def prepare_release_tests_5(constr):
""" This function prepares the initialization files so that they can be processed by both
releases under investigation. The idea is to have all hand-crafted modifications grouped in
this function only.
"""
sys.path.insert(0, "../../../respy/tests")
from respy.tests.codes.random_model import generate_init
# Prepare fresh subdirectories
for which in ["old", "new"]:
if os.path.exists(which):
shutil.rmtree(which)
os.mkdir(which)
init_dict = generate_init(constr)
json.dump(init_dict, open("old/init_dict.respy.json", "w"))
# We added an additional coefficient indicating whether there is any experience in a
# particular job.
init_dict["OCCUPATION A"]["coeffs"].append(0.00)
init_dict["OCCUPATION A"]["bounds"].append([None, None])
init_dict["OCCUPATION A"]["fixed"].append(True)
init_dict["OCCUPATION B"]["coeffs"].append(0.00)
init_dict["OCCUPATION B"]["bounds"].append([None, None])
init_dict["OCCUPATION B"]["fixed"].append(True)
# This release rescaled the squared term in the experience variable by 100. The presence of
# the scratch file ensures that this is undone
open("new/.restud.respy.scratch", "w").close()
json.dump(init_dict, open("new/init_dict.respy.json", "w"))
def prepare_release_tests_2(constr):
""" This function prepares the initialization files so that they can be processed by both
releases under investigation. The idea is to have all hand-crafted modifications grouped in
this function only.
"""
sys.path.insert(0, "../../../respy/tests")
from respy.tests.codes.random_model import generate_init
# Prepare fresh subdirectories
for which in ["old", "new"]:
if os.path.exists(which):
shutil.rmtree(which)
os.mkdir(which)
constr["level"] = 0.00
constr["fixed_ambiguity"] = True
constr["file_est"] = "../data.respy.dat"
init_dict = generate_init(constr)
json.dump(init_dict, open("new/init_dict.respy.json", "w"))
# In the old version, we did not allow for variability in the standard deviations.
del init_dict["AMBIGUITY"]["mean"]
json.dump(init_dict, open("old/init_dict.respy.json", "w"))
def prepare_release_tests_10(constr):
""" This function prepares the initialization files so that they can be processed by both
releases under investigation. The idea is to have all hand-crafted modifications grouped in
this function only.
"""
# This script is also imported (but not used) for the creation of the virtual environments.
# Thus, the imports might not be valid when starting with a clean slate.
import numpy as np
sys.path.insert(0, "../../../respy/tests")
from respy.tests.codes.random_model import generate_init
# Prepare fresh subdirectories
for which in ["old", "new"]:
if os.path.exists(which):
shutil.rmtree(which)
os.mkdir(which)
constr["flag_estimation"] = True
init_dict = generate_init(constr)
# We aligned the indicator functions with the KW1997 setup and also added a constant term for
# the general rewards. Finally, we added the common rewards.
for label in ["OCCUPATION A", "OCCUPATION B"]:
for j in range(8, 15):
init_dict[label]["coeffs"][j] = 0
init_dict[label]["bounds"][j] = (None, None)
init_dict[label]["fixed"][j] = True
for j in [0, 1]:
init_dict["COMMON"]["coeffs"][j] = 0
init_dict["COMMON"]["bounds"][j] = (None, None)
init_dict["COMMON"]["fixed"][j] = True
new_dict = copy.deepcopy(init_dict)
# We swapped to the order to align it with the KW1997 setup.
for label in ["OCCUPATION A", "OCCUPATION B"]:
new_dict[label]["coeffs"][8], new_dict[label]["coeffs"][11] = (
new_dict[label]["coeffs"][11],
new_dict[label]["coeffs"][8],
)
new_dict[label]["coeffs"][9], new_dict[label]["coeffs"][12] = (
new_dict[label]["coeffs"][12],
new_dict[label]["coeffs"][9],
)
json.dump(new_dict, open("new/init_dict.respy.json", "w"))
old_dict = copy.deepcopy(init_dict)
for label in ["OCCUPATION A", "OCCUPATION B"]:
for name in ["coeffs", "bounds", "fixed"]:
old_dict[label][name].pop(10)
del old_dict["COMMON"]
json.dump(old_dict, open("old/init_dict.respy.json", "w"))
def prepare_release_tests_6(constr):
""" This function prepares the initialization files so that they can be processed by both
releases under investigation. The idea is to have all hand-crafted modifications grouped in
this function only.
