• EstimationModule

Proposed specification for estimation functions

This includes factorization of common code and extensions for (unexpected) structural change

Estimated parameters description

xparam1 becomes a structure with the following fields

• value
• type (1,2,3,4,5)
• ID1
• ID2 (for correlations)
• Period1 (beginning of sub-period)
• Period2 (end of sub-period)

Functions

• Likelihood/posterior evaluation (separate two functions ?)
  • - INPUTS: xparam1, data, prior_specifications, ys, coeff_trends, M_.params, M_.Sigma_e, M_.H, list_of_observed_variables - OUTPUTS: likelihood/posterior value, info
  • parameter updates (for period 1):
    • - INPUTS: xparam1, ys, coeff_trends,M_.params, M_.Sigma_e, M_.H - OUTUTS: coeff_trends, params, Sigma_e, H
  • reduced form (for period 1):
    • - INPUTS: params - OUTPUTS: T R ys - computes steady state - computes linear solution for state variables + observed variables
  • data_filtering (for period 1):
    • - INPUTS: data, ys, coeff_trends, Period1, Period2 - OUTPUTS: centered_data for subperiod
  • Kalman filter initialization
    • - INPUTS: Sigma_e T R list_of_observed_variables - OUTPUTS: a0 P0 Z - Remark: Z is computed only for nonstationary models. mfs is computed once before these functions
      • Diffuse Kalman filter recursions takes place here ?
    LOOP on periods 1 to NP:
    • Kalman filter - INPUTS: a0 Po Z T R centered_data Sigma_e H - OUTPUTS: log-likelihood, a(t|t-1), P(t|t-1)

      if period < NP:

      • parameter updates
      • reduced form
      • data_filtering
    END LOOP
  • priors evaluation