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Spectral measures and periodogram bias

1 · Prerequisites

2 · Summary

For a real weakly stationary sequence on Z, the covariance sequence is positive definite. Under AC and DC, the LCA Bochner theorem gives its unique finite positive spectral measure on the circle; absolute summability of the autocovariance gives a continuous nonnegative density in the angular-frequency convention fixed here. The known-mean periodogram has an exact finite-sample Fejér convolution expectation. A kernel-smoothed periodogram is asymptotically unbiased at a continuity point under the stated bandwidth and grid conditions. The angular Lebesgue and smoothing branch assumes Countable Choice in this library; the finite expectation and supplied-measure convolution identities do not use a choice axiom. AC and DC remain the separate hypotheses for spectral measure existence. These arguments do not supply a raw-periodogram CLT, variance consistency, or interval coverage.

3 · Logical flowchart

4 · Definitions, theorems and proofs

TheoremStatement: Literature-sourcedProof: AI-adaptedjudge pass (gpt-6-luna)Open item page →

The Pontryagin dual of the discrete integers is the circle

Statement

Let Z have the discrete topology, let T={z∈C:∣z∣=1} have the subspace topology from C, and give Z^ the compact-open topology of the Pontryagin dual. Then Ψ:T⟶Z^,Ψ(z)(n):=zn, is a group isomorphism and a homeomorphism. Thus in the angular coordinate z=eiλ, the characters of Z are exactly n↦einλ, with λ taken modulo 2π.

Facts & Assumptions

Given: The additive integer group with the discrete topology, the multiplicative unit circle, and the Pontryagin dual with its compact-open topology.

[F1]

The Pontryagin dual with the compact-open topology: Z^ is the set of continuous group homomorphisms Z→T, with pointwise multiplication and the compact-open topology.

[F2]

The integers as equivalence classes of pairs of naturals, Arithmetic on the integers, The integers form a commutative ring, and The discrete, indiscrete, cofinite, cocountable, particular-point and Sierpinski topologies: the integers form an additive group, and the discrete topology makes every subset open; every map from a discrete space is continuous.

[F3]

Monoid homomorphism and group homomorphism: a group homomorphism preserves multiplication.

[F6]

C=R[x]/(x2+1) is a field, every element is uniquely a+bi, and every nonzero element has inverse (a−bi)/(a2+b2) and Conjugation is an involutive real-field automorphism, zz‾=∣z∣2, and modulus is definite, multiplicative, and subadditive: multiplication on C is commutative, ∣zw∣=∣z∣∣w∣, zz‾=∣z∣2, and conjugation is an involution. Hence ∣z‾∣2=z‾ z=zz‾=∣z∣2, and nonnegative moduli give ∣z‾∣=∣z∣. Thus for ∣z∣=1, zz‾=1 and ∣z‾∣=1, so the unit circle is an abelian group under multiplication with inverse z−1=z‾.

[F7]

On a discrete domain the compact-open topology is the topology of pointwise convergence and The topology of pointwise convergence on YX, which is the product topology, and its restriction to C(X,Y): when the domain is discrete, the compact-open topology on a family of functions is the subspace topology from the product TZ.

[F9]

Homeomorphism, open map, closed map, embedding, and what it means for a property to be topological: a homeomorphism is a continuous bijection with continuous inverse.

[F11]
[F12]

t↦(cos⁡t,sin⁡t) is a bijection from [0,2π) onto the real unit circle: under z=x+iy↔(x,y), every point of T has the form exp⁡(iλ) for a unique λ∈[0,2π).

Proof

Proof technique: Identify each character by its value at 1, then verify continuity in the pointwise topology.

1.1F1F2F3F4

For z∈T, define χz(n):=zn. By [F4], χz(m+n)=zm+n=zmzn=χz(m)χz(n), so χz is a group homomorphism by [F3]. It is continuous because its domain Z is discrete by [F2]. Hence χz∈Z^ by [F1], and Ψ(z)=χz is well defined.

