with saved_figure('effect_of_zlb.pdf', nrows=2, ncols=2) as (fig, ax):
not_mean.plot(ax=ax[0, 0], linewidth=2)
ax[0, 0].fill_between(not_q16.index, not_q16, not_q84, alpha=0.3)
not_mean_low_me.plot(ax=ax[0, 0], linewidth=2, linestyle='dashed', color='red')
ax[0, 0].set_title(r'Notional Interest Rate',fontsize=16)
ax[0, 0].set_xlim(['2007Q4','2014Q1'])
ax[0, 0].get_xaxis().set_minor_locator(MultipleLocator(4))
ax[0, 0].tick_params('x',length=5,width=1,which='minor',direction='in',labelsize=14,pad=5)
ax[0, 0].set_xticks(['2008Q3','2009Q3','2010Q3','2011Q3','2012Q3','2013Q3'])
ax[0, 0].tick_params('x', length=0,width=0,which='major',direction='in',labelsize=12,pad=5)
ax[0, 0].set_xticklabels(['2008','2009','2010','2011','2012','2013'])
ax[0, 0].tick_params('y',labelsize=14)
ax[0, 0].grid(False)
despine()
gdp_mean.plot(ax=ax[0, 1], linewidth=2)
ax[0, 1].fill_between(gdp_q16.index, gdp_q16, gdp_q84, alpha=0.3)
gdp_mean_low_me.plot(ax=ax[0, 1], linewidth=2, linestyle='dashed', color='red')
ax[0, 1].set_title(r'Output',fontsize=16)
ax[0, 1].set_xlim(['2007Q4','2014Q1'])
ax[0, 1].get_xaxis().set_minor_locator(MultipleLocator(4))
ax[0, 1].tick_params('x',length=5,width=1,which='minor',direction='in',labelsize=14,pad=5)
ax[0, 1].set_xticks(['2008Q3','2009Q3','2010Q3','2011Q3','2012Q3','2013Q3'])
ax[0, 1].tick_params('x', length=0,width=0,which='major',direction='in',labelsize=12,pad=5)
ax[0, 1].set_xticklabels(['2008','2009','2010','2011','2012','2013'])
ax[0, 1].tick_params('y',labelsize=14)
ax[0, 1].set_yticklabels(np.arange(-4,2,1))
ax[0 ,1].set_ylim([-4,1])
ax[0, 1].grid(False)
index = p.period_range(freq='Q', start='1983Q1', periods=80)
else:
index = p.period_range(start='2003Q1', freq='Q', periods=44)
reps = filter(lambda x: x.isdigit(), results['output'].keys())
T = len(index)
sim_results = p.DataFrame()
for r in reps:
time_series = [results['output'][r]['mean_filtered_states']['{:03d}'.format(n+1)]
for n in range(T)]
res_r = p.DataFrame(np.array(time_series), index=index, columns=var_names)
res_r['sim'] = int(r)
sim_results = sim_results.append(res_r)
stds.append(sim_results.groupby(sim_results.index).std())
means.append(sim_results.groupby(sim_results.index).mean())
cp = figure_defaults(n=5)
with saved_figure(args.output) as (fig,ax):
for i, std in enumerate(stds):
std.g.plot(ax=ax, linewidth=5, color=cp[i])
ax.legend(['BSPF, M=40k', 'TPF(r*=2), M=40k', 'TPF(r*=3), M=40k',
'TPF(r*=2), M=4k', 'TPF(r*=3), M=4k'], loc='lower right', ncol=2)
despine()