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expected values of the price level at each time along its future path. In either case the price level has drift in the sense of a rising expected value, but the cases differ according to the type of non-stationarity: difference stationarity in the former case, but trend stationarity in the latter case.
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from its current level in each time period, or whether to target a return of the price level to a predetermined growth path. In the latter case no price level drift is allowed away from the predetermined path, while in the former case any stochastic change to the price level permanently affects the
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has a drift rate of 1/2 per toss. This is in contrast to the random fluctuations about this average value. The stochastic mean of that coin-toss process is 1/2 and the drift rate of the stochastic mean is 0, assuming 1 = heads and 0 = tails.
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is a zero-long-run-mean stationary random variable. In this case the stochastic term is stationary and hence there is no stochastic drift, though the time series itself may drift with no fixed long-run mean due to the deterministic component
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population of randomly reproducing organisms would experience changes from generation to generation in the frequencies of the different genotypes. This may lead to the fixation of one of the genotypes, and even the emergence of a
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491:=0), this unit root process exhibits drift, and specifically stochastic drift, due to the presence of the stationary random shocks
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Krus, D. J., & Jacobsen, J. L. (1983) Through a glass, clearly? A computer program for generalized adaptive filtering.
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which is the rate at which the average changes. For example, a process that counts the number of heads in a series of
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This article is about mathematical concept. For the slow accumulation of errors in navigation systems, see
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of secular events are frequently conceptualized as consisting of a trend component fitted by a
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Krus, D.J., & Ko, H.O. (1983) Algorithm for autocorrelation analysis of secular trends.
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per period. In this case the non-stationarity can be removed from the data by
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is a non-stochastic drift parameter: even in the absence of the random shocks
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Time series variables in economics and finance — for example,
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value, and so forth forever. So after the initial shock hits
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and hence no drift. But even in the absence of the parameter
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value, which itself in the next period becomes the lagged
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is a zero-long-run-mean stationary random variable; here
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620:July 2010
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