1.2F5F6F8algebra

Multiplication on T is continuous. For z,w,z0,w0∈T, ∣zw−z0w0∣≤∣z∣ ∣w−w0∣+∣w0∣ ∣z−z0∣=∣w−w0∣+∣z−z0∣. Thus a product-neighborhood restricting both coordinates within ε/2 of (z0,w0) maps inside the ε-ball about z0w0. Inversion is continuous because z−1=z‾ by [F6] and ∣z‾−w‾∣=∣z−w∣ by conjugation preserving modulus [F6].

2.1F1F2F3F4F6step 1.1

The map Ψ is a group homomorphism: for z,w∈T and n∈Z, (zw)n=znwn because T is abelian, by [F4] and [F6]. It is injective since Ψ(z)(1)=z. If χ∈Z^, put z:=χ(1)∈T. First χ(0)=χ(0)2; since χ(0)∈T is invertible, χ(0)=1. Repeatedly applying the homomorphism law then gives χ(n)=zn for n≥0. Thus for n>0, χ(n)χ(−n)=χ(0)=1, so χ(−n)=z−n; hence χ=Ψ(z) and Ψ is surjective.

2.2F5F7F8F10step 1.2

Each coordinate map z↦Ψ(z)(n)=zn is continuous: for n=0 it is constant; for n>0 it is a finite composite of the continuous multiplication map from step 1.2 and the identity; for n<0 it is a finite composite followed by the continuous inverse map from step 1.2. Composite continuity follows from [F10]. A basic open set in the product TZ restricts only finitely many coordinates by [F8], so its preimage is a finite intersection of open coordinate preimages. Thus Ψ is continuous into the product, and [F7] gives continuity into the subspace Z^.

3.1F7F8F9step 2.1

The inverse map sends χ to χ(1). Evaluation at coordinate 1 is continuous as the restriction of a product projection by [F7]–[F8], and Ψ(χ(1))=χ by step 2.1. Thus the inverse is continuous; Ψ is a homeomorphism by [F9], and with step 2.1 it is the asserted group isomorphism.

4.1F4F11F12step 1.1∎

By [F12], every z∈T has a unique angle λ modulo 2π with z=eiλ, using [F11]. The exponential addition law [F11] and integer-power laws [F4] give Ψ(eiλ)(n)=(eiλ)n=einλ for every n∈Z.

DefinitionDefinition: Literature-sourcedProof: Not applicableOpen item page →

Spectral measures and the known-mean periodogram

Definition

Assume Countable Choice (The Axiom of Countable Choice (ACω)) for the angular Lebesgue measure clause below; under it λ1 is a measure and intervals have their usual lengths (Assuming countable choice, L(Rn) is a sigma-algebra containing every elementary set and λn is a complete measure extending elementary volume, A box in Rn with parameters ai≤bi is Lebesgue measurable of measure ∏i<n(bi−ai), whichever of its faces are included).

Let T:={z∈C:∣z∣=1} be the multiplicative unit circle with its subspace topology from C (The multiplicative unit circle is a compact metrizable topological abelian group), identified with angular frequencies by z=eiλ. Write dλ for the finite angular Lebesgue Borel measure obtained from ordinary Lebesgue measure on [−π,π); thus dλ(T)=2π, and the endpoints −π and π denote the same point. The characters of discrete additive Z are h↦eihλ by The Pontryagin dual of the discrete integers is the circle.

For angular representatives λ,θ∈R, define their circular distance by dT(λ,θ):=min⁡k∈Z∣λ−θ+2πk∣. The minimum exists because the displayed absolute values tend to infinity with ∣k∣, and changing either representative by 2π leaves it unchanged.

Precisely, for Borel B⊆T put dλ(B):=λ1{θ∈[−π,π):eiθ∈B}. The map θ↦eiθ is continuous by The complex exponential is entire and its complex derivative is itself and Complex differentiability at a point implies continuity there, so the preimage is Borel by A continuous map has Borel preimages of Borel sets. Preimages preserve disjoint unions, so this is a finite Borel measure under the stated Countable Choice premise. Integrating a Borel function of the circle angle means integrating its periodic lift over this half-open interval.

