particles issueshttps://git.dynare.org/Dynare/particles/-/issues2018-10-25T15:22:46Zhttps://git.dynare.org/Dynare/particles/-/issues/5Add an interface/flag for declaring that the measurement equation is linear...2018-10-25T15:22:46ZStéphane Adjemianstepan@adjemian.euAdd an interface/flag for declaring that the measurement equation is linear...... with respect to the measurement errors. The code for computing the covariance of the predicted errors is conditional on this property (See also issue #4). If the measurement equation is not linear w.r.t. the measurement errors, we must explicitly add noise in the measurement equations (as we do with the structural shocks in the state equations).
... with respect to the measurement errors. The code for computing the covariance of the predicted errors is conditional on this property (See also issue #4). If the measurement equation is not linear w.r.t. the measurement errors, we must explicitly add noise in the measurement equations (as we do with the structural shocks in the state equations).
0.2https://git.dynare.org/Dynare/particles/-/issues/4Handle the case where the measurement errors are non additive2018-10-25T15:22:46ZStéphane Adjemianstepan@adjemian.euHandle the case where the measurement errors are non additiveIn the current state, the filter codes are written under the assumption that the measurement errors enter linearly in the measurement equation. For instance in `sequential_importance_particle_filter` we compute the variance of the prediction errors, `PredictedObservedVariance`, by adding the covariance matrix of the measurement errors, `H`, to the covariance of the predicted observed variables.
In the current state, the filter codes are written under the assumption that the measurement errors enter linearly in the measurement equation. For instance in `sequential_importance_particle_filter` we compute the variance of the prediction errors, `PredictedObservedVariance`, by adding the covariance matrix of the measurement errors, `H`, to the covariance of the predicted observed variables.
0.2