Normal Distribution
nothing is called 95% until it has been counted
Concept

Random walk — where it appears

A series whose changes are independent, so its level wanders without returning and its variance grows with time. Regressing one on another produces a significant relationship most of the time even when the two are unrelated, which is what a test for a unit root exists to prevent.

Named by 17 essays across 5 fields — each of them below, with the objects they name alongside it.

A pair pulled back at 20% of the gap per step. Above, the two series. Below, the difference between them. The gap is pulled back towards zero by 20% of itself each step, so it stays inside a band of 14.3 while the series themselves travel much further. Nothing here is stationary except the difference. The faint line below is the gap for two free walks from the same seed, drawn for comparison.

The regression that is not spurious

Two random walks regressed on each other are called significantly related three times in four, so the time-series field ends in a warning. The exception it names and does not measure is here — and when the pair is genuinely tied, the fitted relation converges at rate 1/n rather than the usual 1/√n.

cointegration · Spurious Three series and one relation between them. Above, three series generated from Δy = Πy₋₁ + ε with Π of rank 1. Below, the combination y1 −y2. It stays inside a band of 9.5 while the series themselves travel 28.4. The count of combinations that behave this way is the rank of Π, and it is what every method in the field sets out to estimate.

Three series and a count

A pair of series is either tied together or it is not, so its whole inference is one test with one answer. Three can carry none, one or two relations at once — and the thing being estimated stops being a slope and becomes an integer, read off the gap in a spectrum whose top eigenvalue holds at 0.25 while the rest fall like 1/n.

systems · Rank One forecast, and the band the arithmetic puts round it. An AR(1) with φ = 0.75, 60 observations, fitted by least squares and forecast 14 steps ahead. The point forecast decays towards the fitted mean at φ̂^h; the band is ±1.96 standard errors from σ̂²Σψ̂², which grows with the horizon and stops at the unconditional spread 1.72. The dashed pair is the same band computed at the true parameters, which nobody has. The marks past zero are what actually arrived: 12 of 14 inside the band this once, which is one draw and settles nothing.

What the model says next

The usual account of a time series stops at estimation. A forecast asks the other question — not what the parameter is but what the next observation will be — and the band round it is a closed form that grows with the horizon and then stops growing, at a value the series was going to reach anyway.

forecast · Forecast Testing at 0.05 every time the data is looked at. The null is true in every one of these trials and the test is correct every time it is run. Looking once rejects 4.9% of the time, as it should; looking ten times rejects 19.2% of the time. Nothing changed except permission to look.

When the looking happens

A p-value is defined relative to a sampling plan, so the same data means different things under different stopping rules. Testing five times at the nominal level rejects a true null 14% of the time, and no observation in the dataset changed.

sequential · Stopping Two independent random walks, 100 steps. Nothing connects these two series: each is generated from its own independent draws. Regressing one on the other gives a slope with t = -10.9, R² = 0.55 and p = 0.0e+0 — a result that would be reported as a finding by any standard output.

Two walks and a finding

Regress one random walk on another, independently generated, and the slope is significant 76.7% of the time with a median R² of 0.17. Nothing connects the two series, nothing in the output says so, and more data makes it worse.

timeseries · Spurious The same data, one regression per choice of left-hand side. The two-step procedure has to put one series on the left, and with 3 series there are 3 ways to do it. Each returns a relation and a residual test; the 5% point is -3.71, simulated. Here they do not agree: 2 of 3 reject, and the relations they report are written with a 1 in the position of whichever series was on the left, so they can be compared. Nothing in a printed output records which regression was run.

Which series goes on the left

The two-step procedure has to pick a series to regress the others on, and nothing in its output records which. With a pair that choice never changes the verdict. With three series and one relation between them, the three choices disagree about whether the system is cointegrated at all 98.0% of the time.

systems · Rank The trace statistic under the null, and the 5% point it needs. 600 systems of 3 unrelated random walks, each put through the reduced-rank regression, with the statistic for "rank ≤ 0" collected. The 5% point is 31.91. There is no standard table to look that up in: the distribution depends on the number of common trends under the null and is not a chi-square, so the value is simulated on one set of seeds and applied on another — exactly the position the pair's residual test was in one field ago.

