Python load_simulation Examples

Example #1

#### Used in aer2 figures

import numpy as np

from simulations_aer2 import load_simulation
from figures import saved_figure, despine, figure_defaults

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':]

Example #2

File: fig_shocks_combo.py Project: jngod2011/empirical-implications-of-zlb

#### Used in aer2 figures

from simulations_aer2 import load_simulation, date_index, sim_dir
from figures import saved_figure, despine, figure_defaults

import numpy as np
import pandas as p

from pandas import ExcelWriter

from matplotlib.ticker import MultipleLocator, FixedLocator

baseline = load_simulation()

mean = baseline.groupby(level=0).mean()['2008':]
q16 = baseline.groupby(level=0).quantile(0.16)['2008':]
q84 = baseline.groupby(level=0).quantile(0.84)['2008':]

#shocks = ['scaled_liq', 'scaled_inv', 'techdev']

#names = {'scaled_liq': 'Risk Premium Shock',
#         'scaled_inv': 'MEI Shock',
#         'techdev': 'Technology Shock'}

ebp = p.read_csv('data/GLZ_variables.csv', index_col=0,
                 parse_dates=True).ebp_oa
ebpq = ebp.resample('Q', how=lambda x: x[-1])
ebpq = ebpq / ebpq.std()

baa = p.read_csv('data/Baaspread10y.csv', index_col=0, parse_dates=True).baa
baaq = baa.resample('Q', how=lambda x: x[-1])

Example #3

File: fig_output_inf_tech.py Project: jngod2011/empirical-implications-of-zlb

from model import data
from simulations_aer2 import load_simulation
from simulations import load_simulation_old
from figures import saved_figure, despine, figure_defaults

import pandas as p

from pandas import ExcelWriter

from matplotlib.ticker import MultipleLocator, FixedLocator
import matplotlib


sim_dir_low_me = 'results/low-me/smoothed-states/' 

smoothed_states = load_simulation()
smoothed_states_low_me = load_simulation(sim_dir=sim_dir_low_me)

smoothed_states['techdev'] = smoothed_states['Technology Level'] - smoothed_states['Technology Det']
smoothed_states_low_me['techdev'] = smoothed_states_low_me['Technology Level'] - smoothed_states_low_me['Technology Det']

mean = smoothed_states.groupby(level=0).mean()
q16 = smoothed_states.groupby(level=0).quantile(0.16)
q84 = smoothed_states.groupby(level=0).quantile(0.84)

mean_low_me = smoothed_states_low_me.groupby(level=0).mean()
q16_low_me = smoothed_states_low_me.groupby(level=0).quantile(0.16)
q84_low_me = smoothed_states_low_me.groupby(level=0).quantile(0.84)

Example #4

File: fig_observable_fit.py Project: jngod2011/empirical-implications-of-zlb

from model import data
from simulations_aer2 import load_simulation
from figures import saved_figure, despine, figure_defaults

import pandas as p
import numpy as np
import matplotlib

from matplotlib.ticker import MultipleLocator, FixedLocator

from pandas import ExcelWriter

smoothed_states = load_simulation()

mean = smoothed_states.groupby(level=0).mean()
q16 = smoothed_states.groupby(level=0).quantile(0.16)
q84 = smoothed_states.groupby(level=0).quantile(0.84)

to_plot = [
    'Output Growth', 'Investment Growth', 'Consumption Growth', 'Inflation',
    'Interest Rate', 'Notional Rate'
]

col = figure_defaults()

with saved_figure('observable_fit.pdf', nrows=3, ncols=2) as (fig, ax):

    xlabels = p.PeriodIndex(range(1985, 2016, 5), freq='A')

    for axi, obs in zip(ax.reshape(-1), to_plot):

Example #5

### Using in aer2 revision

import numpy as np

from simulations_aer2 import load_simulation
from figures import saved_figure, despine, figure_defaults

import pandas as p

from pandas import ExcelWriter

from matplotlib.ticker import MultipleLocator, FixedLocator
import matplotlib

baseline = load_simulation().groupby(level=0).mean()['2008':]
only_liq = load_simulation(sim_dir='results/alt-sims/great_recession_liq').groupby(level=0).mean()['2008':]
only_inv = load_simulation(sim_dir='results/alt-sims/great_recession_inv').groupby(level=0).mean()['2008':]
only_tech = load_simulation(sim_dir='results/alt-sims/great_recession_tech').groupby(level=0).mean()['2008':]


to_plot = ['Output Growth', 
           'Investment Growth', 
           'Consumption Growth', 
           'Inflation', 
           'Interest Rate', 
           'Notional Rate']

col = figure_defaults()

with saved_figure('drivers_of_gr.pdf', nrows=3, ncols=2) as (fig, ax):