2183
Comment:
|
2651
|
Deletions are marked like this. | Additions are marked like this. |
Line 13: | Line 13: |
=== Data === * stl::vector with prior density parameters for each estimated parameter (see EstimationModule) |
|
Line 17: | Line 20: |
=== Data === * stl::vector of pairs with initial period and number of periods for each subsample |
|
Line 26: | Line 31: |
=== Data === * observed data * detrended data * state space representation matrices |
|
Line 29: | Line 38: |
=== Data === * stl::vector of a structure with type, position, and relevant subsample for each estimated parameter |
|
Line 32: | Line 43: |
=== Data === * formulas for linear trends |
This page describes the operations needed to evaluate the posterior density and the likelihood function of a model
LogPosteriorDensity
evaluate log-likelihood (LogLikelihoodMain)
- evaluate log prior density
- returns the sum of log likelihood and log prior density
LogPriorDensity
- evaluate log prior density for each estimated parameter
- returns the sum of the log prior densities
Data
stl::vector with prior density parameters for each estimated parameter (see EstimationModule)
LogLikelihoodMain
- evaluate log-likelihood for each subsample
- returns the sum of the above
Data
- stl::vector of pairs with initial period and number of periods for each subsample
LogLikelihood
- update parameters (including covariance matrices)
- detrend data
- compute model solution
- if first subsample
- initialize Kalman filter
- run Kalman filter
- returns log likelihood
Data
- observed data
- detrended data
- state space representation matrices
UpdateParameters
- for each estimated parameter, reset its value in model's data members, if it belongs to the estimated subsample
Data
- stl::vector of a structure with type, position, and relevant subsample for each estimated parameter
DetrendData
- remove possible constants and possible linear trends from observed data
Data
- formulas for linear trends
ComputeModelSolution
- compute the steady state
- computes first order approximation
InitializeKalmanFilter
set
- if model is declared stationary
compute covariance matrix of endogenous variables () by doubling algorithm
- else (not prioritary)
- compute Schur transformation of state space model
- recover order of integration of the model (still need to determine exact algorithm)
- recover list of stationary/non-stationary factors
compute covariance matrix of stationary endogenous variables () by doubling algorithm
set
- compute diffuse Kalman filter for as many periods as order of integration
KalmanFilter
- vanilla Kalman filter without constant and with measurement error (use scalar 0 when no measurement error)
- we still need to compare multivariate and univariate filter
- if multivariate filter is faster, do as in Matlab: start with multivariate filter and switch to univariate filter only in case of singularity