def get_new_g(ws, params):
np.random.seed(42)
pths, shks = sample_path(ws, params, nseries=1000, nperiods=30)
pth, shocks = pths[28], shks[28] # a period in steady state
g = ecdf(np.sort(pth))
shocks = np.sort(shocks)
return g, shocks
def get_df(pi, lambda_, ws, params):
"""
Give a pi, lambda pair, get the dataframe of wage choices
sample from the steady state.
"""
pths, shocks = sample_path(ws, params, lambda_=lambda_, w0=.9, nseries=1000,
nperiods=100)
df = pd.DataFrame(pths)
return df
def get_df(pi, lambda_, ws, params):
"""
Give a pi, lambda pair, get the dataframe of wage choices
sample from the steady state.
"""
pths, shocks = sample_path(ws,
params,
lambda_=lambda_,
w0=.9,
nseries=1000,
nperiods=100)
df = pd.DataFrame(pths)
return df
def run_one(params, res_dict=None):
"""
Once you have the parameters, this function completes one loop to get
a dictionary of results.
For the first loop leave res_dict as None.
"""
pi = params['pi'][0]
# lambda_ = params['lambda_'][0]
np.random.seed(42)
w_grid = params['w_grid'][0]
z_grid = params['z_grid'][0]
if res_dict:
print("Reusing res_dict.")
v = res_dict['Tv']
out = res_dict['rigid_out']
else:
v = Interp(w_grid, -w_grid + 27.3, kind='linear')
out = ss_output_flexible(params) # ss output w/ flexible wages
# Get close with linear first. Then do a few cubic to finish up
Tv, ws, rest = iter_bellman(v,
tol=0.005,
strict=False,
log=False,
params=params,
pi=pi,
aggL=out,
kind='linear')
Tvc = Interp(Tv.X, Tv.Y, kind='cubic')
Tv, ws, rest = iter_bellman(Tvc,
tol=0.005,
strict=False,
log=False,
params=params,
pi=pi,
aggL=out)
res_dict = {'Tv': Tv, 'ws': ws, 'rest': rest}
flex_ws = Interp(z_grid, ss_wage_flexible(params, shock=z_grid))
#-------------------------------------------------------------------------
pths, shks = sample_path(ws, params, nseries=1000, nperiods=30)
pth, shocks = pths[28], shks[28] # a period in steady state
g = ecdf(np.sort(pth))
shocks = np.sort(shocks)
#-------------------------------------------------------------------------
rigid_out = get_rigid_output(ws, params, flex_ws, g)
res_dict['gp'] = g
res_dict['rigid_out'] = rigid_out
return res_dict
def make_panel(wses,
params,
pairs=None,
log=False,
nseries=100,
nperiods=50,
seed=42):
"""
Panel of wage choices where
items : (pi, lambda)
major : periods
minor : series
seed is an int given to np.random.set_seed().
"""
if pairs is None:
pairs = wses.iterkeys()
dfs = {}
sks = {}
for key in pairs:
ws = wses[key]
pths, shocks = sample_path(ws,
params,
w0=.9,
lambda_=key[1],
nseries=nseries,
nperiods=nperiods,
seed=seed)
df = pd.DataFrame(pths)
shocks = pd.DataFrame(shocks)
if log:
dfs[key] = np.log(df)
else:
dfs[key] = df
sks[key] = shocks
df_pan = pd.Panel(dfs)
sk_pan = pd.Panel(sks)
df_pan.minor_axis.name = 'person'
sk_pan.minor_axis.name = 'person'
df_pan.items.name = 'key'
sk_pan.items.name = 'key'
df_pan.major_axis.name = 'period'
sk_pan.major_axis.name = 'period'
return df_pan, sk_pan
def get_g(pi, lambda_, period=28):
"""
Helper function to get to a wage distribution.
Warning: Will not touch the params in your global state.
If you go on the to more things make sure to adjust those params.
"""
import analyze_run as ar
params = load_params()
params['pi'] = pi, 'a'
params['lambda_'] = lambda_, 'b'
all_files = ar.get_all_files(params)
wses = ar.read_output(all_files, kind='ws')
ws = wses[(pi, lambda_)]
pths, shks = sample_path(ws, params, nseries=1000, nperiods=30, seed=42)
pth, shocks = pths[28], shks[28]
shocks = np.sort(shocks)
g = ecdf(np.sort(pth))
return g, shocks
def get_g(pi, lambda_, period=28):
"""
Helper function to get to a wage distribution.
Warning: Will not touch the params in your global state.
If you go on the to more things make sure to adjust those params.
"""
import analyze_run as ar
params = load_params()
params['pi'] = pi, 'a'
params['lambda_'] = lambda_, 'b'
all_files = ar.get_all_files(params)
wses = ar.read_output(all_files, kind='ws')
ws = wses[(pi, lambda_)]
pths, shks = sample_path(ws, params, nseries=1000, nperiods=30, seed=42)
pth, shocks = pths[28], shks[28]
shocks = np.sort(shocks)
g = ecdf(np.sort(pth))
return g, shocks
def run_one(params, res_dict=None):
"""
Once you have the parameters, this function completes one loop to get
a dictionary of results.
For the first loop leave res_dict as None.
"""
pi = params['pi'][0]
# lambda_ = params['lambda_'][0]
np.random.seed(42)
w_grid = params['w_grid'][0]
z_grid = params['z_grid'][0]
if res_dict:
print("Reusing res_dict.")
v = res_dict['Tv']
out = res_dict['rigid_out']
else:
v = Interp(w_grid, -w_grid + 27.3, kind='linear')
out = ss_output_flexible(params) # ss output w/ flexible wages
# Get close with linear first. Then do a few cubic to finish up
Tv, ws, rest = iter_bellman(v, tol=0.005, strict=False, log=False,
params=params, pi=pi, aggL=out, kind='linear')
Tvc = Interp(Tv.X, Tv.Y, kind='cubic')
Tv, ws, rest = iter_bellman(Tvc, tol=0.005, strict=False, log=False,
params=params, pi=pi, aggL=out)
res_dict = {'Tv': Tv, 'ws': ws, 'rest': rest}
flex_ws = Interp(z_grid, ss_wage_flexible(params, shock=z_grid))
#-------------------------------------------------------------------------
pths, shks = sample_path(ws, params, nseries=1000, nperiods=30)
pth, shocks = pths[28], shks[28] # a period in steady state
g = ecdf(np.sort(pth))
shocks = np.sort(shocks)
#-------------------------------------------------------------------------
rigid_out = get_rigid_output(ws, params, flex_ws, g)
res_dict['gp'] = g
res_dict['rigid_out'] = rigid_out
return res_dict