Let X=(Xt)t∈Z be a real weakly stationary process with mean μ and autocovariance γ(h)=Cov⁡(Xt+h,Xt) (Strict and weak stationarity for integer-indexed time series, Autocovariance and sample autocovariance). A spectral measure for X is a finite positive Radon Borel measure F on T (Radon measure on an LCH space) satisfying γ(h)=∫Teihλ F(dλ)(h∈Z). Putting h=0 gives F(T)=γ(0). A spectral density is a nonnegative measurable function f with F(B)=∫Bf(λ) dλ for every Borel set B⊆T; it exists only when F is absolutely continuous with respect to dλ and is unique up to dλ-null sets.

For a known mean μ and integer n≥1, the known-mean periodogram is the random function In(λ):=12πn∣∑t=1n(Xt−μ)e−itλ∣2,λ∈T. The centering uses the population mean, not the sample mean. The finite sum is square-integrable, so In(λ) has finite expectation: for Yt=Xt−μ, the pointwise inequality ∣∑t=1nYte−itλ∣2≤n∑t=1n∣Yt∣2 bounds its expectation by n∑t=1nE[Yt2]<∞ (Moments, variance, and covariance on a probability space).

Define the normalized finite-sample Fejér kernel Φn(x):=12πn∣∑t=1ne−itx∣2,x∈T.

For smoothing, let K:R→[0,∞) be continuous with compact support, ∫RK(u) du=1, and K(0)>0. For bandwidth b>0, periodize it on the circle by Kb(x):=∑k∈Z1bK ⁣(x+2πkb). Compact support makes this a finite sum at each x, and changing variables over the intervals that tile R gives ∫TKb(x) dλ(x)=1. Fix a sequence of bandwidths (bn)n≥1 with bn>0. For the Fourier grid λj,n=2πj/n (0≤j<n) and target λ0∈T, when the denominator is positive define wn,j(λ0):=Kbn(λ0−λj,n)∑ℓ=0n−1Kbn(λ0−λℓ,n),f~n(λ0):=∑j=0n−1wn,j(λ0)In(λj,n). The weights are nonnegative and sum to one.

Remarks

The finite-sum bound used in the finite-expectation paragraph follows directly. Put Zt:=Yte−itλ. Since ∣e−itλ∣=1 by exp⁡(x+iy)=ex(cos⁡y+isin⁡y), ∣exp⁡(x+iy)∣=ex, and eiπ+1=0, ∑t=1n∣Zt∣2=∑t=1n∣Yt∣2. Expanding the squares gives n∑t=1n∣Zt∣2−∣∑t=1nZt∣2=∑1≤s<t≤n∣Zs−Zt∣2≥0, so ∣∑t=1nYte−itλ∣2≤n∑t=1n∣Yt∣2.

LemmaStatement: Literature-sourcedProof: AI-adaptedOpen item page →

Integer exponentials are orthogonal for angular Lebesgue measure

Statement

Assume Countable Choice. For every integer k, with the unnormalized angular Lebesgue measure dλ on T from Spectral measures and the known-mean periodogram, ∫Teikλ dλ={2π,k=0,0,k≠0.

Facts & Assumptions

Given: Countable Choice, an integer k, and angular Lebesgue measure dλ on the circle, integrated over one period [−π,π).

[A1]

The Axiom of Countable Choice (ACω): the Countable Choice assumption required for the angular Lebesgue measure and its interval values.

[F1]

Spectral measures and the known-mean periodogram: dλ is ordinary Lebesgue measure on one angular period, with total mass 2π.

[F3]

The nonnegative Lebesgue integral, The integral of a nonnegative simple function, and Monotonicity and nonnegative homogeneity of the nonnegative integral: the Lebesgue integral is monotone on nonnegative functions, and the integral of a nonnegative step function is the sum of its values times the measures of its intervals.

[F5]

The Lebesgue integral is linear on L1(μ): the Lebesgue integral is linear on integrable real functions.

[F7]

The zero sets of sine and cosine and the least positive common period 2 pi and Parity and the Pythagorean identity for sine and cosine: for integer k≠0, sin⁡(kπ)=sin⁡(−kπ)=0 and cos⁡(kπ)=cos⁡(−kπ).

