Jun 21

Solution to Question 1 from UoL exam 2020

Solution to Question 1 from UoL exam 2020

The assessment was an open-book take-home online assessment with a 24-hour window. No attempt was made to prevent cheating, except a warning, which was pretty realistic. Before an exam it's a good idea to see my checklist.

Question 1. Consider the following ARMA(1,1) process:

(1) z_{t}=\gamma +\alpha z_{t-1}+\varepsilon _{t}+\theta \varepsilon _{t-1}

where \varepsilon _{t} is a zero-mean white noise process with variance \sigma ^{2}, and assume |\alpha |,|\theta |<1 and \alpha+\theta \neq 0, which together make sure z_{t} is covariance stationary.

(a) [20 marks] Calculate the conditional and unconditional means of z_{t}, that is, E_{t-1}[z_{t}] and E[z_{t}].

(b) [20 marks] Set \alpha =0. Derive the autocovariance and autocorrelation function of this process for all lags as functions of the parameters \theta and \sigma .

(c) [30 marks] Assume now \alpha \neq 0. Calculate the conditional and unconditional variances of z_{t}, that is, Var_{t-1}[z_{t}] and Var[z_{t}].

Hint: for the unconditional variance, you might want to start by deriving the unconditional covariance between the variable and the innovation term, i.e., Cov[z_{t},\varepsilon _{t}].

(d) [30 marks] Derive the autocovariance and autocorrelation for lags of 1 and 2 as functions of the parameters of the model.

Hint: use the hint of part (c).


Part (a)

Reminder: The definition of a zero-mean white noise process is

(2) E\varepsilon _{t}=0, Var(\varepsilon _{t})=E\varepsilon_{t}^{2}=\sigma ^{2} for all t and Cov(\varepsilon _{j},\varepsilon_{i})=E\varepsilon _{j}\varepsilon _{i}=0 for all i\neq j.

A variable indexed t-1 is known at moment t-1 and at all later moments and behaves like a constant for conditioning at such moments.

Moment t is future relative to t-1.  The future is unpredictable and the best guess about the future error is zero.

The recurrent relationship in (1) shows that

(3) z_{t-1}=\gamma +\alpha z_{t-2}+... does not depend on the information that arrives at time t and later.

Hence, using also linearity of conditional means,

(4) E_{t-1}z_{t}=E_{t-1}\gamma +\alpha E_{t-1}z_{t-1}+E_{t-1}\varepsilon _{t}+\theta E_{t-1}\varepsilon _{t-1}=\gamma +\alpha z_{t-1}+\theta\varepsilon _{t-1}.

The law of iterated expectations (LIE): application of E_{t-1}, based on information available at time t-1, and subsequent application of E, based on no information, gives the same result as application of E.

Ez_{t}=E[E_{t-1}z_{t}]=E\gamma +\alpha Ez_{t-1}+\theta E\varepsilon _{t-1}=\gamma +\alpha Ez_{t-1}.

Since z_{t} is covariance stationary, its means across times are the same, so Ez_{t}=\gamma +\alpha Ez_{t} and Ez_{t}=\frac{\gamma }{1-\alpha }.

Part (b)

With \alpha =0 we get z_{t}=\gamma +\varepsilon _{t}+\theta\varepsilon _{t-1} and from part (a) Ez_{t}=\gamma . Using (2), we find variance

Var(z_{t})=E(z_{t}-Ez_{t})^{2}=E(\varepsilon _{t}^{2}+2\theta \varepsilon_{t}\varepsilon _{t-1}+\theta ^{2}\varepsilon _{t-2}^{2})=(1+\theta^{2})\sigma ^{2}

and first autocovariance

(5) \gamma_{1}=Cov(z_{t},z_{t-1})=E(z_{t}-Ez_{t})(z_{t-1}-Ez_{t-1})=E(\varepsilon_{t}+\theta \varepsilon _{t-1})(\varepsilon _{t-1}+\theta \varepsilon_{t-2})=\theta E\varepsilon _{t-1}^{2}=\theta \sigma ^{2}.

Second and higher autocovariances are zero because the subscripts of epsilons don't overlap.

