ConsAggShockModel.py 文件源码

def calcAFunc(self,MaggNow,AaggNow):
        '''
        Calculate a new aggregate savings rule based on the history
        of the aggregate savings and aggregate market resources from a simulation.

        Parameters
        ----------
        MaggNow : [float]
            List of the history of the simulated  aggregate market resources for an economy.
        AaggNow : [float]
            List of the history of the simulated  aggregate savings for an economy.

        Returns
        -------
        (unnamed) : CapDynamicRule
            Object containing a new savings rule
        '''
        verbose = True
        discard_periods = 200 # Throw out the first T periods to allow the simulation to approach the SS
        update_weight = 0.5   # Proportional weight to put on new function vs old function parameters
        total_periods = len(MaggNow)

        # Regress the log savings against log market resources
        logAagg   = np.log(AaggNow[discard_periods:total_periods])
        logMagg = np.log(MaggNow[discard_periods-1:total_periods-1])
        slope, intercept, r_value, p_value, std_err = stats.linregress(logMagg,logAagg)

        # Make a new aggregate savings rule by combining the new regression parameters
        # with the previous guess
        intercept = update_weight*intercept + (1.0-update_weight)*self.intercept_prev
        slope = update_weight*slope + (1.0-update_weight)*self.slope_prev
        AFunc = AggregateSavingRule(intercept,slope) # Make a new next-period capital function

        # Save the new values as "previous" values for the next iteration    
        self.intercept_prev = intercept
        self.slope_prev = slope

        # Plot aggregate resources vs aggregate savings for this run and print the new parameters
        if verbose:
            print('intercept=' + str(intercept) + ', slope=' + str(slope) + ', r-sq=' + str(r_value**2))
            #plot_start = discard_periods
            #plt.plot(logMagg[plot_start:],logAagg[plot_start:],'.k')
            #plt.show()

        return AggShocksDynamicRule(AFunc)