[F8]

exp⁡(x+iy)=ex(cos⁡y+isin⁡y), ∣exp⁡(x+iy)∣=ex, and eiπ+1=0 and Integrable real and complex functions, and their integrals: eikλ=cos⁡(kλ)+isin⁡(kλ), and the integral of a complex function is the integral of its real part plus i times that of its imaginary part.

[F10]

A continuous map has Borel preimages of Borel sets: the preimage of every Borel set under a continuous map is Borel; in particular the continuous real function in step 1.1 is Borel measurable.

[F11]

For every ε>0 in a complete ordered field there is a natural n≥1 with 1/n<ε: for every δ>0 there is an N≥1 with 2π/N<δ.

Proof

Proof technique: compare the Lebesgue integral with Darboux sums, then use the fundamental theorem of calculus.

1.1A1F1F2F3F4F5F9F10F11given

Let g:[−π,π]→R be continuous and bounded by B≥0. For each N≥1, partition the interval into the fixed equal pieces Jj,N=[−π+2πj/N,−π+2π(j+1)/N), with the endpoint π added to the last piece. On each piece take the infimum and supremum of g+B on its closed hull and use them as the constant values of simple step functions ℓN and uN. They satisfy 0≤ℓN≤g+B≤uN. By [F3]–[F4], under [A1], their Lebesgue integrals with respect to λ1 are exactly the lower and upper Darboux sums of g+B for this partition. Given η>0, uniform continuity [F2] gives δ>0 such that the oscillation on any interval of length below δ is less than η. Choose N with 2π/N<δ by [F11]; then every oscillation is below η, and the gap between the sums is at most 2πη. Hence that gap tends to zero. Monotonicity of the Lebesgue integral places ∫[−π,π)(g+B) dλ1 between the same sums; the Darboux integral of g+B lies between them as well. Therefore the Lebesgue and Darboux integrals of g+B agree. By linearity [F5] and λ1([−π,π))=2π from [F1], the two integrals of g agree.

1.2A1F1given

If k=0, the integrand is the constant 1, so its integral is 2π by [F1] under [A1].

2.1A1F1F2F6F7F8step 1.1

If k≠0, apply step 1.1 to the continuous real functions g(λ)=cos⁡(kλ) and g(λ)=sin⁡(kλ). Their antiderivatives are respectively sin⁡(kλ)/k and −cos⁡(kλ)/k by [F6] and the chain rule. Their endpoint differences vanish by [F7], so both Lebesgue integrals on [−π,π) are zero. These functions are 2π-periodic; by the angular integral definition [F1], their integrals on T are those same integrals over [−π,π). Euler's identity and the complex-integral convention [F8] give ∫Teikλ dλ=0.

3.1givenstep 1.2step 2.1algebra∎

The cases k=0 and k≠0 exhaust the integers, and steps 1.2 and 2.1 give the stated formula.

TheoremStatement: Literature-sourcedProof: AI-adaptedOpen item page →

Herglotz spectral measure and the summable-covariance density

Statement

Assume the Axiom of Choice and Dependent Choice. Let X=(Xt)t∈Z be a real weakly stationary process with mean μ and autocovariance γ(h)=Cov⁡(Xt+h,Xt). Then there is a unique finite positive Radon measure F on T such that γ(h)=∫Teihλ F(dλ)(h∈Z), and F(T)=γ(0). This is the spectral measure in Spectral measures and the known-mean periodogram.

If, in addition, ∑h∈Z∣γ(h)∣<∞, then F is absolutely continuous with respect to angular Lebesgue measure dλ and has the continuous nonnegative density f(λ)=12π∑h∈Zγ(h)e−ihλ,λ∈T. No mixing, central limit theorem, raw-periodogram consistency, or interval coverage follows from these conclusions.

Facts & Assumptions

Given: The Axiom of Choice, Dependent Choice, and a real weakly stationary process X=(Xt)t∈Z with mean μ and autocovariance γ(h)=Cov⁡(Xt+h,Xt). For the density conclusion also assume ∑h∈Z∣γ(h)∣<∞.

