KimFilterCpp: Implementation of Kim filter (1994), an extension to Kalman...

In SebKrantz/dynfacto_R: Dynamic Factor Model Estimation for Nowcasting

Description

Implementation of Kim filter (1994), an extension to Kalman filter for dynamic linear models with Markov-switching parameters. Currently, Markov switching is assumed to happen only in observation and/or transition matrices.

Usage

KimFilterCpp(y, R, Q, F1, A1, x0, P0, p)

Arguments

y	Data matrix (T x n)
R	Observation equation covariance
Q	State equation covariance
F1	Array of observation matrices, one matrix per state
A1	Array of transition matrices, one matrix per state
x0	Initial condition for state vector
P0	Initial condition for state covariance matrix
p	Markov transition probability matrix

Value

a list with estimates

SebKrantz/dynfacto_R documentation built on Dec. 31, 2020, 4:30 p.m.