In coatless/timeseriesisgreat: Time Series is Great!

Stationarity

There are two kinds of stationarity: Strong and Weak Stationarity. These types of stationarity are not equivalent and the presence of one kind of stationarity does not imply the other. That is, a time series can be strongly stationary but not weakly stationary and vice versa. In very rare cases, a time series can be both strong and weakly stationary. The most common form of stationarity is that of weakly stationarity.

Weak Stationarity

Definition: Weak Stationarity or Second-order Stationarity

The mean and autocovariance of the stochastic process are finite and invariant under a shift in time, i.e. $$\begin{aligned} E\left[X_t \right] &= \mu_t = \mu < \infty \ cov\left(X_t, X_{t+h} \right) &= cov\left(X_{t+k}, X_{t+h+k}\right) = \gamma \left(h\right) \end{aligned}$$

Strong Stationarity

Definition: Strong Stationarity or Strict Stationarity

The joint probability distribution of $\left(X_t \right)$, $t \in N$ is invariant under a shift in time, i.e. $$P\left(X_t \le x_1, \ldots, X_{t+k} \le x_k \right) = P\left(X_{t+h} \le x_1, \ldots, X_{t+h+k} \le x_k \right)$$ for any time shift $h$ and any $x_1, x_2, \ldots, x_k$ belong to the domain of $X_{t_1}, X_{t_2}, \ldots, X_{t_k}$ respectively.

So to summarize, we have weak stationarity that relies on how separated each observation is rather than their location in time. On the flip side, we have strict stationarity that relies on the location in time instead of how separated observations are.

Stationarity of $X_t$ matters, because it provides the framework in which averaging makes sense. Unless properties like mean and covariance are either fixed or "evolve" in a known manner, we cannot average the observed data.

With this being said, here are a few examples of stationarity:

1. $X_t \sim Cauchy$ is strictly stationary but NOT weakly stationary.
   • The strong stationarity exists due to the symmetric properties of the distribution.
   • It cannot be weakly stationary because it has an infinite variance!
2. $X_{2t} = U_{2t}, X_{2t+1} = V_{2t+1} \forall t$ where ${U_t}\mathop \sim \limits^{iid} N\left( {1,1} \right)$ and ${V_t}\mathop \sim \limits^{iid} Exponential\left( 1 \right)$ is weakly stationary but NOT strictly stationary.
   • The weak stationary exists since the mean is constant ($\mu = 1$) and the variance does not depend on time ($\sigma ^2 = 1$).
   • It cannot be strongly stationary due to values not aligning in time.

Regarding white noises, we can obtain different levels of stationarity depending on the assumption:

1. If $X_t \sim WN$, e.g. uncorrelated observations with a finite variance, then it is weakly stationary but NOT strictly stationary.
2. If $X_t \mathop \sim \limits^{iid} NWN$, e.g. normally distributed independent observations with a finite variance, then it is weakly stationary AND strictly stationary.

The autocovariance of weakly stationary processes has the following properties:

1. $\gamma \left(0\right) = var\left[X_t \right] \ge 0$ (variance)
2. $\gamma \left(h\right) = \gamma \left(-h\right)$ (function is even / symmetric)
3. $\left| \gamma \left(h\right) \right| \le \gamma \left( 0 \right) \forall h.$

We obtain these properties through:

1. [\gamma \left( 0 \right) = Var\left( {{x_t}} \right) = E\left[ {{{\left( {{x_t} - \mu } \right)}^2}} \right] = \sum\limits_{t = 1}^T {{p_t}{{\left( {{x_t} - \mu } \right)}^2}} = {p_1}{\left( {{x_1} - \mu } \right)^2} + \cdots + {p_T}{\left( {{x_T} - \mu } \right)^2} \ge 0]
2. [\begin{aligned} \gamma \left( h \right) &= \gamma \left( {t + h - t} \right) \ &= E\left[ {\left( {{x_{t + h}} - \mu } \right)\left( {{x_t} - \mu } \right)} \right] \ &= E\left[ {\left( {{x_t} - \mu } \right)\left( {{x_{t + h}} - \mu } \right)} \right] \ &= \gamma \left( {t - \left( {t + h} \right)} \right) \ &= \gamma \left( { - h} \right) \end{aligned}]
3. Using the Cauchy-Schwarz Inequality, ${\left( {E\left[ {XY} \right]} \right)^2} \le E\left[ {{X^2}} \right]E\left[ {{Y^2}} \right]$, we have: [\begin{aligned} {\left( {\left| {\gamma \left( h \right)} \right|} \right)^2} &= {\left( {\gamma \left( h \right)} \right)^2} \ &= {\left( {E\left[ {\left( {{x_t} - \mu } \right)\left( {{x_{t + h}} - \mu } \right)} \right]} \right)^2} \ &\le E\left[ {{{\left( {{x_t} - \mu } \right)}^2}} \right]E\left[ {{{\left( {{x_{t + h}} - \mu } \right)}^2}} \right] \ &= {\left( {\gamma \left( 0 \right)} \right)^2} \ {\left( {\gamma \left( h \right)} \right)^2} &\le {\left( {\gamma \left( 0 \right)} \right)^2} \ \left| {\gamma \left( h \right)} \right| &\le \gamma \left( 0 \right) \end{aligned}]

Is it weakly stationary?

In order to verify if it is weakly stationary, we must make sure the time series satisfies:

1. $E\left[y_t \right] = \mu_t = \mu < \infty$
2. $cov\left(y_t, Y_{t+h} \right) = \gamma \left(h\right)$

Is a random walk weakly stationary?

First up, is a RW stationary? By intuition, the answer should be "no" since there is a randomness component that cannot be accounted for when looking for a pattern. But, we need to prove that.

1. [\begin{aligned} E\left[ {{y_t}} \right] &= E\left[ {{y_{t - 1}} + {w_t}} \right] \ &= E\left[ {\sum\limits_{i = 1}^t {{w_t}} + {Y_0}} \right] \ &= E\left[ {\sum\limits_{i = 1}^t {{w_t}} } \right] + {Y_0} \ &= 0 + c \ &= c \ \end{aligned} ] Note, the mean here is constant since it depends only on the value of the first term in the sequence.
2. [\begin{aligned} Var\left( {{y_t}} \right) &= Var\left( {\sum\limits_{i = 1}^t {{w_t}} + {Y_0}} \right) \ &= Var\left( {\sum\limits_{i = 1}^t {{w_t}} } \right) + \underbrace {Var\left( {{Y_0}} \right)}{ = 0{\text{ constant}}} \ &= \sum\limits{i = 1}^t {Var\left( {{w_t}} \right)} \ &= t\sigma w^2 \ &\Rightarrow Cov\left( {{y_t},{y{t + h}}} \right) \ne \gamma \left( h \right) \ \end{aligned}] Alas, the variance has a dependence on time. This causes the $Var\left(y_t\right) \ge \infty$ as $t \rightarrow \infty$. As a result, the process is not weakly stationary.

Continuing on just to obtain the covariance, we have: [\begin{aligned} \gamma \left( h \right) &= Cov\left( {{y_t},{y_{t + h}}} \right) \ &= Cov\left( {\sum\limits_{i = 1}^t {{w_i}} ,\sum\limits_{j = 1}^{t + h} {{w_j}} } \right) \ &= Cov\left( {\sum\limits_{i = 1}^t {{w_i}} ,\sum\limits_{j = 1}^t {{w_j}} } \right) \ &= \min \left( {t,t + h} \right)\sigma _w^2 \ &= \left( {t + \min \left( {0,h} \right)} \right)\sigma _w^2 \ \end{aligned} ]

Is an MA(1) Stationary?