[F1]

Strict and weak stationarity for integer-indexed time series: every Xt has finite second moment, E[Xt]=μ is constant, and Cov⁡(Xt+h,Xt) is independent of t.

[F2]

Autocovariance and sample autocovariance: the autocovariance function is γ(h)=Cov⁡(Xt+h,Xt) for h∈Z.

[F3]

Positive definite functions on an abelian group: a function ϕ:G→C is positive definite when every finite quadratic form ∑j,kcjck‾ϕ(xj−xk) is a nonnegative real number.

[F4]

Bochner's theorem for LCA groups: assuming AC and DC, a continuous positive-definite function on a locally compact Hausdorff abelian group G has a unique finite positive Radon representing measure on G^, with total mass ϕ(0) and representation ϕ(x)=∫G^χ(x) dF(χ).

[F5]

The Pontryagin dual of the discrete integers is the circle: for discrete additive Z, the dual characters are χλ(h)=eihλ under λ↦eiλ.

[F6]

Radon measure on an LCH space: a Radon Borel measure is finite on compact sets, outer regular on Borel sets, and inner regular on open sets.

[F7]

The Borel sigma-algebra of a topological space and Homeomorphism, open map, closed map, embedding, and what it means for a property to be topological: a homeomorphism and its inverse carry Borel sets to Borel sets, since they carry open sets to open sets.

[F8]

Every nonnegative measurable function is the increasing limit of simple measurable functions and Monotone convergence for the integral: nonnegative Borel functions are increasing limits of simple functions, and their integrals pass to that limit.

[F9]

Integrable real and complex functions, and their integrals and Monotonicity and nonnegative homogeneity of the nonnegative integral: bounded real or complex Borel functions are integrable against a finite measure, and complex integrals are defined from the real and imaginary parts.

[F11]

Integer exponentials are orthogonal for angular Lebesgue measure: for each integer k, ∫Teikλ dλ=2π if k=0 and is 0 otherwise.

[F12]

The indefinite integral of a nonnegative measurable function is a measure: for nonnegative measurable f, the set function B↦∫Bf dλ is a measure.

[F13]

The Lebesgue integral is linear on L1(μ): integrals of integrable real functions are linear; together with [F9] this justifies integrating the real and imaginary parts separately.

[F14]

A uniform limit of continuous complex-valued functions is continuous: a uniform limit of continuous complex functions is continuous.

[A1]

The Axiom of Choice: every family of nonempty sets has a choice function; this is assumed in the Bochner theorem [F4].

Proof

technique · Apply Bochner's theorem to the covariance sequence, then construct and identify the density directly by absolutely convergent Fourier series
1.1F1F2F3givenalgebra

For any finite integers h1,…,hm and complex numbers c1,…,cm, finite second moments make ∑jcj(Xhj−μ) square-integrable. Expanding its squared modulus and using stationarity gives ∑j,k=1mcjck‾γ(hj−hk)=E∣∑j=1mcj(Xhj−μ)∣2≥0. Thus γ is positive definite on the additive group Z by [F1]–[F3].

1.2F14givenalgebra

Now assume absolute summability and define f(λ)=(2π)−1∑h∈Zγ(h)e−ihλ. For every N, the tail is bounded uniformly in λ by (2π)−1∑∣h∣>N∣γ(h)∣, which tends to zero; finite partial sums are continuous, so the series converges uniformly to a continuous function f by [F14].

1.3F3givenalgebra

For each N≥0, set fN(λ):=12π∑∣h∣≤N(1−∣h∣N+1)γ(h)e−ihλ. Using cj=e−ijλ in [F3] for j=0,…,N and grouping pairs by h=j−k gives fN(λ)=12π(N+1)∑j,k=0Ne−i(j−k)λγ(j−k)≥0. Moreover, 2π∣fN(λ)−f(λ)∣≤∑∣h∣>N∣γ(h)∣+∑∣h∣≤N∣h∣N+1∣γ(h)∣, and the right side tends to zero: first make the absolutely summable tail small, then let N→∞ on the remaining finite sum. Thus fN→f uniformly, so f is real and nonnegative.

