DynamicFactor.update(params, transformed=True, includes_fixed=False, complex_step=False)[source]

Update the parameters of the model

Updates the representation matrices to fill in the new parameter values.


Array of new parameters.

transformedbool, optional

Whether or not params is already transformed. If set to False, transform_params is called. Default is True..


Array of parameters.


Let n = k_endog, m = k_factors, and p = factor_order. Then the params vector has length \([n imes m] + [n] + [m^2 imes p]\). It is expanded in the following way:

  • The first \(n imes m\) parameters fill out the factor loading matrix, starting from the [0,0] entry and then proceeding along rows. These parameters are not modified in transform_params.

  • The next \(n\) parameters provide variances for the error_cov errors in the observation equation. They fill in the diagonal of the observation covariance matrix, and are constrained to be positive by transofrm_params.

  • The next \(m^2 imes p\) parameters are used to create the p coefficient matrices for the vector autoregression describing the factor transition. They are transformed in transform_params to enforce stationarity of the VAR(p). They are placed so as to make the transition matrix a companion matrix for the VAR. In particular, we assume that the first \(m^2\) parameters fill the first coefficient matrix (starting at [0,0] and filling along rows), the second \(m^2\) parameters fill the second matrix, etc.