Predictive Likelihood calculation using Importance Sampling and mixture of Studentt densities as candidate
Description
Calculation of predictive likelihoods using Importance Sampling,
given subsample and full data sample and Mixture of Studentt
candidate density. Predictive likelihood is calculated using the
marginal likelihood from full sample and subsample.
See MargLik
.
Usage
1 
Arguments
N 
integer > 100 number of draws for Importance Sampling 
mit.fs 
Mixture of Studentt density for the full sample, list describing the mixture of Studentt. See 
mit.ss 
Mixture of Studentt density for subsample. Must be defined as 
KERNEL 
Function/posterior to be approximated.

data.fs 
Full data, vector (length T1) or matrix (size T1xm) with data values, T1 observations and m data series. 
data.ss 
Sample of data, vector (length T2) or matrix (size T2xm) with data values, T2 observations and m data series. T2 <T1. 
... 
other arguments to be passed to 
Details
Argument KERNEL
Value
list containing:
PL 
Predictive likelihood x 10^{scale} 
scale 
integer > 0 providing the scaling for predictive likelihood. (scaling may be necessary for numerical accuracy) 
References
Eklund, J. and Karlsson, S. (2007). Forecast combination and model averaging using predictive measures. Econometric Reviews, 26, 329363.
Min, C. and Zellner, A. (1993). Bayesian and nonBayesian methods for combining models and forecasts with applications to forecasting international growth rates. Journal of Econometrics, 56, 89118.
See Also
isMit,MargLik,MitISEM,SeqMitISEM