Autocorrelation function: \rho _{0}=\frac{Cov(z_{t},z_{t})}{\sqrt{Var(z_{t})Var(z_{t})}}=1 (this is always true),

\rho _{1}=\frac{Cov(z_{t},z_{t-1})}{\sqrt{Var(z_{t})Var(z_{t-1})}}=\frac{\theta \sigma ^{2}}{(1+\theta ^{2})\sigma ^{2}}=\frac{\theta }{1+\theta ^{2}}, \rho _{j}=0 for j>1.

This is characteristic of MA processes: their autocorrelations are zero starting from some point.

Part (c)

If we replace all expectations in the definition of variance, we obtain the definition of conditional variance. From (1) and (4)

Var_{t-1}(z_{t})=E_{t-1}(z_{t}-E_{t-1}z_{t})^{2}=E_{t-1}\varepsilon_{t}^{2}=\sigma ^{2}.

By the law of total variance

(6) Var(z_{t})=EVar_{t-1}(z_{t})+Var(E_{t-1}z_{t})=\sigma ^{2}+Var(\gamma+\alpha z_{t-1}+\theta \varepsilon _{t-1})=

(an additive constant does not affect variance)

=\sigma ^{2}+Var(\alpha z_{t-1}+\theta \varepsilon _{t-1})=\sigma^{2}+\alpha ^{2}Var(z_{t})+2\alpha \theta Cov(z_{t-1},\varepsilon_{t-1})+\theta ^{2}Var(\varepsilon _{t-1}).

By the LIE and (3)

Cov(z_{t-1},\varepsilon _{t-1})=Cov(\gamma +\alpha z_{t-2}+\varepsilon  _{t-1}+\theta \varepsilon _{t-2},\varepsilon _{t-1})=\alpha  Cov(z_{t-2},\varepsilon _{t-1})+E\varepsilon _{t-1}^{2}+\theta  EE_{t-2}\varepsilon _{t-2}\varepsilon _{t-1}=\sigma ^{2}+\theta  E(\varepsilon _{t-2}E_{t-2}\varepsilon _{t-1}).

Here E_{t-2}\varepsilon _{t-1}=0, so

(7) Cov(z_{t-1},\varepsilon _{t-1})=\sigma ^{2}.

This equation leads to

Var(z_{t})=Var(\gamma +\alpha z_{t-1}+\varepsilon _{t}+\theta \varepsilon  _{t-1})=\alpha ^{2}Var(z_{t-1})+Var(\varepsilon _{t})+\theta  ^{2}Var(\varepsilon _{t-1})+

+2\alpha Cov(z_{t-1},\varepsilon _{t})+2\alpha \theta  Cov(z_{t-1},\varepsilon _{t-1})+2\theta Cov(\varepsilon _{t},\varepsilon  _{t-1})=\alpha ^{2}Var(z_{t})+\sigma ^{2}+\theta ^{2}\sigma ^{2}+2\alpha  \theta \sigma ^{2}

and, finally,

(8) Var(z_{t})=\frac{(1+2\alpha \theta +\theta ^{2})\sigma ^{2}}{1-\alpha  ^{2}}.

Part (d)

From (7)

(9) Cov(z_{t-1},\varepsilon _{t-2})=Cov(\gamma +\alpha z_{t-2}+\varepsilon  _{t-1}+\theta \varepsilon _{t-2},\varepsilon _{t-2})=\alpha  Cov(z_{t-2},\varepsilon _{t-2})+\theta Var(\varepsilon _{t-2})=(\alpha  +\theta )\sigma ^{2}.

It follows that

Cov(z_{t},z_{t-1})=Cov(\gamma +\alpha z_{t-1}+\varepsilon _{t}+\theta  \varepsilon _{t-1},\gamma +\alpha z_{t-2}+\varepsilon _{t-1}+\theta  \varepsilon _{t-2})=

(a constant is not correlated with anything)

=\alpha ^{2}Cov(z_{t-1},z_{t-2})+\alpha Cov(z_{t-1},\varepsilon  _{t-1})+\alpha \theta Cov(z_{t-1},\varepsilon _{t-2})+

+\alpha Cov(\varepsilon _{t},z_{t-2})+Cov(\varepsilon _{t},\varepsilon  _{t-1})+\theta Cov(\varepsilon _{t},\varepsilon _{t-2})+

+\theta \alpha Cov(\varepsilon _{t-1},z_{t-2})+\theta Var(\varepsilon  _{t-1})+\theta ^{2}Cov(\varepsilon _{t-1},\varepsilon _{t-2}).

