For a random variable *y _{t}*, the

For a *static* conditional mean model, the conditioning set
of variables is measured contemporaneously with the dependent variable
*y _{t}*. An example of a static
conditional mean model is the ordinary linear regression model. Given $${x}_{t},$$ a row vector of exogenous covariates measured at time

$$E({y}_{t}|{x}_{t})={x}_{t}\beta $$

(that is, the conditioning set is $${\Omega}_{t}={x}_{t}$$).

In time series econometrics, there is often interest in the dynamic behavior of a
variable over time. A *dynamic* conditional mean model
specifies the expected value of *y _{t}* as a
function of historical information. Let

Past observations,

*y*_{1},*y*_{2},...,*y*_{t–1}Vectors of past exogenous variables, $${x}_{1},{x}_{2},\dots ,{x}_{t-1}$$

Past innovations, $${\epsilon}_{1},{\epsilon}_{2},\dots ,{\epsilon}_{t-1}$$

By definition, a covariance stationary stochastic process has an unconditional
mean that is constant with respect to time. That is, if
*y _{t}* is a stationary stochastic
process, then $$E({y}_{t})=\mu $$ for all times

The constant mean assumption of stationarity does not preclude the possibility of
a dynamic conditional expectation process. The serial autocorrelation between lagged
observations exhibited by many time series suggests the expected value of
*y _{t}* depends on historical
information. By Wold’s decomposition [2], you can write the conditional mean of any stationary process

$$E({y}_{t}|{H}_{t-1})=\mu +{\displaystyle \sum _{i=1}^{\infty}{\psi}_{i}{\epsilon}_{t-i},}$$ | (1) |

Any model of the general linear form given by Equation 1 is a valid specification for the dynamic behavior of a stationary stochastic process. Special cases of stationary stochastic processes are the autoregressive (AR) model, moving average (MA) model, and the autoregressive moving average (ARMA) model.

[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.

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