# Wold Decomposition Theorem and Moving Average model - Error Terms

I'm stuck with Wold's decomposition theorem in time series analysis. The theorem says that every stationary time series can be written as a sum of two components, one being entirely deterministic (here represented with mean) and one being stochastic. Here is the formula from my book:

$$X_t - \mu = e_t + \psi_1 e_{t-1} + \psi_2 e_{t-2} + \cdots = \sum_{j=0} ^\infty \psi_j e_{t-j} \quad \quad \quad \quad \quad \psi_0 = 1.$$

A few chapters later it has the MA(q) model written as: $$X_t = e_t - \theta_1 e_{t-1} - \theta_2 e_{t-2} - \cdots - \theta_q e_{t-q}.$$

Where did these $$e_t$$s come from? I searched online for the answer and all I get is that $$e_t$$s are error terms that are also White Noise processes. So my first question when I read this is: Error terms of what? Is there some estimated model that we extract these from? What do these truly represent and how do I calculate them?