From (7) Cov(z_{t-2},\varepsilon _{t-2})=\sigma ^{2} and from (9) Cov(z_{t-1},\varepsilon _{t-2})=(\alpha +\theta )\sigma ^{2}.

From (3) Cov(\varepsilon _{t},z_{t-2})=Cov(\varepsilon _{t-1},z_{t-2})=0.

Using also the white noise properties and stationarity of z_{t}

Cov(z_{t},z_{t-1})=Cov(z_{t-1},z_{t-2})=\gamma _{1},

we are left with

\gamma _{1}=\alpha ^{2}\gamma _{1}+\alpha \sigma  ^{2}+\alpha \theta (\alpha +\theta )\sigma ^{2}+\theta \sigma ^{2}=\alpha  ^{2}\gamma _{1}+(1+\alpha \theta )(\alpha +\theta )\sigma ^{2}.


\gamma _{1}=\frac{(1+\alpha \theta )(\alpha +\theta )\sigma ^{2}}{1-\alpha  ^{2}}

and using (8)

\rho _{0}=1, \rho _{1}=\frac{(1+\alpha \theta )(\alpha +\theta )}{  1+2\alpha \theta +\theta ^{2}}.

The finish is close.

Cov(z_{t},z_{t-2})=Cov(\gamma +\alpha z_{t-1}+\varepsilon _{t}+\theta  \varepsilon _{t-1},\gamma +\alpha z_{t-3}+\varepsilon _{t-2}+\theta  \varepsilon _{t-3})=

=\alpha ^{2}Cov(z_{t-1},z_{t-3})+\alpha Cov(z_{t-1},\varepsilon  _{t-2})+\alpha \theta Cov(z_{t-1},\varepsilon _{t-3})+

+\alpha Cov(\varepsilon _{t},z_{t-3})+Cov(\varepsilon _{t},\varepsilon  _{t-2})+\theta Cov(\varepsilon _{t},\varepsilon _{t-3})+

+\theta \alpha Cov(\varepsilon _{t-1},z_{t-3})+\theta Cov(\varepsilon  _{t-1},\varepsilon _{t-2})+\theta ^{2}Cov(\varepsilon _{t-1},\varepsilon  _{t-3}).

This simplifies to

(10) Cov(z_{t},z_{t-2})=\alpha ^{2}Cov(z_{t-1},z_{t-3})+\alpha (\alpha  +\theta )\sigma ^{2}+\alpha \theta Cov(z_{t-1},\varepsilon _{t-3}).

By (7)

Cov(z_{t-1},\varepsilon _{t-3})=Cov(\gamma +\alpha z_{t-2}+\varepsilon  _{t-1}+\theta \varepsilon _{t-2},\varepsilon _{t-3})=\alpha  Cov(z_{t-2},\varepsilon _{t-3})=

=\alpha Cov(\gamma +\alpha z_{t-3}+\varepsilon _{t-2}+\theta \varepsilon  _{t-3},\varepsilon _{t-3})=\alpha \sigma ^{2}+\alpha \theta \sigma ^{2}=\alpha (1+\theta )\sigma ^{2}.

Finally, using (10)

\gamma _{2}=\alpha ^{2}\gamma _{2}+\alpha (\alpha +\theta )\sigma  ^{2}+\alpha^2 \theta (1 +\theta )\sigma ^{2}=\alpha ^{2}\gamma  _{2}+\alpha\sigma^2 (\alpha +\theta +\alpha\theta +\alpha\theta^2)\sigma ^{2},

\gamma _{2}=\frac{\alpha\sigma^2 (\alpha +\theta +\alpha\theta +\alpha\theta^2)\sigma ^{2}}{1-\alpha  ^{2}},

\rho _{2}=\frac{\alpha\sigma^2 (\alpha +\theta +\alpha\theta +\alpha\theta^2)}{1+2\alpha \theta  +\theta ^{2}}.

A couple of errors have been corrected on June 22, 2021. Hope this is final.