Counting what is still wandering

The statistic that turns a spectrum into an integer has one name and three distributions. Its 5% point is 8.12, 18.64 or 31.74 depending only on how many series are left wandering under the null being tested — and read against the wrong one of those three, it calls unrelated random walks cointegrated most of the time.

systems · Rank What differencing fixes, and what it costs, 100 steps. The first pair is the false-positive rate for two independent random walks: 77% on the levels, 4.9% on the differences. The second pair is how much of a real relationship survives: R² falls from 0.91 to 0.33. The same operation does both.

What differencing costs

Differencing takes the false-positive rate between two unrelated walks from 76.7% to 4.9%, and takes a genuine relationship's R² from 0.91 to 0.33. Applied to a series that did not need it, it doubles the variance and installs a correlation of −0.5 that the data never had.

timeseries · Spurious What the long-run relation is worth, at α = -0.2. Root mean squared one-step forecast error of the error-correction model divided by that of the model fitted on differences alone; below one means the levels helped. With the equilibrium known the ratio is 0.929 at 100 observations and settles on 0.905 by 3,200, against a closed form of 0.905 that mentions no sample size at all; the excess at short series is the cost of fitting three coefficients on fifty observations. With the equilibrium estimated as well it is 1.127 at 100 — worse than differencing — and 0.914 at 3,200. The gap between the two curves is the cost of not knowing β.

The cost of differencing a pair

Differencing two cointegrated series makes every standard error honest and throws away the one thing known about where they are going. The error-correction model forecasts better by exactly what a closed form says — and at four hundred observations it is better on four series in five and worse on average.

cointegration · Dependence Every equation's adjustment speed, and the one number they make together. Each series gets its own equation, each is regressed on the same lagged disequilibrium, and what comes back is the whole vector α. Averaged over 400 systems at n = 300: α₁ = -0.154 against -0.15 generated, α₂ = 0.104 against 0.1 generated. The gap closes at the combination of them rather than at any one entry — 25% of any disagreement per step, a half-life of 2.41 steps, where the single equation that fits only the first series reports 4.27.

Which series does the moving

“y adjusts towards x” and “x adjusts towards y” are different mechanisms with identical long-run relations, and a single-equation model cannot tell them apart because it only writes one equation. Writing all of them recovers a vector — and a gap that closes at 25% a step where one equation alone reports 15%.

systems · Adjustment The damage and the warning, against the same dial. Two readings at each persistence. In the darker colour, how often a regression between two independent series of 200 steps is called significant at 5%: 4.9% at φ = 0, 34.2% at 0.8, 52.4% at 0.9, 83.4% at a unit root. In the lighter, how often the standard unit-root test refuses a unit root on one of those series — the chance the analyst is told the series is stationary and may be regressed: 87.2% at φ = 0.9 and 31.9% at 0.95. At φ = 0.9 both are high at once, which is a correct diagnostic licensing a regression that is wrong half the time.

The cliff that is a slope

A regression between two independent series is called significant 4.9% of the time at no persistence, 52.4% at a lag-one correlation of 0.9, and 83.4% at a unit root. The rule the field offers asks whether the last of those holds, and at 0.9 the unit-root test correctly refuses one 87.2% of the time.

timeseries · Spurious The bounded error and the unbounded one. How the sequential trace procedure's answer is distributed, against the sample length, for a three-series system with 2 genuine relations. Over-counting — claiming a stationary combination that is a random walk — reads 4.9%, 7.2%, 5.7%, 6.2%, 5.9%, 4.2% across the six lengths, never far from the 5% of a single test. Under-counting reads 69.5%, 40.2%, 14.0%, 0.5%, 0.0%, 0.0%. The procedure is described as a 5% rule and the 5% applies to one of those columns.

The rank is a decision

The sequential procedure's 5% bounds one of its two errors. Over-counting reads between 4.2% and 7.2% at every sample length from fifty observations to three hundred; under-counting reads 69.5% at fifty and 0.0% at three hundred, and nothing in the procedure bounds it.

systems · Rank How slow a return each test can see at 200 observations: the relation estimated against the relation supplied. The estimated relation's test reaches 80% power up to a half-life of 5.05 steps and the supplied relation's up to 6.88. At a half-life of 8 the two find the pair 36.3% and 71.9% of the time; at 12, 16.9% and 39.3%. Their 5% points are −3.38 and −2.89.