1. [\begin{aligned} E\left[ {{y_t}} \right] &= E\left[ {{\theta 1}{w{t - 1}} + {w_t}} \right] \ &= {\theta 1}E\left[ {{w{t - 1}}} \right] + E\left[ {{w_t}} \right] \ &= 0 \ \end{aligned}] The mean is constant over time. So the first criterion is okay.
2. [\begin{aligned} Cov\left( {{y_t},{y_{t + h}}} \right) &= E\left[ {\left( {{y_t} - E\left[ {{y_t}} \right]} \right)\left( {{y_{t + h}} - E\left[ {{y_{t + h}}} \right]} \right)} \right] \ &= E\left[ {{y_t}{y_{t + h}}} \right] - \underbrace {E\left[ {{y_t}} \right]}{ = 0}\underbrace {E\left[ {{y{t + h}}} \right]}{ = 0} \ &= E\left[ {\left( {{\theta _1}{w{t - 1}} + {w_t}} \right)\left( {{\theta 1}{w{t + h - 1}} + {w_{t + h}}} \right)} \right] \ &= E\left[ {\theta 1^2{w{t - 1}}{w_{t + h - 1}} + \theta {w_t}{w_{t + h}} + {\theta 1}{w{t - 1}}{w_{t + h}} + {w_t}{w_{t + h}}} \right] \ &\ E\left[ {{w_t}{w_{t + h}}} \right] &= \operatorname{cov} \left( {{w_t},{w_{t + h}}} \right) + E\left[ {{w_t}} \right]E\left[ {{w_{t + h}}} \right] = {1_{\left{ {h = 0} \right}}}\sigma w^2 \ \ &\Rightarrow Cov\left( {{y_t},{y{t + h}}} \right) = \left( {\theta 1^2{1{\left{ {h = 0} \right}}} + {\theta 1}{1{\left{ {h = 1} \right}}} + {\theta 1}{1{\left{ {h = - 1} \right}}} + {1_{\left{ {h = 0} \right}}}} \right)\sigma _w^2 \ \gamma \left( h \right) &= \left{ {\begin{array}{*{20}{c}} {\left( {\theta _1^2 + 1} \right)\sigma _w^2}&{h = 0} \ {{\theta _1}\sigma _w^2}&{\left| h \right| = 1} \ 0&{\left| h \right| > 1} \end{array}} \right. \end{aligned} ] Note, the autocovariance function does not depend on time. Thus, the second weakly stationary criterion is satisfied.

The MA(1) process is weakly stationary since both the mean and variance are constant over time.

As a bonus, note that we also can easily obtain the autocorrelation function (ACF) $$\Rightarrow \rho \left( h \right) = \left{ {\begin{array}{*{20}{c}} 1&{h = 0} \ {\frac{{{\theta _1}\sigma _w^2}}{{\left( {\theta _1^2 + 1} \right)\sigma _w^2}} = \frac{{{\theta _1}}}{{\theta _1^2 + 1}}}&{\left| h \right| = 1} \ 0&{\left| h \right| > 1} \end{array}} \right.$$

Is an AR(1) Stationary?

Consider the AR(1) process given as: $${y_t} = {\phi 1}{y{t - 1}} + {w_t} \text{, where } {w_t}\mathop \sim \limits^{iid} WN\left( {0,\sigma _w^2} \right)$$

This process was shown to simplify to:

$$y_t = {\phi ^t}{y_0} + \sum\limits_{i = 0}^{t - 1} {\phi 1^i{w{t - i}}}$$

In addition, we add the requirement that $\left| \phi 1 \right| < 1$. This requirement allows for the process to be stationary. If $\phi _1 \ge 1$, the process would not converge. This way the process will be able to be written as a geometric series that converges: [\sum\limits{k = 0}^\infty {{r^k}} = \frac{1}{{1 - r}},{\text{ }}\left| r \right| < 1]

Next, we demonstrate how crucial this property is:

