Moving Average Model
Moving average model of order 1
The moving average
(MA) model is a linear combination of $Z$’s. The MA(1) model is given by:
$$ X_t = \mu + Z_t + \theta Z_{t1} $$
where $\mu$ is the overall mean, $\theta$ is a moving average coefficient, and $Z_t \overset{i.i.d.}{\sim} N(0, \sigma^2)$.
Note that many books use $\theta$ in the formula, but we’re following R’s convention with the $+\theta$.
Mean and variance
The expectation is
$$ E(X_t) = \mu + E(Z_t) + \theta E(Z_{t1}) = \mu $$
Setting $\mu = 0$, the variance is
$$ Var(X_t) = Var(Z_t + \theta Z_{t1}) = Var(Z_t) + \theta^2 Var(Z_{t1}) = (1 + \theta^2)\sigma^2 $$
Autocovariance and autocorrelation
The autocovariance function of lag 1 is
$$
\begin{aligned}
\gamma_X(1) &= Cov(X_t, X_{t1}) \\
&= Cov(Z_t + \theta Z_{t1}, X_{t1}) \\
&= Cov(Z_t, X_{t1}) + \theta Cov(Z_{t1}, X_{t1}) \\
&= \theta Cov(Z_{t1}, Z_{t1} + \theta Z_{t2}) \\
&= \theta \left[ Cov(Z_{t1}, Z_{t1}) + \theta Cov(Z_{t1}, Z_{t2}) \right] \\
&= \theta \sigma^2
\end{aligned}
$$
Thus the autocorrelation of lag 1 is
$$ \rho_X(1) = \frac{\gamma_X(1)}{\gamma_X(0)} = \frac{\theta}{1 + \theta^2} $$
For time lags of 2 and above,
$$
\begin{aligned}
\gamma_X(k) &= Cov(X_t, X_{tk}) \\
&= Cov(Z_t + \theta Z_{t1}, X_{tk}) \\
&= Cov(Z_t, X_{tk}) + \theta Cov(Z_{t1}, X_{tk}) \\
&= 0, \quad k = 2, 3, \cdots
\end{aligned}
$$
which means
$$ \rho_X(k) = 0,\quad k = 2, 3, \cdots $$
A sample ACF with significant autocorrelation only at lag 1 is an indicator of a potential MA(1) model.
Partial autocorrelation
For the MA(1) model, the PACF is
$$ \phi_{00} = 1, \phi_{11} = \frac{\theta}{1 + \theta^2} = \rho(1), \phi_{kk} = \frac{\theta^k(1\theta^2)}{1  \theta^{2(k+1)}} \text{ for } k > 1 $$
The PACF od MA(1) tails off after lag 1.
Simulation in R
We can use the arima.sim()
function to simulate the MA model, specifying the model
parameter as list(ma = theta)
. Of course we can also write our own function to generate a series manually:


Here’s an example of a simulated MA(1) series with $n=200$ timepoints^{1}. We can see the tail off pattern in the ACF and the cut off after lag 1 in the PACF.
Moving average model of order $q$
The model is
$$ X_t = \mu + Z_t + \theta_1 Z_{t1} + \theta_2 Z_{t2} + \cdots + \theta_q Z_{tq} $$
where the $\theta_j$’s are moving average coefficients and $Z_t$ is $WN(0, \sigma^2)$.
Mean and variance
The terms are all independent, so we don’t have to worry about covariance terms when finding the variance:
$$ E(X_t) = \mu, Var(X_t) = (1 + \theta_1^2 + \theta_2^2 + \cdots + \theta_q^2)\sigma^2 $$
MA(q) is always stationary.
Autocovariance and autocorrelation
The autocovariance for lag 1 is:
$$
\begin{aligned}
\gamma_X(1) = &= E\left[(Z_t + \theta_1 Z_{t1} + \cdots + \theta_q Z_{tq})(Z_{t1} + \theta_1 Z_{t2} + \cdots + \theta_q Z_{t1q}) \right] \\
&= (\theta_1 + \theta_2 \theta_1 + \theta_3 \theta_2 + \cdots + \theta_q \theta_{q1})\sigma^2
\end{aligned}
$$
The autocovariance function for lag $k$ is:
$$
\begin{aligned}
\gamma_X(1) = &= E\left[(Z_t + \theta_1 Z_{t1} + \cdots + \theta_q Z_{tq})(Z_{tk} + \theta_1 Z_{tk1} + \cdots + \theta_q Z_{tkq}) \right] \\
&= \begin{cases}
(\theta_k + \theta_{k+1} \theta_1 + \theta_{k+2} \theta_2 + \cdots + \theta_q \theta_{qk})\sigma^2, & k = 1, 2, \cdots, q \\
0, & k > q
\end{cases}
\end{aligned}
$$
From this we can find the autocorrelation function for lag $k$:
$$
\rho_X(k) = \begin{cases}
\frac{\theta_k + \theta_{k+1}\theta_1 + \theta_{k+2}\theta_2 + \cdots + \theta_q\theta_{qk}}{1 + \theta_1^2 + \theta_2^2 + \cdots + \theta_q^2}, & k = 1, 2, \cdots, q \\
0, & k > q
\end{cases}
$$
To summarize the differences between the two models:
Model  ACF  PACF 