"""
# This script is also imported (but not used) for the creation of the virtual environments.
# Thus, the imports might not be valid when starting with a clean slate.
import numpy as np
sys.path.insert(0, "../../../respy/tests")
from respy.tests.codes.random_model import generate_init
# Prepare fresh subdirectories
for which in ["old", "new"]:
if os.path.exists(which):
shutil.rmtree(which)
os.mkdir(which)
# Unfortunately, we needed to perform edits to the likelihood function which breaks
# comparability in all cases but for a model with a single period. We also changed the
# treatment of inadmissible states, so we need to ensure that these are not relevant.
constr["periods"] = 1
edu_start = np.random.randint(1, 5)
constr["edu"] = (edu_start, edu_start + 100)
init_dict = generate_init(constr)
old_dict = init_dict.copy()
old_dict["EDUCATION"]["start"] = old_dict["EDUCATION"]["start"][0]
del old_dict["TYPE_SHARES"], old_dict["TYPE_SHIFTS"], old_dict[
"EDUCATION"]["share"]
json.dump(old_dict, open("old/init_dict.respy.json", "w"))
# We need to specify a sample with a baseline type only a single initial condition.
init_dict["TYPE_SHIFTS"] = {}
init_dict["TYPE_SHIFTS"]["coeffs"] = [0.0, 0.0, 0.0, 0.0]
init_dict["TYPE_SHIFTS"]["bounds"] = [
(None, None),
(None, None),
(None, None),
(None, None),
]
init_dict["TYPE_SHIFTS"]["fixed"] = [True, True, True, True]
init_dict["TYPE_SHARES"] = {}
init_dict["TYPE_SHARES"]["coeffs"] = [1.0]
init_dict["TYPE_SHARES"]["bounds"] = [(0.0, None)]
init_dict["TYPE_SHARES"]["fixed"] = [True]
init_dict["EDUCATION"]["start"] = [init_dict["EDUCATION"]["start"]]
init_dict["EDUCATION"]["share"] = [1.0]
init_dict["EDUCATION"]["max"] = init_dict["EDUCATION"]["max"]
json.dump(init_dict, open("new/init_dict.respy.json", "w"))
def prepare_release_tests_7(constr):
""" This function prepares the initialization files so that they can be processed by both
releases under investigation. The idea is to have all hand-crafted modifications grouped in
this function only.
"""
# This script is also imported (but not used) for the creation of the virtual environments.
# Thus, the imports might not be valid when starting with a clean slate.
import numpy as np
sys.path.insert(0, "../../../respy/tests")
from respy.tests.codes.random_model import generate_init
# Prepare fresh subdirectories
for which in ["old", "new"]:
if os.path.exists(which):
shutil.rmtree(which)
os.mkdir(which)
# This dictionary is generated by the current code, so we first store the new initialization
# dictionary.
init_dict = generate_init(constr)
new_dict = init_dict.copy()
for name in ["OCCUPATION A", "OCCUPATION B", "HOME"]:
new_dict[name]["coeffs"][-2:] = [0.0, 0.0]
new_dict[name]["bounds"][-2:] = [[None, None], [None, None]]
new_dict[name]["fixed"][-2:] = [True, True]
new_dict["EDUCATION"]["coeffs"][2] = 0.0
new_dict["EDUCATION"]["bounds"][2] = [None, None]
new_dict["EDUCATION"]["fixed"][2] = True
new_dict["EDUCATION"]["coeffs"][-2:] = [0.0, 0.0]
new_dict["EDUCATION"]["bounds"][-2:] = [[None, None], [None, None]]
new_dict["EDUCATION"]["fixed"][-2:] = [True, True]
json.dump(new_dict, open("new/init_dict.respy.json", "w"))
old_dict = init_dict.copy()
for name in ["OCCUPATION A", "OCCUPATION B", "HOME"]:
for label in ["coeffs", "bounds", "fixed"]:
old_dict[name][label] = old_dict[name][label][:-2]
for label in ["coeffs", "bounds", "fixed"]:
del old_dict["EDUCATION"][label][2]
del old_dict["EDUCATION"][label][-2:]
json.dump(old_dict, open("old/init_dict.respy.json", "w"))
def no_preparations_required(constr):
""" This function prepares the initialization files so that they can be processed by both
releases under investigation. The idea is to have all hand-crafted modifications grouped in
this function only.
"""
sys.path.insert(0, "../../../respy/tests")
from respy.tests.codes.random_model import generate_init
# Prepare fresh subdirectories
for which in ["old", "new"]:
if os.path.exists(which):
shutil.rmtree(which)
os.mkdir(which)
init_dict = generate_init(constr)
json.dump(init_dict, open("new/init_dict.respy.json", "w"))
json.dump(init_dict, open("old/init_dict.respy.json", "w"))
def prepare_release_tests_4(constr):
""" This function prepares the initialization files so that they can be processed by both
releases under investigation. The idea is to have all hand-crafted modifications grouped in
this function only.
