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Many observed time series exhibit serial autocorrelation; that
is, linear association between lagged observations. This suggests
past observations might predict current observations. The autoregressive
(AR) process models the conditional mean of *y _{t}* as
a function of past observations, $${y}_{t-1},{y}_{t-2},\dots ,{y}_{t-p}$$.
An AR process that depends on

The form of the AR(*p*) model in Econometrics
Toolbox™ is

$${y}_{t}=c+{\varphi}_{1}{y}_{t-1}+\dots +{\varphi}_{p}{y}_{t-p}+{\epsilon}_{t},$$ | (6-6) |

In lag operator polynomial notation, $${L}^{i}{y}_{t}={y}_{t-i}$$. Define the degree *p* AR
lag operator polynomial $$\varphi (L)=(1-{\varphi}_{1}L-\dots -{\varphi}_{p}{L}^{p})$$ . You can write
the AR(*p*) model as

$$\varphi (L){y}_{t}=c+{\epsilon}_{t}.$$ | (6-7) |

Consider the AR(*p*) model in lag operator
notation,

$$\varphi (L){y}_{t}=c+{\epsilon}_{t}.$$

$${y}_{t}=\mu +{\varphi}^{-1}(L){\epsilon}_{t}=\mu +\psi (L){\epsilon}_{t},$$ | (6-8) |

$$\mu =\frac{c}{\left(1-{\varphi}_{1}-\dots -{\varphi}_{p}\right)}$$

The `Constant`

property of an `arima`

model
object corresponds to *c*, and not the unconditional
mean *μ*.

By Wold’s decomposition [2], Equation 6-8 corresponds to a stationary stochastic
process provided the coefficients $${\psi}_{i}$$ are
absolutely summable. This is the case when the AR polynomial, $$\varphi (L)$$, is *stable*,
meaning all its roots lie outside the unit circle.

Econometrics
Toolbox enforces stability of the AR polynomial.
When you specify an AR model using `arima`

, you get
an error if you enter coefficients that do not correspond to a stable
polynomial. Similarly, `estimate`

imposes stationarity
constraints during estimation.

[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.

- Specify Conditional Mean Models Using arima
- AR Model Specifications
- Plot the Impulse Response Function

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