Periodogram ($I_T(\lambda )$)

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.