AR Model

Description#

An autoregressive model specifies that the dependent variable depends linearly on its own previous values and on a stochastic term.

$$ X_t = c + \sum_{i=1}^p \varphi_i X_{t-i}+ \varepsilon_t $$

This relation can also be represented with a lag operator $ L $:

$$ X_t = c + \sum_{i=1}^p \varphi_i L^i X_t + \varepsilon_t $$

Returns#

• $ L_{i} $: Lag coefficients
• MSE: Mean squared error
• Yh: Predicted values