Spectral density of a stationary process ($f_X(\lambda )$)

When its autocovariance is absolutely summable, a weakly stationary process has spectral density $f(\lambda )=(2\pi {)}^{-1}{\sum }_h\gamma (h)e^{-ih\lambda }$. The inverse relation is $\gamma (h)={\int }_{-\pi }^{\pi }{e}^{ih\lambda }f(\lambda )d\lambda$. A linear filter multiplies this density by the squared modulus of its frequency response.

Under angular-frequency normalization, the displayed time-series spectral density gives ${\gamma }_0={\pi }^2$ and ${\gamma }_k=2(1-(-1{)}^k)/k^2$ for nonzero integer $k$. This follows from the even integral $2{\int }_0^{\pi }(\pi -\lambda )\cos (k\lambda )d\lambda$. All nonzero even-lag covariances vanish while the odd-lag covariances are positive.

For zero-mean real weakly stationary processes whose cross-covariances are summable, the cross-spectrum is the Fourier series of their lagged covariance, with the displayed convention. Reality gives $f_{XY}(-\lambda )=\overline{f_{XY}(\lambda )}$, but the cross-spectrum need not be real or even. Exchanging the two series gives $f_{YX}(\lambda )=\overline{f_{XY}(\lambda )}$. The entries form a Hermitian spectral density of a stationary process matrix.

For real stationary processes, the one-sided density on $[0,\pi ]$ is twice the usual two-sided density. Its integral is the variance and ${\gamma }_k={\int }_0^{\pi }f(\omega )\cos (k\omega )d\omega$. When covariances are absolutely summable, $f(\omega )={\pi }^{-1}({\gamma }_0+2{\sum }_{k\ge 1}{\gamma }_k\cos (k\omega ))$.

For a real weakly stationary process, restricting its even angular-frequency density $f$ to $[0,\pi ]$ gives $\gamma (k)=2{\int }_0^{\pi }f(\omega )\cos (k\omega )d\omega$. Folding both frequency halves into one density gives $g=2f$ and removes that factor two. Consequently white noise of variance $v$ has restricted density $v/(2\pi )$ and folded density $v/\pi$.

A weakly stationary process has a time-series spectral density precisely when its spectral measure of a stationary time series is absolutely continuous with respect to Lebesgue measure. Absolute summability of its autocovariances is sufficient and yields a continuous Fourier-series density. It is not necessary for existence; for a general integrable density, Fejér sums give an $L^1$ recovery formula.

In cycles per observation, frequency lies in $[-1/2,1/2]$ and $\gamma (h)=\int e^{2\pi ih\omega }{f}_c(\omega )d\omega$. Compared with angular-frequency density $f_a$, one has $f_c(\omega )=2\pi f_a(2\pi \omega )$. In particular, white noise of variance ${\sigma }^2$ has $f_c={\sigma }^2$.

A centered weakly stationary process has a representation $X_t={\int }_{-1/2}^{1/2}{e}^{2\pi it\omega }dZ(\omega )$ with orthogonal random increments. Their variance measure is the spectral measure. When it has density $f$, the autocovariance is $\gamma (h)=\int e^{2\pi ih\omega }f(\omega )d\omega$.

For a centered record of length $T$, the periodogram is the squared modulus of its discrete Fourier transform, with normalizationUnder suitable short-memory assumptions its expectation approaches the spectral density of a stationary process, but its variance generally does not vanish. Smoothing nearby frequencies can provide an estimate with statistical consistency.

At an interior frequency, the real and imaginary Fourier coefficients of a short-memory Gaussian time series converge jointly to independent centered normal variables, each with variance $s(\omega )/2$, where $s$ is the one-sided spectral density. Their squared sum therefore converges to $s(\omega ){\chi }_2^2/2$. Its variance tends to $s(\omega {)}^2$, so an unsmoothed periodogram is not consistent when $s(\omega )>0$. Averaging nearby ordinates can reduce this persistent variance.

For a centered stationary real Gaussian sequence, let $A_T$ and $B_T$ be its cosine and sine coefficients with normalization $1/\sqrt{\pi T}$ at a nonzero Fourier frequency. Grouping the double covariance sum by lag and extending each lag sum to a whole record leaves at most $2k$ omitted bounded products. The full cosine-sine sum is zero, giving the bound. If $\sum k|{\gamma }_k|<\infty$, the two coefficients become asymptotically uncorrelated, and their limiting joint normal distribution consequently has independent components.

Average neighboring periodogram ordinates with nonnegative weights summing to one. Let the number $L_T$ of averaged ordinates tend to infinity while their frequency span, of order $L_T/T$, tends to zero. For a smooth spectral density and suitable short-memory or fourth-order dependence conditions, the bias tends to zero and variance is of order $1/L_T$. Smooth second-order spectral density alone does not ensure this: a time-constant random scale multiplying independent white noise has a smooth unconditional spectrum but a nonergodic random limiting local power.