[\begin{aligned} \mathop {\lim }\limits_{t \to \infty } E\left[ {{y_t}} \right] &= \mathop {\lim }\limits_{t \to \infty } E\left[ {{\phi ^t}{y_0} + \sum\limits_{i = 0}^{t - 1} {\phi 1^i{w{t - i}}} } \right] \ &= \mathop {\lim }\limits_{t \to \infty } \underbrace {{\phi ^t}{y_0}}{\left| \phi \right| < 1 \Rightarrow t \to \infty {\text{ = 0}}} + \sum\limits{i = 0}^{t - 1} {\phi 1^i\underbrace {E\left[ {{w{t - i}}} \right]}{ = 0}} \ &= 0 \ \mathop {\lim }\limits{t \to \infty } Var\left( {{y_t}} \right) &= \mathop {\lim }\limits_{t \to \infty } Var\left( {{\phi ^t}{y_0} + \sum\limits_{i = 0}^{t - 1} {\phi 1^i{w{t - i}}} } \right) \ &= \mathop {\lim }\limits_{t \to \infty } \underbrace {Var\left( {{\phi ^t}{y_0}} \right)}{ = 0{\text{ since constant}}} + Var\left( {\sum\limits{i = 0}^{t - 1} {\phi 1^i{w{t - i}}} } \right) \ &= \mathop {\lim }\limits_{t \to \infty } \sum\limits_{i = 0}^{t - 1} {\phi 1^{2i}Var\left( {{w{t - i}}} \right)} \ &= \mathop {\lim }\limits_{t \to \infty } \sigma w^2\sum\limits{i = 0}^{t - 1} {\phi 1^{2i}} \ &= \sigma _w^2 \cdot
\underbrace {\frac{1}{{1 - {\phi ^2}}}}{\begin{subarray}{l} {\text{Geometric Series}} \end{subarray}} \end{aligned} ]

This leads us to being able to conclude the autocovariance function is: [\begin{aligned} Cov\left( {{y_t},{y_{t + h}}} \right) &= Cov\left( {{y_t},\phi {y_{t + h - 1}} + {w_{t + h}}} \right) \ &= Cov\left( {{y_t},\phi {y_{t + h - 1}}} \right) \ &= Cov\left( {{y_t},{\phi ^{\left| h \right|}}{y_t}} \right) \ &= {\phi ^{\left| h \right|}}Cov\left( {{y_t},{y_t}} \right) \ &= {\phi ^{\left| h \right|}}Var\left( {{y_t}} \right) \ &= {\phi ^{\left| h \right|}}\frac{{\sigma _w^2}}{{1 - \phi _1^2}} \ \end{aligned} ]

Both the mean and autocovariance function do not depend on time and, thus, the AR(1) process is stationary if $\left| \phi _1 \right| < 1$.

If we assume that the AR(1) process is stationary, we can derive the mean and variance in another way. Without a loss of generality, we'll assume $y_0 = 0$.

Therefore:

[\begin{aligned} {y_t} &= {\phi t}{y{t - 1}} + {w_t} \ &= {\phi 1}\left( {{\phi _1}{y{t - 2}} + {w_{t - 1}}} \right) + {w_t} \ &= \phi 1^2{y{t - 2}} + {\phi 1}{w{t - 1}} + {w_t} \ &\vdots \ &= \sum\limits_{i = 0}^{t - 1} {\phi 1^i{w{t - i}}} \ & \ E\left[ {{y_t}} \right] &= E\left[ {\sum\limits_{i = 0}^{t - 1} {\phi 1^i{w{t - i}}} } \right] \ &= \sum\limits_{i = 0}^{t - 1} {\phi 1^i\underbrace {E\left[ {{w{t - i}}} \right]}{ = 0}} \ &= 0 \ &\ Var\left( {{y_t}} \right) &= E\left[ {{{\left( {{y_t} - E\left[ {{y_t}} \right]} \right)}^2}} \right] \ &= E\left[ {y_t^2} \right] - {\left( {E\left[ {{y_t}} \right]} \right)^2} \ &= E\left[ {y_t^2} \right] \ &= E\left[ {{{\left( {{\phi _1}{y{t - 1}} + {w_t}} \right)}^2}} \right] \ &= E\left[ {\phi 1^2y{t - 1}^2 + w_t^2 + 2{\phi 1}{y_t}{w_t}} \right] \ &= \phi _1^2E\left[ {y{t - 1}^2} \right] + \underbrace {E\left[ {w_t^2} \right]}{ = \sigma _w^2} + 2{\phi _1}\underbrace {E\left[ {{y_t}} \right]}{ = 0}\underbrace {E\left[ {{w_t}} \right]}{ = 0} \ &= \underbrace {\phi _1^2Var\left( {{y{t - 1}}} \right) + \sigma w^2 = \phi _1^2Var\left( {{y_t}} \right) + \sigma _w^2}{{\text{Assume stationarity}}} \ Var\left( {{y_t}} \right) &= \phi _1^2Var\left( {{y_t}} \right) + \sigma _w^2 \ Var\left( {{y_t}} \right) - \phi _1^2Var\left( {{y_t}} \right) &= \sigma _w^2 \ Var\left( {{y_t}} \right)\left( {1 - \phi _1^2} \right) &= \sigma _w^2 \ Var\left( {{y_t}} \right) &= \frac{{\sigma _w^2}}{{1 - \phi _1^2}} \ \end{aligned} ]

