SimulateDLM: Simulate from DLM of West and Harrison (1999).
In eDMA: Dynamic Model Averaging with Grid Search

Description Usage Arguments Details Value Author(s) References Examples

Simulate from DLM of West and Harrison (1999), as in Section 2 of Catania and Nonejad (2016).

1	SimulateDLM(iT, mX, vBeta0, mW, dV, dPhi)

`iT`	`numeric`, number of observation to simulate.
`mX`	`matrix` of dimension `iT` x N m where N is the number of covariates.
`vBeta0`	`numeric` vector with initial value for the regressor coefficients.
`mW`	`matrix` covariance matrix of the state equation.
`dV`	`numeric` variance of the observation (measurement equation).
`dPhi`	`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.

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.

An object of the class list.

Leopoldo Catania & Nima Nonejad

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. doi: 10.18637/jss.v084.i11.

West, Mike. Bayesian forecasting. John Wiley & Sons, Inc., 1999.

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