Simulate from DLM of West and Harrison (1999), as in Section 2 of Catania and Nonejad (2016).
SimulateDLM(iT, mX, vBeta0, mW, dV, dPhi)numeric, number of observation to simulate.
matrix of dimension iT x N m where N is the number of covariates.
numeric vector with initial value for the regressor coefficients.
matrix covariance matrix of the state equation.
numeric variance of the observation (measurement equation).
numeric value for the autoregressive parameter of the regressors. It imposes that all the regressors have the same autoregressive parameters, if dPhi = 1, then the regressors evolve as random-walks.
An object of the class list.
The function returns a list of two elements: vY and mBeta. vY is a iT x 1 numeric vector of simulated dependent variables. mBeta is a matrix of dimension iT x ncol(mX) of regressor coefficients.
Catania, Leopoldo, and Nima Nonejad (2018). "Dynamic Model Averaging for Practitioners in Economics and Finance: The eDMA Package." Journal of Statistical Software, 84(11), 1-39. 10.18637/jss.v084.i11.
West, Mike. Bayesian forecasting. John Wiley & Sons, Inc., 1999.
# NOT RUN {
set.seed(7892)
iT <- 500
iK <- 3
dV <- 0.1
mW <- diag(iK + 1) * 0.01
dPhi <- 1
vBeta0 <- rep(0, iK + 1)
mX <- cbind(1, matrix(rnorm(iT * (iK)), iT, iK))
lOut <- SimulateDLM(iT, mX, vBeta0, mW, dV, dPhi)
vY <- lOut$vY
# }
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