Autoregressive Process Proofs

Basic Properties

Property 1: The mean of the yi in a stationary AR(p) process is

Mean AR(p)

Proof: Since the process is stationary, for any k, E[yi] = E[yi-k], a value which we will denote μ. Since E[εi] = 0, E[φ0] = φ0 and

AR(p)

it follows that

Mean AR(p) derivation

Solving for μ yields the desired result.

Property 2: The variance of the yi in a stationary AR(1) process is

Variance AR(1)

Proof: Since the yi and εi are independent, by basic properties of variance, it follows that

Variance AR(1) derivation

Variance AR(1) proof

Since the process is stationary, var(yi) = var(yi-1), and so

Variance AR(1) proof 3

Solving for var(yi) yields the desired result.

Property 3: The lag h autocorrelation in a stationary AR(1) process is

Proof: First note that for any constant a, cov(a+x, a+y) = cov(x,y). Thus, cov(yi,yj) has the same value even if we assume that φ0 = 0, and similarly for var(yi) = cov(yi,yi). Thus, it suffices to prove the property when φ0 = 0. In this case, by Property 1, μ = 0, and so cov(yi,yj) = E[yiyj].

Thus

Autocovariance AR(1) derivation

since by the stationary property, E[yi-1,yi-k] = γi-1. Now, by induction on k, it is easy to see that

HenceYule-Walker equations

Property 4 : For any stationary AR(p) process, we can calculate the autocovariance at lag k > 0 by

image065z

Similarly, the autocorrelation at lag k > 0 can be calculated as

image066z

Proof: As usual, we can assume that the mean is zero (and so φ0 = 0). Thus, we are dealing with the process

AR(p)

Thus

Walker-Yule equations derivation

The second form of the property follows by dividing both sides of the equation by γ0.

Proof of Property 7

Property 7: The variance of the yi in a stationary AR(2) process is

image084z

Proof:

Property 7 - 1

Property 7 - 2

Property 7 - 3

By the stationary property

Property 7 - 4

and so

Property 7 - 5

But by Property 4, 

Property 7 - 6

and so

Property 7 - 7

The result follows by expanding the terms in the denominator and seeing that these are equal to the expansion of the denominator in the statement of the property.

Links

↑ Autoregressive processes basic concepts

References

Alonso, A. M., Garcia-Martos, C. (2012) Time series analysis: autoregressive, MA and ARMA processes
https://www.academia.edu/35659911/Time_Series_Analysis_Autoregressive_MA_and_ARMA_processes

Greene, W. H. (2002) Econometric analysis. 5th Ed. Prentice-Hall
https://www.ctanujit.org/uploads/2/5/3/9/25393293/_econometric_analysis_by_greence.pdf

8 thoughts on “Autoregressive Process Proofs”

  1. I love that you have proofs here, and I love the format. This has been useful for my final exam study for my time-series course. Many textbooks only provide proofs for the general AR(p) case, which makes it more difficult to follow, and frustrating when I know how to use the formula.

    Reply
  2. Dear Charles, In Property 2 proof, line 6. It mentioned the below.
    “Since the process is stationary, yi = yi-1, and so”. Would you please elaborate how true this statement is ? Shouldn’t the statement states that, Since the process is stationary, var(yi)=var(yi-1), and so ?

    Reply
    • Pankaj,
      Sorry, but it looks like I forgot to add this webpage. I am now finishing up the testing for the next release of the Real Statistics software (rel 5.3). When I have finished this, I will add the missing proofs.
      Charles

      Reply

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