SMFilter (version 1.0.3)

SimModel2: Simulate from the type two state-space Model on Stiefel manifold.

Description

This function simulates from the type two model on Stiefel manifold. See Details part below.

Usage

SimModel2(iT, mX = NULL, mZ = NULL, mY = NULL, beta_0, alpha,
  mB = NULL, Omega = NULL, vD, burnin = 100)

Arguments

iT

the sample size.

mX

the matrix containing X_t with dimension \(T \times q_1\).

mZ

the matrix containing Z_t with dimension \(T \times q_2\).

mY

initial values of the dependent variable for ik-1 up to 0. If mY = NULL, then no lagged dependent variables in regressors.

beta_0

the initial beta, iqx+ip*ik, y_1,t-1,y_1,t-2,...,y_2,t-1,y_2,t-2,....

alpha

the \(\alpha\) matrix, \(p \times r\).

mB

the coefficient matrix \(\boldsymbol{B}\) before mZ with dimension \(p \times q_2\).

Omega

covariance matrix of the errors.

vD

vector of the diagonals of \(D\).

burnin

burn-in sample size (matrix Langevin).

Value

A list containing the sampled data and the dynamics of beta.

The object is a list containing the following components:

dData

a data.frame of the sampled data

aBeta

an array of the \(\boldsymbol{\beta}_t\) with the dimension \(T \times q_1 \times r\)

Details

The type two model on Stiefel manifold takes the form: $$\boldsymbol{y}_t \quad = \quad \boldsymbol{\alpha} \boldsymbol{\beta}_t ' \boldsymbol{x}_t + \boldsymbol{B}' \boldsymbol{z}_t + \boldsymbol{\varepsilon}_t$$ $$\boldsymbol{\beta}_{t+1} | \boldsymbol{\beta}_{t} \quad \sim \quad ML (q_1, r, \boldsymbol{\beta}_{t} \boldsymbol{D})$$ where \(\boldsymbol{y}_t\) is a \(p\)-vector of the dependent variable, \(\boldsymbol{x}_t\) and \(\boldsymbol{z}_t\) are explanatory variables wit dimension \(q_1\) and \(q_2\), \(\boldsymbol{x}_t\) and \(\boldsymbol{z}_t\) have no overlap, matrix \(\boldsymbol{B}\) is the coefficients for \(\boldsymbol{z}_t\), \(\boldsymbol{\varepsilon}_t\) is the error vector.

The matrices \(\boldsymbol{\alpha}\) and \(\boldsymbol{\beta}_t\) have dimensions \(p \times r\) and \(q_1 \times r\), respectively. Note that \(r\) is strictly smaller than both \(p\) and \(q_1\). \(\boldsymbol{\alpha}\) and \(\boldsymbol{\beta}_t\) are both non-singular matrices. \(\boldsymbol{\beta}_t\) is time-varying while \(\boldsymbol{\alpha}\) is time-invariant.

Furthermore, \(\boldsymbol{\beta}_t\) fulfills the condition \(\boldsymbol{\beta}_t' \boldsymbol{\beta}_t = \boldsymbol{I}_r\), and therefor it evolves on the Stiefel manifold.

\(ML (p, r, \boldsymbol{\beta}_t \boldsymbol{D})\) denotes the Matrix Langevin distribution or matrix von Mises-Fisher distribution on the Stiefel manifold. Its density function takes the form $$f(\boldsymbol{\beta_{t+1}} ) = \frac{ \mathrm{etr} \left\{ \boldsymbol{D} \boldsymbol{\beta}_{t}' \boldsymbol{\beta_{t+1}} \right\} }{ _{0}F_1 (\frac{p}{2}; \frac{1}{4}\boldsymbol{D}^2 ) }$$ where \(\mathrm{etr}\) denotes \(\mathrm{exp}(\mathrm{tr}())\), and \(_{0}F_1 (\frac{p}{2}; \frac{1}{4}\boldsymbol{D}^2 )\) is the (0,1)-type hypergeometric function for matrix.

Note that the function does not add intercept automatically.

Examples

# NOT RUN {
iT = 50
ip = 2
ir = 1
iqx =3
iqz=2
ik = 1

if(iqx==0) mX=NULL else mX = matrix(rnorm(iT*iqx),iT, iqx)
if(iqz==0) mZ=NULL else mZ = matrix(rnorm(iT*iqz),iT, iqz)
if(ik==0) mY=NULL else mY = matrix(0, ik, ip)

alpha = matrix(c(runif_sm(num=1,ip=ip,ir=ir)), ip, ir)
beta_0 = matrix(c(runif_sm(num=1,ip=ip*ik+iqx,ir=ir)), ip*ik+iqx, ir)
if(ip*ik+iqz==0) mB=NULL else mB = matrix(c(runif_sm(num=1,ip=(ip*ik+iqz)*ip,ir=1)), ip, ip*ik+iqz)
vD = 50

ret = SimModel2(iT=iT, mX=mX, mZ=mZ, mY=mY, alpha=alpha, beta_0=beta_0, mB=mB, vD=vD)

# }