Description Usage Arguments Details Value References See Also
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
.
1 |
N |
integer > 100 number of draws for Importance Sampling |
mit.fs |
Mixture of Student-t density for the full sample, list describing the mixture of Student-t. See |
mit.ss |
Mixture of Student-t 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 |
Argument KERNEL
list containing:
PL |
Predictive likelihood x 10^{scale} |
scale |
integer > 0 providing the scaling for predictive likelihood. (scaling may be necessary for numerical accuracy) |
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.
isMit,MargLik,MitISEM,SeqMitISEM
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