Lag operator

Short description: Operator for offsetting time series elements

"Backshift" redirects here. For the linguistic sense, see Sequence of tenses.

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In time series analysis, the lag operator (L) or backshift operator (B) operates on an element of a time series to produce the previous element. For example, given some time series

X={X1,X2,…}

then

LXt=Xt−1 for all t>1

or similarly in terms of the backshift operator B: BXt=Xt−1 for all t>1. Equivalently, this definition can be represented as

Xt=LXt+1 for all t≥1

The lag operator (as well as backshift operator) can be raised to arbitrary integer powers so that

L−1Xt=Xt+1

and

LkXt=Xt−k.

## Contents

* 1 Lag polynomials
* 2 Difference operator
* 3 Conditional expectation
* 4 See also
* 5 References

## Lag polynomials

Polynomials of the lag operator can be used, and this is a common notation for ARMA (autoregressive moving average) models. For example,

εt=Xt−∑i=1pφiXt−i=(1−∑i=1pφiLi)Xt

specifies an AR(p) model.

A polynomial of lag operators is called a lag polynomial so that, for example, the ARMA model can be concisely specified as

φ(L)Xt=θ(L)εt

where φ(L) and θ(L) respectively represent the lag polynomials

φ(L)=1−∑i=1pφiLi

and

θ(L)=1+∑i=1qθiLi.

Polynomials of lag operators follow similar rules of multiplication and division as do numbers and polynomials of variables. For example,

Xt=θ(L)φ(L)εt,

means the same thing as

φ(L)Xt=θ(L)εt.

As with polynomials of variables, a polynomial in the lag operator can be divided by another one using polynomial long division. In general dividing one such polynomial by another, when each has a finite order (highest exponent), results in an infinite-order polynomial.

An annihilator operator, denoted [ ]+, removes the entries of the polynomial with negative power (future values).

Note that φ(1) denotes the sum of coefficients:

φ(1)=1−∑i=1pφi

## Difference operator

Main article: Finite difference

In time series analysis, the first difference operator  :Δ

ΔXt=Xt−Xt−1ΔXt=(1−L)Xt.

Similarly, the second difference operator works as follows:

Δ(ΔXt)=ΔXt−ΔXt−1Δ2Xt=(1−L)ΔXtΔ2Xt=(1−L)(1−L)XtΔ2Xt=(1−L)2Xt.

The above approach generalises to the i-th difference operator ΔiXt=(1−L)iXt .

## Conditional expectation

It is common in stochastic processes to care about the expected value of a variable given a previous information set. Let Ωt be all information that is common knowledge at time t (this is often subscripted below the expectation operator); then the expected value of the realisation of X, j time-steps in the future, can be written equivalently as:

E[Xt+j|Ωt]=Et[Xt+j].

With these time-dependent conditional expectations, there is the need to distinguish between the backshift operator (B) that only adjusts the date of the forecasted variable and the Lag operator (L) that adjusts equally the date of the forecasted variable and the information set:

LnEt[Xt+j]=Et−n[Xt+j−n],
BnEt[Xt+j]=Et[Xt+j−n].

## See also

* Autoregressive model
* Autoregressive moving average model
* Moving average model
* Shift operator
* Z-transform

## References

* Hamilton, James Douglas (1994). Time Series Analysis. Princeton University Press. ISBN 0-691-04289-6.
* Verbeek, Marno (2008). A Guide to Modern Econometrics. John Wiley and Sons. ISBN 0-470-51769-7. https://archive.org/details/guidetomoderneco0003verb.
* Weisstein, Eric. "Wolfram MathWorld". Wolfram Research. http://mathworld.wolfram.com/DifferenceOperator.html. Retrieved 10 November 2017.
* Box, George E. P.; Jenkins, Gwilym M.; Reinsel, Gregory C.; Ljung, Greta M. (2016). Time Series Analysis: Forecasting and Control (5th ed.). New Jersey: Wiley. ISBN 978-1-118-67502-1.

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