Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Library: times
See also: ksmoother kem

Quantlet: kfilter
Description: Calculates a filtered time serie (uni- or multivariate) using the Kalman filter equations. The state-space model is assumed to be in the following form:

y_t = H x_t + v_t

x_t = F x_t-1 + w_t

x_0 ~ (mu,Sig), v_t ~ (0,Q), w_t ~ (0,R)

All parameters are assumed known.


Usage: fy = kfilter(y,mu,Sig,H,F,Q,R)
Input:
y T x m matrix of observed time series, T is the number of observations, m is the dimension of time series
mu n x 1 vector, the mean of the initial state
Sig n x n covariance matrix of the initial state
H m x n matrix
F n x n matrix
Q m x m variance-covariance matrix
R n x n variance-covariance matrix
Output:
fy T x p matrix of filtered time series

Example:

library("xplore")

library("times")

library("plot")

serie = read("kalman1.dat")

y = serie[,2]

mu = 10

Sig = 0

H = 1

F = 1

Q = 9

R = 9

fy = kfilter(y,mu,Sig,H,F,Q,R)

fserie = serie[,1]~serie[,2]~fy                        

data = fserie[,1]~fserie[,2]

data = setmask(data, "line", "red", "thin")

fdata = fserie[,1]~fserie[,3]

fdata = setmask(fdata, "line", "blue", "thin")

disp = createdisplay(1,1)

show(disp,1,1, data, fdata)

setgopt(disp,1,1, "title", "Kalman filter 1")

Result:

Original serie is displayed with red colour,

filtered serie is displayed with blue colour.

(y is a lagged random walk with errors.)

Example:

library("xplore")

library("times")

library("plot")

serie = read("kalman3.dat")

y  = serie[,2:3] 

mu = #(20,0)


        Sig = #(0,0)~#(0,0)

H  = #(0.3,-0.3)~#(1,1)

F  = #(1,0)~#(1,0)

Q  = #(9,0)~#(0,9)

R  = #(0,0)~#(0,9)

fy = kfilter(y,mu,Sig,H,F,Q,R)

fserie = serie[,1]~serie[,2]~serie[,3]~fy[,1]~fy[,2]

data1 = fserie[,1]~fserie[,2]

data1 = setmask(data1, "line", "red", "thin")

fdata1 = fserie[,1]~fserie[,4]

fdata1 = setmask(fdata1, "line", "blue", "thin")

data2 = fserie[,1]~fserie[,3]

data2 = setmask(data2, "line", "red", "thin")

fdata2 = fserie[,1]~fserie[,5]

fdata2 = setmask(fdata2, "line", "blue", "thin")

disp = createdisplay(2,1)

show(disp,1,1, data1, fdata1)

setgopt(disp, 1, 1, "title", "Kalman filter 2 - 1st element")

show(disp,2,1, data2, fdata2)

setgopt(disp,2,1, "title", "Kalman filter 2 - 2nd element")

Result:

Original serie is displayed with red colour,

filtered serie is displayed with blue colour.

(y is a lagged bivariate MA process with errors.)

Example:

library("xplore")

library("times")

library("plot")

serie = read("kalman2.dat")

y = serie[,2]

mu = #(0,0)


Sig = #(0,0)~#(0,0)

H = #(1,0)'

F = #(0.5,1)~#(-0.3,0)

R = #(1,0)~#(0,0)

Q = 4

fy = kfilter(y,mu,Sig, H,F,Q,R)

fserie = serie[,1]~serie[,2]~fy

data1 = fserie[,1]~fserie[,2]

data1 = setmask(data1, "line", "red", "thin")

fdata1 = fserie[,1]~fserie[,3]

fdata1 = setmask(fdata1, "line", "blue", "thin")

disp = createdisplay(1,1)

show(disp,1,1, data1, fdata1)

setgopt(disp,1,1, "title", "Kalman filter 3")

Result:

Original serie is displayed with red colour,

filtered serie is displayed with blue colour.

(y is an AR(2) process with errors.)


Library: times
See also: ksmoother kem

Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Author: P.Franek 990507
(C) MD*TECH Method and Data Technologies, 21.9.2000