Page 31 of Chapter 13 [Time Series 1] - question on roots of an equation

Discussion in 'CS2' started by Stephan Iliffe, Mar 10, 2019.

  1. Hi all

    On page 31 of chapter 13 of CS2, there is the following in the first solution:

    “The polynomial can be factorized as (1+3z)(1-z), so it’s roots are 1/3 and 1.”

    I understand that we are interested in the absolute value of the roots so in this case there is one less than 1 and the process is not stationary.

    Surely it is still incorrect to say the root is 1/3 and not -1/3.

    I do not see any corrections for this and was just wondering if my logic is wrong.

    Regards
    Stephan Iliffe
     
  2. Also, in the next question, the solution says it is easy to see that [with roots 1, 2 and 3] the differencing process will eliminate the root 1. So the process will be stationary.

    Is it really obvious that the root 1 will be eliminated or do you actually have to do the differencing to see this?
     
  3. Sid Kumar

    Sid Kumar Member

    For the purpose of this exam focus on absolute value of roots, they should be greater than one for stationarity test.

    With the next question, since only one root is failing the test, you can assume that the process needs to be differenced only once. You can try and work out the roots for the differenced characteristic poly, I suspect they will have roots >1
     
  4. Thanks Sid

    Does this mean that the root failing the test is always the root that will be eliminated when differencing?

    Why is this?

    I can see that it happens in this example but why would we assume in advance this to be true?

    Regards
     
  5. Sid Kumar

    Sid Kumar Member

    I think that is the basic idea behind differencing. I havent come across a question that suggests otherwise. Generally it will ask you to check for stationarity and a simple way to eliminate 1 as a root is to put it in the characteristic equation to see if it equals zero (I have seen this to be the case in a few questions).
     
  6. Thanks Sid

    I found this on Wikipedia:
    If the other roots of the characteristic equation lie inside the unit circle—that is, have a modulus (absolute value) less than one—then the first difference of the process will be stationary; otherwise, the process will need to be differenced multiple times to become stationary.[1] Due to this characteristic, unit root processes are also called difference stationary.[2][3]

    https://en.m.wikipedia.org/wiki/Unit_root

    So I suppose it is in the specific case.
     
  7. I suppose it makes sense since a root of one means there is a lag of 1.

    The roots less than one do not contradict the stationarity of the process.

    So differencing once will remove the lag of one and the process will be stationary.
     
  8. Sid Kumar

    Sid Kumar Member

    Right, good one. Cheers
     
  9. Jordan Ko

    Jordan Ko Made first post

    Differentiating the characteristic equation is equivalent to applying the backward shift operator, i.e. inverse delta Xn= (1-B) Xn. When we factor out -(lambda - 1) from the characteristic equation, we are basically differentiating the process by taking out -(B-1). This is very similar to the discussion on page 48 in Ch 13.
     
    Last edited: Sep 19, 2020
  10. Andrew Martin

    Andrew Martin ActEd Tutor Staff Member

    Hello

    In answer to the first question, the root should be -1/3. This was an error in the 2019 Course Notes and the correction appeared in the 2019 corrections document. It is correct in the 2020 Course Notes.

    As has been pointed out, we are generally interested in the absolute value of the root; however, the correct root is still -1/3.

    In terms of the discussion on differencing, I think the easiest way to see how it removes unit roots (roots of 1) is the idea mentioned in a previous post about factoring out (1-B).

    Short answer

    Say the characteristic polynomial (for the terms of the process) of some time series \( X_t \) is given by:

    \( 1 - \alpha_1 \lambda - ... - \alpha_p \lambda^p \)

    This is a polynomial of degree \(p \) that has \( p\) roots, say \( r_1, ..., r_p \). This means we can factorise to:

    \( (r_1 - \lambda )(r_2 - \lambda)...(r_p - \lambda) \)

    Say we have a unit root (say \( r_1 \) for ease of reference). Then we can write this as:

    \( (1 - \lambda )(r_2 - \lambda)...(r_p - \lambda) \)

    For our purposes we can think of this as:

    \( (1 - \lambda ) * something \)

    Where we have factored out the \( (1 - \lambda ) \). Now, this \( something \) turns out to be the characteristic polynomial (for the terms of the process) of the differenced series:

    \( X_t - X_{t-1} \)

    To see this let's go back to writing out the equation with \( B \) instead of \( \lambda \):

    \( (1 - \alpha_1 B - ... - \alpha_p B^p) X_t = RHS \)

    where the RHS contains any constants and error terms.

    Now, we know that we can write the brackets as:

    \( (1 - B ) something \)

    So we have:

    \( ((1 - B ) something) X_t = RHS \)

    However:

    \( (1 - B ) X_t = X_t - X_{t-1} \)

    So we have:

    \( (something) (X_t - X_{t-1}) = RHS \)

    This is effectively an equation for the differenced series. Let:

    \( Y_t = X_t - X_{t-1} \)

    Then we have:

    \( (something) Y_t = RHS \)

    So we've shown that taking out the unit root, factoring out the (1-B), leaves us with the differenced series with remaining polynomial \(something \). Now it's possible this differenced series still has unit roots and requires differencing again, however the process is the same.

    Longer answer

    If you still want to see all the algebra for \( Y_t \) really fall into place, I've set it out below.

