import pandas as p import matplotlib from pandas import ExcelWriter from matplotlib.ticker import MultipleLocator, FixedLocator baseline = load_simulation() nozlb = load_simulation(sim_dir='results/alt-sims/nozlb_2008') gdp_diff = 100*(np.log(baseline.gdp) - np.log(nozlb.gdp)) gdp_mean = gdp_diff.groupby(level=0).mean()['2007Q4':] gdp_q16 = gdp_diff.groupby(level=0).apply(lambda x: np.percentile(x, 16))['2007Q4':] gdp_q84 = gdp_diff.groupby(level=0).apply(lambda x: np.percentile(x, 84))['2007Q4':] col = figure_defaults() #nozlbq16 = nozlb.groupby(level=0).quantile(0.16)['Interest Rate']['2007Q4':] #nozlbq84 = nozlb.groupby(level=0).quantile(0.84)['Interest Rate']['2007Q4':] not_mean = baseline.groupby(level=0).mean()['Notional Rate']['2007Q4':] not_q16 = baseline.groupby(level=0)['Notional Rate'].apply(lambda x: np.percentile(x, 16))['2007Q4':] not_q84 = baseline.groupby(level=0)['Notional Rate'].apply(lambda x: np.percentile(x, 84))['2007Q4':] inv_diff = 100*(np.log(baseline.inv) - np.log(nozlb.inv)) inv_mean = inv_diff.groupby(level=0).mean()['2007Q4':] inv_q16 = inv_diff.groupby(level=0).apply(lambda x: np.percentile(x, 16))['2007Q4':] inv_q84 = inv_diff.groupby(level=0).apply(lambda x: np.percentile(x, 84))['2007Q4':] cons_diff = 100*(np.log(baseline.cc) - np.log(nozlb.cc)) cons_mean = cons_diff.groupby(level=0).mean()['2007Q4':]
premium = 400 * np.log(premium)
premium_mean = premium.groupby(level=0).mean()
premium_q16 = premium.groupby(level=0).quantile(0.16)
premium_q84 = premium.groupby(level=0).quantile(0.84)
ep_mean = p.Series(premium_mean['endogvar_29'])
ep_mean.index = date_index
ep_q16 = p.Series(premium_q16['endogvar_29'])
ep_q16.index = date_index
ep_q84 = p.Series(premium_q84['endogvar_29'])
ep_q84.index = date_index
figure_defaults()
with saved_figure('shocks_combo.pdf', nrows=2, ncols=2) as (fig, ax):
# Risk Premium Shock (top left)
mean['scaled_liq'].plot(ax=ax[0, 0], linewidth=2)
ax[0, 0].fill_between(q16['scaled_liq'].index,
q16['scaled_liq'],
q84['scaled_liq'],
alpha=0.3)
ebpq['2008':].plot(ax=ax[0, 0], linestyle='dashed', color='green')
baaq['2008':].plot(ax=ax[0, 0], linestyle='dotted', color='red')
ax[0, 0].set_title(r'Risk Premium Shock', fontsize=16)
ax[0, 0].set_xlim(['2008Q1', '2014Q1'])
ax[0, 0].get_xaxis().set_minor_locator(MultipleLocator(4))
ax[0, 0].tick_params('x',
gamQ = sim['inputs']['para'][9]
var_names = ['c', 'ppi', 'R', 'z', 'y', 'g', 'ylag', 'Eppi', 'Ec', 'Ey', 'Ez']
forecast = p.DataFrame(sim['output']['001']['fcst_states']['024'])
forecast.columns = var_names + ['weights']
forecast['ygr'] = forecast.y - forecast.ylag + forecast.z + gamQ
update = p.DataFrame(sim['output']['001']['update_states']['024'])
update.columns = var_names + ['weights']
update['ygr'] = update.y - update.ylag + update.z + gamQ
from scipy.stats import norm
true_filtered_density = norm(loc=-2.977 + gamQ, scale=np.sqrt(0.013452427))
xgrid = np.linspace(-6, 6, 1000)
cp = figure_defaults(grayscale=False)
with saved_figure(args.output) as (fig, ax):
forecast.ygr.plot(kind='kde',
ax=ax,
linewidth=5,
alpha=0.7,
linestyle='dashed')
despine()
ax.axvline(x=update.ygr.mean(), linewidth=7, color=cp[1], alpha=0.7)
ax.plot(xgrid,
true_filtered_density.pdf(xgrid),
linewidth=5,
color=cp[2],
alpha=0.7)
ax.set_xlim(-4, 2)
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()