2.1F4F5F6F7F8F9F15A1A2step 1.1given

Give Z the discrete topology. Every singleton is compact and open, distinct integers have disjoint open singleton neighborhoods, and maps from a discrete space are continuous; hence Z is a locally compact Hausdorff abelian topological group and γ is continuous. By [F4] and step 1.1, there is a unique finite positive Radon measure M on Z^ with γ(h)=∫Z^χ(h) M(dχ),M(Z^)=γ(0). Use the homeomorphism Ψ:T→Z^ from [F5] and define F(B):=M(Ψ(B)) for Borel B⊆T. Images of Borel sets are Borel by [F7], so this is a finite positive Borel measure. It is finite on compact sets because Ψ maps compact sets to compact sets. For Borel E⊆T, outer regularity of M gives an open V⊇Ψ(E) with M(V)<M(Ψ(E))+ε; then U=Ψ−1(V) is open, contains E, and F(U)=M(V). For open O⊆T, inner regularity gives compact C⊆Ψ(O) with M(C)>M(Ψ(O))−ε; then K=Ψ−1(C) is compact in O and F(K)=M(C). Thus F is Radon by [F6]. For any nonnegative Borel q on Z^, ∫Tq(Ψ(z)) F(dz)=∫Z^q(χ) M(dχ). Indeed the identity holds for indicators by the definition of F, hence for nonnegative simple functions by finite addition, and for nonnegative Borel functions by increasing simple approximation and monotone convergence [F8]. It also holds for bounded complex Borel q by applying that argument to its positive and negative real and imaginary parts; these are integrable by [F9] because both measures are finite. Taking q(χ)=χ(h) and using [F5] proves γ(h)=∫TeihλF(dλ). Also F(T)=M(Z^)=γ(0). If another finite positive Radon measure G on T represents γ, let G~(E):=G(Ψ−1(E)) for Borel E⊆Z^. It is finite on compact sets because Ψ−1 maps compact sets to compact sets. For Borel E, outer regularity of G gives open U⊇Ψ−1(E) with G(U)<G(Ψ−1(E))+ε; then Ψ(U) is open, contains E, and G~(Ψ(U))=G(U). For open V⊆Z^, inner regularity gives compact K⊆Ψ−1(V) with G(K)>G(Ψ−1(V))−ε; then Ψ(K) is compact in V and G~(Ψ(K))=G(K). Thus G~ is Radon by [F6] and [F7]. The same indicator/simple/monotone argument gives ∫Z^q(χ) G~(dχ)=∫Tq(Ψ(z)) G(dz) for bounded Borel q. Taking q(χ)=χ(h) shows G~ represents γ; uniqueness in [F4] gives G~=M. Hence G(B)=G~(Ψ(B))=M(Ψ(B))=F(B) for every Borel B⊆T. The integrands χ↦χ(h) are Borel by [F15], so the representing integrals are defined.

3.1F1F4F6F9F10F11F12F13A1A2givenstep 1.2step 1.3step 2.1algebra∎

Put sN(λ):=(2π)−1∑∣k∣≤Nγ(k)e−ikλ. Absolute summability gives ∥sN−f∥∞→0. For N≥∣h∣, the finite sum and [F11] give ∫TeihλsN(λ) dλ=12π∑∣k∣≤Nγ(k)∫Tei(h−k)λ dλ=γ(h). If qN=eihλ(f−sN) and τN=∥f−sN∥∞, then ∣Re⁡qN∣,∣Im⁡qN∣≤τN. By [F9], [F13], and dλ(T)=2π, the integrals of the real and imaginary parts each lie between −2πτN and 2πτN: subtracting either part from the constant τN or adding it to that constant gives a nonnegative function, so monotonicity and linearity apply. The complex integral is the real-part integral plus i times the imaginary-part integral [F9], hence its modulus is at most the sum of the two absolute values, 4πτN. This tends to zero. Hence ∫Teihλf(λ) dλ=γ(h). By [F12], ν(B):=∫Bf dλ is a measure; it is finite and positive since 0≤f≤∥f∥∞ and dλ(T)=2π. It is Radon: for a Borel E, outer regularity of dλ [F10] gives open U⊇E with dλ(UsetminusE)<ε/(∥f∥∞+1), hence ν(UsetminusE)<ε. Countable additivity [F12] then yields ν(U)<ν(E)+ε. For open O, inner regularity gives compact K⊆O with dλ(OsetminusK)<ε/(∥f∥∞+1), hence ν(OsetminusK)<ε and ν(O)<ν(K)+ε by [F12]. These are the regularity clauses in [F6]. Thus ν is a finite positive Radon measure representing γ, and uniqueness in [F4] gives F=f dλ, with the stated density.

