros of θ on the unit circle are permitted.

Facts & Assumptions

Given: Countable Choice; the normalized ARMA equation, a real weakly stationary solution, centered white noise with variance σ2>0, and no zero of ϕ on the unit circle.

[A1]

The Axiom of Countable Choice (ACω) is assumed for the angular Lebesgue measure and Polish-space regularity steps below.

[F1]

Linear processes, white noise, and ARMA models: the equation uses the backshift and normalized real polynomials; white noise is centered, finite-variance, and uncorrelated at distinct times.

[F2]

Spectral measures and the known-mean periodogram: with T=R/(2πZ), a spectral measure represents γ(h) by ∫eihλF(dλ); a spectral density f means F(dλ)=f(λ)dλ, where angular Lebesgue measure has total mass 2π.

[F3]

Absolutely summable linear filters preserve weak stationarity: two-sided ℓ1 filters are well-defined in L2, compose by convolution, and transform covariance through their coefficients.

[F4]

Radon measure on an LCH space: a finite Borel measure on the compact circle is Radon when it is outer regular on Borel sets and inner regular on open sets.

[F5]

Tonelli's theorem for double series of nonnegative extended real numbers permits interchanging the order of summation for absolutely summable coefficient products.

[F6]

Every nonconstant polynomial in C[x] splits into linear factors: every nonconstant complex polynomial factors into linear terms over C.

[F8]

Under Countable Choice, every Borel probability measure on a Polish space is inner regular on all Borel sets (Assuming countable choice, Borel probability measures on Polish spaces are inner regular).

[F9]

If f is nonnegative and Borel measurable, then E↦∫Ef dλ is a Borel measure (The indefinite integral of a nonnegative measurable function is a measure).

[F10]

The nonnegative integral is monotone and homogeneous (Monotonicity and nonnegative homogeneity of the nonnegative integral).

[F11]

A probability measure is a measure of total mass one (Probability measures and probability spaces).

Proof

Proof technique: invert ϕ by a two-sided absolutely summable Laurent filter, compute the filtered white-noise covariance, and identify its Fourier density directly.

1.1F1F3F6givenconstruct

By [F6], factor ϕ over C as ϕ(z)=∏ℓ=1m(1−z/rℓ), with multiplicities and ϕ(0)=1; if m=0, take the empty product 1. None of the roots satisfies ∣rℓ∣=1. For a root with ∣rℓ∣>1 use (1−z/rℓ)−1=∑j≥0rℓ−jzj on the unit circle. For a root with ∣rℓ∣<1 use (1−z/rℓ)−1=−∑j≥1rℓjz−j there. Each coefficient sequence is in ℓ1(Z), so their finite convolution and multiplication by θ give a two-sided sequence h∈ℓ1(Z) with ∑k∈Zhkzk=θ(z)ϕ(z),∣z∣=1. Conjugate roots occur in pairs for real ϕ,θ. For each conjugate pair the finite convolution coefficient is unchanged by complex conjugation after reversing its summation index; real roots have real coefficients. Multiplication by real θ preserves reality.

1.2A1F2F4F7F8F11algebra

The angular measure dλ is a finite Borel measure of total mass 2π by [F2]. Normalize it to P=dλ/(2π), a Borel probability by [F11]. By [F7], T is Polish, so [F8] gives compact inner approximations for every Borel set under P; scaling by 2π gives the same property for dλ. For any Borel E, apply this inner approximation to T∖E: for every η>0 there is compact K⊆T∖E with dλ((T∖E)∖K)<η. Then U=T∖K is open, contains E, and dλ(U∖E)<η. Thus dλ is outer regular on Borel sets and inner regular on open sets; it is finite on compact sets because its total mass is finite. Hence dλ is Radon by [F4].

2.1F1F3step 1.1algebra

Write a(z)=1/ϕ(z) for the two-sided absolutely convergent series constructed in step 1.1. Its product with ϕ is the constant series 1. Applying filter composition [F3] to the ARMA equation gives Xt−μ=a(B)ϕ(B)(Xt−μ)=a(B)θ(B)ϵt=∑k∈Zhkϵt−k. Thus every weakly stationary solution under the stated root condition is the same two-sided filtered white-noise process.

2.2F2F4F9F10step 1.1step 1.2algebra

Define f(λ):=σ22π∣∑k∈Zhke−ikλ∣2=σ22π∣θ(e−iλ)∣2∣ϕ(e−iλ)∣2. The series is uniformly convergent because h∈ℓ1, so f is continuous, periodic, and nonnegative. It is bounded on the compact circle; therefore F(E):=∫Ef dλ is a finite positive Borel measure by [F9] and [F10]. For any Borel E and ε>0, outer regularity of dλ gives an open U⊃E with dλ(U∖E)<ε/(∥f∥∞+1). Since F(U)=F(E)+F(U∖E) and F(U∖E)≤∥f∥∞dλ(U∖E), this proves outer regularity of F. For any open O, inner regularity of dλ gives a compact K⊂O with dλ(O∖K)<ε/(∥f∥∞+1); the same bound on F(O∖K) proves inner regularity of F on open sets. Thus F is Radon by [F4].

3.1F1F3step 2.1

Applying the covariance formula in [F3] to the representation in step 2.1 and using Cov⁡(ϵs,ϵt)=σ2 when s=t and 0 otherwise gives γ(h)=σ2∑k∈Zhk+hhk. The sum is absolutely convergent because h∈ℓ1(Z).

4.1F2F5step 3.1step 2.2algebra∎

Put HN(λ):=∑∣k∣≤Nhke−ikλ. Since h∈ℓ1, HN converges uniformly to H(λ):=∑khke−ikλ. The sequence HN is uniformly bounded, so ∣HN∣2 converges uniformly to ∣H∣2. Expanding each finite square gives ∣HN(λ)∣2=∑∣j∣,∣k∣≤Nhjhke−i(j−k)λ, and [F5] gives ∑j,k∣hjhk∣=(∑k∣hk∣)2<∞. Hence the finite squares also converge uniformly to the full double series. Integration against eihλ preserves this uniform limit because the circle has finite measure. For each integer h, ∫Teihλf(λ) dλ=σ2∑j,khjhk12π∫−ππei(h−j+k)λ dλ=σ2∑khk+hhk=γ(h), where the middle integral is 1 when h−j+k=0 and 0 otherwise, and the last equality is step 3.1. Hence F represents the autocovariance and f is a spectral density under [F2].

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