IRIS Toolbox Reference Manual

filter

Kalman smoother and estimator of out-of-likelihood parameters

Syntax

[M,Outp,V,Delta,PE,SCov] = filter(M,Inp,Range,...)

Input arguments

Output arguments

Options

Options for models with non-linearised equations

Description

The 'ahead=' and 'rollback=' options cannot be combined with one another, or with multiple data sets, or with multiple parameterisations.

Initial conditions in time domain

By default (with 'initCond=' 'stochastic'), the Kalman filter starts from the model-implied asymptotic distribution. You can change this behaviour by setting the option 'initCond=' to one of the following four different values:

Contributions of measurement variables to the estimates of all variables

Use the option 'returnCont=' true to request the decomposition of measurement variables, transition variables, and shocks into the contributions of each individual measurement variable. The resulting output database will include one extra subdatabase called .cont. In the .cont subdatabase, each time series will have Ny columns where Ny is the number of measurement variables in the model. The k-th column will be the contribution of the observations on the k-th measurement variable.

The contributions are additive for linearised variables, and multiplicative for log-linearised variables (log variables). The difference between the actual path for a particular variable and the sum of the contributions (or their product in the case of log varibles) is due to the effect of constant terms and deterministic trends.

Example