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Bayesian Model Averaging and Jointness Measures for gretl

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Figure

Figure 1: Structure of the matrix returned by the BMA package.
Table 1: Main features of the selected Bayesian model averaging software. Model Sampling– type of model sampling algorithms, Model Priors – number of available model priors, Zell-ner’s g-Priors – number of available Zellner’s factors of proportionality, Jointness Analysis –information about which package provides jointness analysis, Jointness Measures – number ofavailable jointness measures, Out-of-Sample Forecast – information about which package canbe used for out-of-sample forecasting, Number of Burn-in Draws, Number of Draws and Num-ber of Top Ranked Models – number ofuser-defined parameters in the sampling algorithm,Graphical User Interface – presence of GUI in the package.
Table 3: The posterior estimates of regression coefficients based on Bayesian model averaging
Table 4: Posterior inclusion probabilities based on Bayesian model averaging software for FLSdata

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