Joint Stationarity

Two time series, say $\left(X_t \right)$ and $\left(Y_t\right)$, are said to be jointly stationary if they are each stationary, and the cross-covariance function

[{\gamma {XY}}\left( {t,t + h} \right) = Cov\left( {{X_t},{Y{t + h}}} \right) = {\gamma _{XY}}\left( h \right)]

is a function only of lag $h$.

The cross-correlation function for jointly stationary times can be expressed as:

[{\rho {XY}}\left( {t,t + h} \right) = \frac{{{\gamma {XY}}\left( {t,t + h} \right)}}{{{\sigma {{X_t}}}{\sigma {{Y_{t + h}}}}}} = \frac{{{\gamma {XY}}\left( h \right)}}{{{\sigma {{X_t}}}{\sigma {{Y{t + h}}}}}} = {\rho _{XY}}\left( h \right)]

The Backshift Operator and Differencing Operations

Definition: Backshift Operator

The Backshift Operator is helpful when manipulating time series. When we backshift, we are changing the indices of the time series. e.g. $t \rightarrow t-1$. The operator is defined as:

[B{x_t} = {x_{t - 1}}]

If we were to repeatedly apply the backshift operator, we would receive:

[\begin{aligned} {B^2}{x_t} &= B\left( {B{x_t}} \right) \ &= B\left( {{x_{t - 1}}} \right) \ &= {x_{t - 2}} \ \end{aligned}]

We can generalize this behavior as: $${B^k}{x_t} = {x_{t - k}}$$

The backshift operator is helpful for later decompositions in addition to making differencing operations more straightforward.

Definition: Differencing Operator

The Differencing Operator is defined as the gradient symbol applied to a time series: [\nabla {x_t} = {x_t} - {x_{t - 1}}]

The differencing operator is helpful when trying to remove trend from the data.

We can take higher moments of differences by: [\begin{aligned} {\nabla ^2}{x_t} &= \nabla \left( {\nabla {x_t}} \right) \ &= \nabla \left( {{x_t} - {x_{t - 1}}} \right) \ &= \left( {{x_t} - {x_{t - 1}}} \right) - \left( {{x_{t - 1}} - {x_{t - 2}}} \right) \ &= {x_t} - 2{x_{t - 1}} + {x_{t - 2}} \ \end{aligned} ]

So, the difference operator has the following properties: [\begin{aligned} {\nabla ^k}{x_t} &= {\nabla ^{k - 1}} \left( {\nabla {x_t}}\right) \hfill \ {\nabla ^1}{x_t} &= \nabla {x_t} \hfill \ \end{aligned} ]

Notice, within the difference operation, we are backshifting the timeseries.

If we rewrite the difference operator to use the backshift operator, we receive: [\nabla {x_t} = {x_t} - {x_{t - 1}} = \left( {1 - B} \right){x_t}]

This holds for later incarnations as well: [\begin{aligned} {\nabla ^2}{x_t} &= {x_t} - 2{x_{t - 1}} + {x_{t - 2}} \hfill \ &= \left( {1 - B} \right)\left( {1 - B} \right){x_t} \hfill \ &= {\left( {1 - B} \right)^2}{x_t} \hfill \ \end{aligned} ]

Thus, we can generalize this to: [{\nabla ^k}{x_t} = {\left( {1 - B} \right)^k}{x_t}]

coatless/timeseriesisgreat documentation built on May 13, 2019, 8:47 p.m.