get the forecast uncertainty contribution from each parameter
        group.  Just some sugar for get_contribution_dataframe() - this method
        automatically constructs the parlist_dict argument where the keys are the
        group names and the values are the adjustable parameters in the groups

        Parameters
        ----------
        include_prior_results : bool
            flag to return a multi-indexed dataframe with both conditional
            prior and posterior forecast uncertainty estimates.  Default is False


        Returns
        -------
        pandas.DataFrame : pandas.DataFrame
            a dataframe that summarizes the parameter contribution analysis.
            The dataframe has index (row labels) that are the parameter groups
            and a column labels of forecast names.  The values in the dataframe
            are the posterior variance of the forecast conditional on perfect
            knowledge of the adjustable parameters in each parameter groups
            Varies depending on `include_prior_results`.

def get_par_group_contribution(self, include_prior_results=False):
        """get the forecast uncertainty contribution from each parameter
        group.  Just some sugar for get_contribution_dataframe() - this method
        automatically constructs the parlist_dict argument where the keys are the
        group names and the values are the adjustable parameters in the groups

        Parameters
        ----------
        include_prior_results : bool
            flag to return a multi-indexed dataframe with both conditional
            prior and posterior forecast uncertainty estimates.  Default is False


        Returns
        -------
        pandas.DataFrame : pandas.DataFrame
            a dataframe that summarizes the parameter contribution analysis.
            The dataframe has index (row labels) that are the parameter groups
            and a column labels of forecast names.  The values in the dataframe
            are the posterior variance of the forecast conditional on perfect
            knowledge of the adjustable parameters in each parameter groups
            Varies depending on `include_prior_results`.

        """
        pargrp_dict = {}
        par = self.pst.parameter_data
        groups = par.groupby("pargp").groups
        for grp,idxs in groups.items():
            #pargrp_dict[grp] = list(par.loc[idxs,"parnme"])
            pargrp_dict[grp] = [pname for pname in list(par.loc[idxs,"parnme"])
                                if pname in self.jco.col_names and pname in self.parcov.row_names]
        return self.get_par_contribution(pargrp_dict,include_prior_results=include_prior_results)