A relation supplied in advance

When theory supplies the long-run coefficient, the gap between two series can be tested directly and the search the Engle–Granger test pays for disappears: its 5% point moves from −3.38 to −2.89, and at two hundred observations the slowest return found four times in five goes from a half-life of 5.05 steps to 6.88. The price is in what a rejection means. With a gap that halves in three steps, a coefficient a fifth too large is confirmed 99.1% of the time and one half again too large 81.9%, though every such gap contains a random walk — and at a thousand observations the second is confirmed more often, not less.

cointegration · Spurious Six cells, and 5% is the right answer in all of them. How often a regression between two independently generated series is called significant at the 5% level, for two worlds and three treatments, at 200 observations. Every pair is independent by construction, so 5% is correct everywhere and every other reading is a failure. Untreated: 82.9% and 100.0%. With a fitted line removed: 74.2% and 33.5%. Differenced: 5.0% and 5.2%. The treatment that controls the rate in both worlds is the one that discards the level and the trend, which is the quantity a study of trending series was about.

The repair that keeps the question

A regression between two independent trending series is significant 82.9% of the time on random walks and 100.0% on trend-stationary ones. Subtracting a fitted line leaves 74.2% and 33.5%; differencing leaves 5.0% and 5.2% and throws away the trend the study was about.

timeseries · Spurious What each wrong count costs, 4 steps ahead. Squared forecast error 4 steps ahead at each imposed rank, relative to the correctly specified fit, at 200 observations. With 1 genuine relations, imposing 0 costs 13.3% and imposing 2 costs 4.8%. With 2 genuine relations, imposing 1 costs 15.6% and imposing 3 costs 2.5%. Under-counting is the more expensive mistake in both systems, and it is the one the procedure's level does not bound.

Which mistake about the rank costs

On a system with two relations, imposing none costs 29.2% of squared forecast error and imposing three costs 2.5%. The expensive mistake is under-counting, which is the error the procedure's 5% does not bound — so the guarantee protects the cheap side.

systems · Rank How fast a gap has to close before a sample can see it close. The power of the test against the half-life of a disagreement, at 100, 200, 400 observations, each read against its own simulated critical value. Every pair in every reading is genuinely tied together, so a non-rejection is a miss. At 200 observations a gap that halves in 3 steps is found 99.9% of the time and one that halves in 12 steps is found 15.3% of the time — and by 35 steps the reading is 6.1%, which is the test's own size. Beyond that the curves are flat because there is nothing left to detect with.

How slow a return a sample can see

At two hundred observations the test finds a gap that halves in five steps four times in five, one that halves in eight 37.3% of the time, and one that halves in fifty 4.95% of the time — which is the rate at which it finds pairs with no mechanism at all. The boundary moves with the sample, not with its square root.

timeseries · Spurious Four treatments of a shock sixteen pairs share: false alarms at the independence point, and power at each one's own point. Sixteen pairs, two hundred dates, nine tenths of each series' variation common. every pair loads the shock equally: nothing removed, false alarms 31.6% and power 22.3%; the cross-sectional average removed, false alarms 4.8% and power 99.8%; an estimated factor removed, false alarms 9.2% and power 76.3%; the observed shocks removed, false alarms 4.5% and power 6.5%. loadings run from a half to one and a half: nothing removed, false alarms 30.8% and power 25.8%; the cross-sectional average removed, false alarms 13.3% and power 82.5%; an estimated factor removed, false alarms 8.9% and power 73.8%; the observed shocks removed, false alarms 4.5% and power 7.5%.

A factor estimated from the panel

When sixteen pairs feel a common shock with loadings from a half to one and a half, removing the cross-sectional average leaves the pooled test rejecting an unrelated panel 13.3% of the time. Estimating the factor and each pair's loading instead brings that to 8.9% — and no further, because each loading comes from the same two hundred dates however many pairs there are — and it costs power against the average at the right critical value, 73.8% against 82.5%. Removing the shocks as observed restores 4.5% exactly and finds a shared relation 7.5% of the time: a series tied to its partner carries the partner's factor in its level, not its own shock.

cointegration · Spurious

Named alongside it

The objects these essays reach for when they reach for this one.

CointegrationStationaritySpurious regressionCritical valueUnit rootCointegrating rankCommon trendDifferencingThe error-correction modelStatistical powerMonte CarloReduced-rank regression

All concepts