TheoremStatement: Literature-sourcedProof: AI-adaptedOpen item page →

Expected periodogram and smoothed spectral-density bias

Statement

Let X=(Xt)t∈Z be a real weakly stationary process with known mean μ and autocovariance γ. For every n≥1 and λ∈T, its known-mean periodogram from Spectral measures and the known-mean periodogram satisfies E[In(λ)]=12π∑∣h∣<n(1−∣h∣n)γ(h)e−ihλ. If F is any spectral measure for X, this is equivalently the finite-sample Fejér convolution E[In(λ)]=∫TΦn(λ−θ) F(dθ). If F(dθ)=f(θ) dθ, the right side is ∫TΦn(λ−θ)f(θ) dθ.

The finite expectation identity and the convolution identity for a supplied spectral measure use no choice principle. Assume Countable Choice for the angular Lebesgue measure and the smoothing conclusion below, as required by the measure convention in Spectral measures and the known-mean periodogram.

For the smoothing definition in Spectral measures and the known-mean periodogram, suppose F(dθ)=f(θ) dθ, where f∈L1(T,dλ) is continuous at λ0. Let K be as defined there and let bn>0 satisfy bn→0 and nbn→∞. Then the weights are defined for all sufficiently large n, and E[f~n(λ0)]⟶f(λ0). This is a bias limit for the smoothed estimator; it asserts no variance consistency, raw-periodogram consistency, central limit theorem, or interval coverage.

Facts & Assumptions

Given: A real weakly stationary process X=(Xt)t∈Z with known mean μ and autocovariance γ. For the spectral-measure identity assume a finite positive Radon measure F satisfies γ(h)=∫TeihθF(dθ). For the smoothing conclusion also assume Countable Choice, F(dθ)=f(θ)dθ, with f integrable and continuous at λ0, and bn>0, bn→0, nbn→∞.

[F1]

Spectral measures and the known-mean periodogram: the known-mean periodogram is In(λ)=(2πn)−1∣∑t=1n(Xt−μ)e−itλ∣2.

[F2]

Strict and weak stationarity for integer-indexed time series and Autocovariance and sample autocovariance: each Xt has finite second moment, the mean is μ, and covariance depends only on the lag with γ(h)=Cov⁡(Xt+h,Xt).

[F3]

Moments, variance, and covariance on a probability space: covariance of square-integrable real variables is the expectation of their centered product.

[F4]

Spectral measures and the known-mean periodogram: a supplied spectral measure has Fourier coefficients γ(h), and F=f dλ means f is a spectral density.

[F5]

Spectral measures and the known-mean periodogram: the normalized finite-sample Fejér kernel is Φn(x)=(2πn)−1∣∑t=1ne−itx∣2.

[F6]

Spectral measures and the known-mean periodogram: the periodized kernel and the finite normalized Fourier-grid weights define f~n(λ0) when their denominator is positive.

[F7]

Integer exponentials are orthogonal for angular Lebesgue measure: under the unnormalized angular measure, ∫Teikx dx=2π for k=0 and 0 for nonzero integer k.

[A1]

The Axiom of Countable Choice (ACω): Countable Choice supplies the angular Lebesgue measure and orthogonality fact [F7], and is used only in the density/smoothing branch.

[F8]

Linearity, monotonicity, and the modulus bound for expectation: expectation is linear for finite sums of integrable random variables and deterministic weights.

[F10]

Signs, monotonicity intervals, and ranges of sine and cosine and The derivatives of sine and cosine are cosine and minus sine: for 0<δ<π and δ≤∣x∣≤π, ∣sin⁡(x/2)∣≥sin⁡(δ/2)>0.

