Calculation of predictive likelihoods using Importance Sampling,
given subsample and full data sample and Mixture of Student-\(t\)
candidate density. Predictive likelihood is calculated using the
marginal likelihood from full sample and subsample.
See MargLik.
PredLik(N=1e4,mit.fs,mit.ss,KERNEL,data.fs,data.ss,...)integer \(> 100\) number of draws for Importance Sampling
Mixture of Student-\(t\) density for the full sample, list describing the mixture of Student-t. See isMit. The mixture density can be obtained from MitISEM or SeqMitISEM
Mixture of Student-\(t\) density for subsample. Must be defined as mit.fs.
Function/posterior to be approximated.
data and log arguments must exist. The function must return log-density if log=TRUE. All data should be loaded in argument data
Full data, vector (length \(T1\)) or matrix (size \(T1\times m\)) with data values, \(T1\) observations and \(m\) data series.
Sample of data, vector (length \(T2\)) or matrix (size \(T2\times m\)) with data values, \(T2\) observations and \(m\) data series. \(T2 <T1\).
other arguments to be passed to KERNEL
list containing:
Predictive likelihood \(\times 10^{scale}\)
integer \(> 0\) providing the scaling for predictive likelihood. (scaling may be necessary for numerical accuracy)
Argument KERNEL
Eklund, J. and Karlsson, S. (2007). Forecast combination and model averaging using predictive measures. Econometric Reviews, 26, 329-363.
Min, C. and Zellner, A. (1993). Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates. Journal of Econometrics, 56, 89-118.