AR(1)  Tail off  Cut off after lag 1 
AR(p)  Tail off  Cut off after lag $p$ 
MA(1)  Cut off after lag 1  Tail off 
MA(q)  Cut off after lag $p$  Tail off 
Invertibility
We’re interested in expressing an MA series as an AR series. AR series are more intuitive because $X_t$ is a linear combination of the past data, and the AR coefficients can be directly interpreted.
Under certain conditions, we can invert an MA series to an AR series. Taking a zeromean MA(1) series as an example:
$$ X_t = Z_t + \theta Z_{t1}, \quad Z_0 = 0 $$
$$
\begin{aligned}
X_1 &= Z_1 + \theta Z_0 = Z_1 \\
X_2 &= Z_2 + \theta Z_1 = Z_2 + \theta X_1 \Rightarrow Z_2 = X_2  \theta X_1 \\
X_3 &= Z_3 + \theta Z_2 = Z_3 + \theta(X_2  \theta X_1) = Z_3 + \theta X_2  \theta^2 X_1 \\
&\,\vdots \\
X_t &= Z_t + \theta Z_{t1} = Z_t + \theta X_{t1}  \theta^2 Z_{t2} \\
&= \cdots = Z_t + \theta X_{t1}  \theta^2 X_{t2} + \cdots  (\theta)^k X_{tk}  (\theta)^{k+1} \underbrace{Z_{tk1}}_{\rightarrow Z_0}
\end{aligned}
$$
By repeated substitutions,
$$ Z_t = X_t + (\theta) X_{t1} + (\theta)^2 X_{t2} + \cdots + (\theta)^k X_{tk} + \cdots \quad \text{if } \theta < 1 $$
The above is not always possible. For example, if $\theta > 1$, the current value will be heavily dependent on data faraway in the past, which doesn’t make sense in a time series. Late we’ll explain how to find this condition systematically.
The current $Z_t$ (shock
) is a linear combination of the present and past $X_t$ (returns
). Since the remote value $X_{tj}$ should have very little impact on the current shock, $\theta < 1$. Such an MA(1) model is said to be invertible
.
Instead of checking stationarity, we usually check invertibility for MA series. The invertibility assures the uniqueness of the connection between values of $\theta$ and $\rho(1)$ in MA(1)  for any value of $\theta$, the reciprocal $\frac{1}{\theta}$ gives the same value for
$$ \rho(1) = \frac{\theta}{1 + \theta^2} = \frac{\frac{\theta}{\theta^2}}{\frac{1 + \theta^2}{\theta^2}} = \frac{\frac{1}{\theta}}{1 + \frac{1}{\theta^2}} $$
Generally, an MA model is invertible if it’s equivalent to a converging infinite order AR model, where converging
means that the AR coefficients decrease to 0 as we move back in time.

R code for simulating the MA(1) series.
↩︎1 2 3 4 5
set.seed(1) x < arima.sim(model = list(ma = 0.7), n = 200, rand.gen = rnorm) plot_time_series(x, x_type = "MA(1)")
Sep 12  ARMA Model  8 min read 
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