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A time series *y _{t}* is
a collection of observations on a variable indexed sequentially over
several time points

The goal of statistical modeling is finding a compact representation
of the data-generating process for your data. The statistical building
block of econometric time series modeling is the stochastic process.
Heuristically, a *stochastic process* is a joint
probability distribution for a collection of random variables. By
modeling the observed time series *y _{t}* as
a realization from a stochastic process $$y=\left\{{y}_{t};t=1,\mathrm{...},T\right\}$$, it is possible to accommodate
the high-dimensional and dependent nature of the data. The set of
observation times

**Figure 1-1, Monthly Average CO2**

Stochastic processes are *weakly stationary* or *covariance
stationary* (or simply, *stationary*)
if their first two moments are finite and constant over time. Specifically,
if *y _{t}* is a stationary stochastic
process, then for all

*E*(*y*) =_{t}*μ*< ∞.*V*(*y*) = $${\sigma}^{2}$$ < ∞._{t}*Cov*(*y*,_{t}*y*) =_{t–h}*γ*for all lags $$h\ne 0.$$_{h}

Does a plot of your stochastic process seem to increase or decrease
without bound? The answer to this question indicates whether the stochastic
process is stationary. “Yes” indicates that the stochastic
process might be nonstationary. In Figure 1-1, Monthly Average CO2, the
concentration of CO_{2} is increasing without
bound which indicates a nonstationary stochastic process.

Wold’s theorem [2] states that you can write all weakly stationary stochastic processes in the general linear form

$${y}_{t}=\mu +{\displaystyle \sum _{i=1}^{\infty}{\psi}_{i}{\epsilon}_{t-i}}+{\epsilon}_{t}.$$

A linear time series model is a *unit root process* if
the solution set to its characteristic equation contains
a root that is on the unit circle (i.e., has an absolute value of
one). Subsequently, the expected value, variance, or covariance of
the elements of the stochastic process grows with time, and therefore
is nonstationary. If your series has a unit root, then differencing
it might make it stationary.

For example, consider the linear time series model $${y}_{t}={y}_{t-1}+{\epsilon}_{t},$$ where $${\epsilon}_{t}$$ is a white noise sequence of
innovations with variance *σ ^{2}* (this
is called the random walk). The characteristic equation of this model
is $$z-1=0,$$ which has a root of one. If
the initial observation

$$E({d}_{t})=0,$$ which is independent of time,

$$V({d}_{t})={\sigma}^{2},$$ which is independent of time, and

$$Cov({d}_{t},{d}_{t-s})=0,$$ which is independent of time for all integers

*0 < s < t*.

Figure 1-1, Monthly Average CO2 appears
nonstationary. What happens if you plot the first difference *d _{t}* =

**Figure 1-2, Monthly Difference in CO2**

The *lag operator* *L* operates
on a time series *y _{t}* such
that $${L}^{i}{y}_{t}={y}_{t-i}$$.

An *m*th-degree lag polynomial of coefficients *b*_{1}, *b*_{2},...,*b _{m}* is
defined as

$$B(L)=(1+{b}_{1}L+{b}_{2}{L}^{2}+\dots +{b}_{m}{L}^{m}).$$

In lag operator notation, you can write the general linear model using an infinite-degree polynomial $$\psi (L)=(1+{\psi}_{1}L+{\psi}_{2}{L}^{2}+\dots ),$$

$${y}_{t}=\mu +\psi (L){\epsilon}_{t}.$$

You cannot estimate a model that has an infinite-degree polynomial of coefficients with a finite amount of data. However, if $$\psi (L)$$ is a rational polynomial (or approximately rational), you can write it (at least approximately) as the quotient of two finite-degree polynomials.

Define the *q*-degree polynomial $$\theta (L)=(1+{\theta}_{1}L+{\theta}_{2}{L}^{2}+\dots +{\theta}_{q}{L}^{q})$$ and the *p*-degree
polynomial $$\varphi (L)=(1+{\varphi}_{1}L+{\varphi}_{2}{L}^{2}+\dots +{\varphi}_{p}{L}^{p})$$. If $$\psi (L)$$ is rational, then

$$\psi (L)=\frac{\theta (L)}{\varphi (L)}.$$

Thus, by Wold’s theorem, you can model (or closely approximate) every stationary stochastic process as

$${y}_{t}=\mu +\frac{\theta (L)}{\varphi (L)}{\epsilon}_{t},$$

A degree *p* *characteristic polynomial* of
the linear times series model $${y}_{t}={\varphi}_{1}{y}_{t-1}+{\varphi}_{2}{y}_{t-2}+\mathrm{...}+{\varphi}_{p}{y}_{t-p}+{\epsilon}_{t}$$ is

$$\varphi (a)={a}^{p}-{\varphi}_{1}{a}^{p-1}-{\varphi}_{2}{a}^{p-2}-\mathrm{...}-{\varphi}_{p}.$$

The roots of the *homogeneous characteristic equation* $$\varphi (a)=0$$ (called the *characteristic
roots*) determine whether the linear time series is stationary.
If every root in $$\varphi (a)$$ lies inside the
unit circle, then the process is stationary. Roots lie within the
unit circle if they have an absolute value less than one. This is
a unit root process if one or more roots lie inside the unit circle
(i.e., have absolute value of one). Continuing the example, the characteristic
roots of $$\varphi (a)=0$$ are $$a=\{0.4562,0.0438\}.$$ Since the absolute values of
these roots are less than one, the linear time series model is stationary.

[1] Box, G. E. P., G. M. Jenkins, and G. C.
Reinsel. *Time Series Analysis: Forecasting and Control*.
3rd ed. Englewood Cliffs, NJ: Prentice Hall, 1994.

[2] Wold, H. *A
Study in the Analysis of Stationary Time Series*. Uppsala,
Sweden: Almqvist & Wiksell, 1938.

[3] Tans, P., and R. Keeling. (2012, August).
“Trends in Atmospheric Carbon Dioxide.” *NOAA
Research.* Retrieved October 5, 2012 from `http://www.esrl.noaa.gov/gmd/ccgg/trends/mlo.html`

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- Specify Conditional Mean Models Using arima
- Specify GARCH Models Using garch
- Specify EGARCH Models Using egarch
- Specify GJR Models Using gjr
- Simulate Stationary Processes
- Assess Stationarity of a Time Series

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