    Say we have the following time series model:

    \( X_t = \mu + \alpha_1 (X_{t-1} - \mu) + ... + \alpha_p (X_{t-p} - \mu) + \beta_1 e_{t-1} + ...+ \beta_q e_{t-q} + e_t \quad \quad \quad \) \( (1) \)

    Then, rearranging by putting the past terms of the process on the LHS:

    \( X_t - \alpha_1 X_{t-1} - ... - \alpha_p X_{t-p} = \mu (1 - \alpha_1 - ... - \alpha_p) + \beta_1 e_{t-1} + ...+ \beta_q e_{t-q} + e_t \)

    In terms of B:

    \((1 - \alpha_1 B - ... - \alpha_p B^p)X_t = RHS \)

    The characteristic polynomial is then:

    \( 1 - \alpha_1 \lambda - ... - \alpha_p \lambda^p \)

    This is a polynomial of degree \(p \) that has \( p\) roots, say \( r_1, ..., r_p \). This means we can factorise to:

    \( (r_1 - \lambda )(r_2 - \lambda)...(r_p - \lambda) \)

    Say we have a unit root (say \( r_1 \) for ease of reference). Now, one of the really key points here is that if we have a unit root then the following must hold:

    \( (1 - \alpha_1 - ... - \alpha_p) = 0 \)

    Why? Well just plug it into the characteristic polynomial:

    \( 1 - \alpha_1 * 1 - ... - \alpha_p * 1 = 0 \)

    Now why does this matter? Let's go back to the original time series model, (1):

    \( X_t = \mu + \alpha_1 (X_{t-1} - \mu) + ... + \alpha_p (X_{t-p} - \mu) + \beta_1 e_{t-1} + ...+ \beta_q e_{t-q} + e_t \quad \quad \quad \) \( (1) \)

    Firstly, if we expand out the brackets on the RHS, we get a term that is \( \mu (1 - \alpha_1 - ... - \alpha_p) \). This is 0, so we can effectively ignore \( \mu \) from now on.

    Subtracting \( X_{t-1} \) from both sides and doing some rearranging:

    \( X_t - X_{t-1} = (\alpha_1 - 1) * ( X_{t-1} - X_{t-2}) + (\alpha_2 + \alpha_1 - 1) * (X_{t-2} - X_{t-3}) + (\alpha_3 + \alpha_2 + \alpha_1 - 1) * (X_{t-3} - X_{t-4}) + ... + \)
    \( (\alpha_{p-1} + \alpha_{p-1} + ... + \alpha_1 - 1) ( X_{t-p+1} - X_{t-p}) + (\alpha_{p} + \alpha_{p-1} + ... + \alpha_1 - 1) X_{t-p} + \beta_1 e_{t-1} + ...+ \beta_q e_{t-q} + e_t \)

    Now, the last \( X_{t-p} \) term is actually 0 because the coefficient is \( \alpha_{p} + \alpha_{p-1} + ... + \alpha_1 - 1 = 0 \). So, we have:

    \( X_t - X_{t-1} = (\alpha_1 - 1) * ( X_{t-1} - X_{t-2}) + (\alpha_2 + \alpha_1 - 1) * (X_{t-2} - X_{t-3}) + (\alpha_3 + \alpha_2 + \alpha_1 - 1) * (X_{t-3} - X_{t-4}) + ... + \)
    \( (\alpha_{p-1} + \alpha_{p-1} + ... + \alpha_1 - 1) ( X_{t-p+1} - X_{t-p}) + \beta_1 e_{t-1} + ...+ \beta_q e_{t-q} + e_t \)

    If we let:

    \( Y_t = X_t - X_{t-1} \)

    Then we have:

    \( Y_t = (\alpha_1 - 1) * Y_{t-1} + (\alpha_2 + \alpha_1 - 1) * Y_{t-2} + (\alpha_3 + \alpha_2 + \alpha_1 - 1) * Y_{t-3} + ... + \)
    \( (\alpha_{p-1} + \alpha_{p-1} + ... + \alpha_1 - 1) Y_{t-p+1} + \beta_1 e_{t-1} + ...+ \beta_q e_{t-q} + e_t \)

    Let's find the characteristic equation involving the terms of the process for \( Y_t \).

    So, putting past terms of the process on the LHS:

    \( Y_t + (1 - \alpha_1) * Y_{t-1} + (1 - \alpha_2 - \alpha_1) * Y_{t-2} + ... + (1 - \alpha_{p-1} - ... - \alpha_1) Y_{t-p+1} = \beta_1 e_{t-1} + ...+ \beta_q e_{t-q} + e_t \)

    The characteristic equation is:

    \( 1 + (1 - \alpha_1) \lambda + ... + (1 - \alpha_{p-1} - ... - \alpha_1) \lambda^{p-1} \)

    If we times this by \( 1 - \lambda \) we get:

    \( 1 + (1 - \alpha_1) \lambda + ... + (1 - \alpha_{p-1} - ... - \alpha_1) \lambda^{p-1} - \lambda - (1 - \alpha_1) \lambda^2 - ... - (1 - \alpha_{p-1} - ... - \alpha_1) \lambda^p \)
    \( = 1 - \alpha_1 \lambda - \alpha_2 \lambda^2 - ... - \alpha_p \lambda^p \)

    This is the characteristic polynomial for \( X_t \). Remember that we could represent it as:

    \( (r_1 - \lambda )(r_2 - \lambda)...(r_p - \lambda) \)

    So, we've shown that the characteristic polynomial for the differenced series, \( Y_t \), is equal to:

    \( (r_2 - \lambda)...(r_p - \lambda) \)

    assuming \( r_1 \) was the unit root, as stated earlier.

    Hope this helps

    Andy
     

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