[F11]

Spectral measures and the known-mean periodogram: circular angular distance is dT(λ,θ)=min⁡k∈Z∣λ−θ+2πk∣.

Proof

Proof technique: Expand the finite square exactly, then use the nonnegative Fejér kernels as an approximate identity on the circle.

1.1F1F2F3givenalgebra

Expand the finite squared modulus in In(λ). By [F2] and [F3], E[(Xt−μ)(Xs−μ)]=γ(t−s), so E[In(λ)]=12πn∑t=1n∑s=1nγ(t−s)e−i(t−s)λ. All sums are finite and all terms integrable.

1.2F6givenchoose

Since K is continuous and K(0)>0, it is positive on some interval [−r,r] with r>0. The nearest point of the Fourier grid is within π/n of λ0; because nbn→∞, eventually π/(nbn)<r, so the denominator defining the weights is positive. Every positive weight is supported at a grid point within circular distance Rbn of λ0, where [−R,R] contains the support of K; this distance tends to zero.

2.1step 1.1algebra

For each h with ∣h∣<n, exactly n−∣h∣ ordered pairs (t,s)∈{1,…,n}2 have t−s=h. Grouping the sum in step 1.1 by h gives the asserted finite Fourier sum.

2.2F4F5step 1.1algebra

If a spectral measure F is supplied, substitute γ(t−s)=∫Tei(t−s)θF(dθ) into the finite double sum and interchange the finite sum and integral. The resulting integrand is 12πn∣∑t=1ne−it(λ−θ)∣2=Φn(λ−θ), which proves the Fejér convolution formula. When F=f dθ, substitute that density in the same integral.

3.1A1F4F5F7step 2.2algebra

The kernel Φn is nonnegative. Expanding its finite square and applying [F7] to each term gives ∫TΦn(x) dx=1. When F=f dλ, steps 2.2 and [F4] also give gn(ω):=E[In(ω)]=∫TΦn(ω−θ)f(θ) dθ as a normalized Fejér average of f.

4.1A1F5F9F10F11givenstep 3.1algebra

For 0<δ<π, if dT(x,0)≥δ, choose its representative in [−π,π], so δ≤∣x∣≤π by [F11]. Put r=e−ix. Finite cancellation gives (1−r)∑t=1nrt=r(1−rn). By [F9], ∣1−r∣=2∣sin⁡(x/2)∣>0 by [F10], and ∣1−rn∣=2∣sin⁡(nx/2)∣≤2. Therefore Φn(x)=12πn∣sin⁡(nx/2)sin⁡(x/2)∣2≤12πnsin⁡2(δ/2). Given ε>0, continuity of f at λ0 gives a positive radius; shrink it so that 0<2δ<π and ∣f(θ)−f(λ0)∣<ε when dT(θ,λ0)<2δ. If dT(ω,λ0)<δ, split the integral for gn(ω)−f(λ0) into that neighborhood and its complement. The first part has modulus at most ε because Φn has integral one; on the complement dT(ω,θ)≥δ: choose minimizing integers k1,k2 for dT(θ,ω) and dT(ω,λ0). Then dT(θ,λ0)≤∣θ−λ0+2π(k1+k2)∣≤dT(θ,ω)+dT(ω,λ0), by [F11] and the real triangle inequality. Since the first term is at least 2δ and the last term is less than δ, the displayed kernel bound makes the second part at most ∥f∥1+2π∣f(λ0)∣2πnsin⁡2(δ/2), which tends to zero uniformly for all such ω. Hence gn(ω) tends uniformly to f(λ0) as ω ranges over any neighborhoods of λ0 whose radii tend to zero.

5.1F8step 4.1step 1.2givenalgebra∎

For all sufficiently large n, the weights are nonnegative and sum to one, and every grid point receiving positive weight lies in a shrinking neighborhood of λ0. Finite-sum linearity of expectation [F8] therefore gives E[f~n(λ0)]=∑j=0n−1wn,j(λ0)gn(λj,n)⟶f(λ0) by the uniform local convergence in step 4.1.

5 · Examples, counterexamples and false statements

None yet.

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