Field

Where a taper's case begins

A tapered block was measured against a plain one on the exact bias each implies, and the answer was that the taper's advantage has not arrived at any block length a hundred and twenty rows can afford — with the crossing at ℓ = 20 and a difference there of three tenths of a point, too small for the critical-value table to resolve. Two of those three statements are about the wrong quantity. What a sample reports at ℓ = 20 is four points rather than three tenths, because the autocovariances the window is applied to are themselves attenuated and the window that discards the long lags loses less of them; and the comparison is made at a shared block length where each window has its own best one. Read at each window's own setting, on the error rather than on the bias, the ordering reverses at a hundred and twenty rows.

The gap a sample shows

The exact difference between two block windows at a block length of twenty is three tenths of a point. What a hundred and twenty rows report is four and a third, because the autocovariances the window is applied to are attenuated too.

What a longer block buys and what it costs. A trapezoidal block at 120 rows, with the error split into the two things it is made of. The bias falls with the block length, because a longer block attenuates less, and it flattens at 23.2% because the sample's own autocovariances are short whatever window is applied to them. The spread rises with it, because a longer block means fewer of them. Their sum in quadrature has a minimum at ℓ = 16, which is not where either of the two has one. The faint line is the rectangle's total error, for scale: it is above the trapezoid's from ℓ = 12 onwards.

Bias is not the whole of it

A window that reaches zero at its ends attenuates less and uses less of each block. The block length that minimises its bias is not the one that minimises its error, and comparing two windows at one length compares one of them mis-tuned.

What each instrument costs to read. The number of draws each instrument needs to separate a rectangular block from a trapezoidal one at two standard errors, at a block length of 20 and 120 rows — measured from each instrument's own spread on the same draws. The implied variance needs 7.0 and the 95% point needs 20.2, a factor of 2.90 at this block length. There is a closed form beside it and it does not depend on either the scale or the size of the gap: the standard error of a p-quantile is √(p(1−p))/f(q) over √B where a standard deviation's is σ/√(2B), which at the 95% point of a nearly normal reference distribution is 3.30 times as many draws for the same statement. And the quantile route needs every one of those draws resampled, where the variance route needs none.

Measuring a variance rather than a quantile

A resample's implied long-run variance can be computed from the sample with no resampling in it at all. A critical value cannot, and the difference is a factor of three in the draws before any of the resampling is counted.

Where the taper's case begins, and it is not where the algebra says. The block length at which a trapezoidal block's implied variance stops being more biased than a rectangular one's, against the length of the sample. Computed exactly — from the law's own autocovariances, with no sampling in it — the answer is 19.2 and does not depend on the sample at all. What a sample of 120 rows reports is 13.3, and the reported crossing walks out towards the exact one as the sample grows: 13.3, 15.0, 16.4, 18.0. The mechanism is that the autocovariances the window is applied to are themselves attenuated, worst at the longest lags, and the window that discards those lags loses less of them.

The error no window repairs

Every block window's best estimate of a long-run variance is wrong by about forty per cent at a hundred and twenty rows, and the largest part of that is not a bias at all. Choosing the window moves a twentieth of it.

A model against a window

A block resample's long-run variance is attenuated by its window, and the construction that escapes the window is a model: fit an autoregression and sum its dependence to every lag in closed form. Measured on both halves of the answer, the escape is narrower than it looks. Correctly specified, on 120 rows of a first-order autoregression, the model's error is 45.5% of the truth against the trapezoid block's 42.4% — it removes the bias and buys a spread larger than the bias was — and it pulls ahead only from about 480 rows. Wrongly specified, on a five-period moving average, it is off by 84% to 105% at every length, and even an order chosen by the criterion trails the window throughout.

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