"""
sys.path.insert(0, "../../../respy/tests")
from respy.tests.codes.random_model import generate_init
# Prepare fresh subdirectories
for which in ["old", "new"]:
if os.path.exists(which):
shutil.rmtree(which)
os.mkdir(which)
init_dict = generate_init(constr)
# We need to make sure that there are no effects on the reentry costs, as there are
# separately estimation in the new release. They are fixed during an estimation there as well.
init_dict["EDUCATION"]["fixed"][-1] = True
json.dump(init_dict, open("old/init_dict.respy.json", "w"))
# We added sheepskin effects to the wage equations.
init_dict["OCCUPATION A"]["coeffs"] += [0.0, 0.0]
init_dict["OCCUPATION A"]["fixed"] += [True, True]
init_dict["OCCUPATION A"]["bounds"] += [[None, None], [None, None]]
init_dict["OCCUPATION B"]["coeffs"] += [0.0, 0.0]
init_dict["OCCUPATION B"]["fixed"] += [True, True]
init_dict["OCCUPATION B"]["bounds"] += [[None, None], [None, None]]
# We are also splitting up the re-entry costs between high school and college graduation
init_dict["EDUCATION"]["coeffs"].append(
init_dict["EDUCATION"]["coeffs"][-1])
init_dict["EDUCATION"]["fixed"].append(True)
init_dict["EDUCATION"]["bounds"].append(
init_dict["EDUCATION"]["bounds"][-1])
json.dump(init_dict, open("new/init_dict.respy.json", "w"))
def prepare_release_tests_8(constr):
""" This function prepares the initialization files so that they can be processed by both
releases under investigation. The idea is to have all hand-crafted modifications grouped in
this function only.
"""
# This script is also imported (but not used) for the creation of the virtual environments.
# Thus, the imports might not be valid when starting with a clean slate.
import numpy as np
sys.path.insert(0, "../../../respy/tests")
from respy.tests.codes.random_model import generate_init
# Prepare fresh subdirectories
for which in ["old", "new"]:
if os.path.exists(which):
shutil.rmtree(which)
os.mkdir(which)
# As we added more information about lagged activities the interpolation estimation does not
# yield the same results.
constr["flag_interpolation"] = False
init_dict = generate_init(constr)
new_dict = init_dict.copy()
for name in ["OCCUPATION A", "OCCUPATION B"]:
new_dict[name]["coeffs"][8] = 0.00
new_dict[name]["bounds"][8] = [None, None]
new_dict[name]["fixed"][8] = True
json.dump(new_dict, open("new/init_dict.respy.json", "w"))
# We need to remove the coefficient denoting whether an individual worked in the same
# occupation last period.
old_dict = init_dict.copy()
for name in ["OCCUPATION A", "OCCUPATION B"]:
for label in ["coeffs", "bounds", "fixed"]:
old_dict[name][label].pop(8)
json.dump(old_dict, open("old/init_dict.respy.json", "w"))
def prepare_release_tests_1(constr):
""" This function prepares the initialization files so that they can be processed by both
releases under investigation. The idea is to have all hand-crafted modifications grouped in
this function only.
"""
# This script is also imported (but not used) for the creation of the virtual environments.
# Thus, the imports might not be valid when starting with a clean slate.
import numpy as np
sys.path.insert(0, "../../../respy/tests")
from respy.tests.codes.random_model import generate_init
# Prepare fresh subdirectories
for which in ["old", "new"]:
if os.path.exists(which):
shutil.rmtree(which)
os.mkdir(which)
constr["level"] = 0.00
constr["fixed_ambiguity"] = True
constr["fixed_delta"] = True
constr["file_est"] = "../data.respy.dat"
init_dict = generate_init(constr)
# In the old release, there was just one location to define the step size for all derivative
# approximations.
eps = np.round(init_dict["SCIPY-BFGS"]["eps"], decimals=15)
init_dict["PRECONDITIONING"]["eps"] = eps
init_dict["FORT-BFGS"]["eps"] = eps
# We also endogenized the discount rate, so we need to restrict the analysis to estimations
# where the discount rate is fixed.
init_dict["BASICS"]["delta"] = init_dict["BASICS"]["coeffs"][0]
# We did not have any preconditioning implemented in the PYTHON version initially. We had to
# switch the preconditioning scheme in the new release and now use the absolute value and
# thus preserve the sign of the derivative.
init_dict["PRECONDITIONING"]["type"] = "identity"
# Some of the optimization algorithms were not available in the old release.
opt_pyth = np.random.choice(["SCIPY-BFGS", "SCIPY-POWELL"])
opt_fort = np.random.choice(["FORT-BFGS", "FORT-NEWUOA"])
if init_dict["PROGRAM"]["version"] == "PYTHON":
init_dict["ESTIMATION"]["optimizer"] = opt_pyth
else:
init_dict["ESTIMATION"]["optimizer"] = opt_fort
del init_dict["FORT-BOBYQA"]
del init_dict["SCIPY-LBFGSB"]
# The concept of bounds for parameters was not available and the coefficients in the
# initialization file were only printed to the first four digits.
for label in [
"HOME", "OCCUPATION A", "OCCUPATION B", "EDUCATION", "SHOCKS"
]:
num = len(init_dict[label]["fixed"])
coeffs = np.round(init_dict[label]["coeffs"], decimals=4).tolist()
init_dict[label]["bounds"] = [(None, None)] * num
init_dict[label]["coeffs"] = coeffs
# In the original release we treated TAU as an integer when printing to file by accident.
init_dict["ESTIMATION"]["tau"] = int(init_dict["ESTIMATION"]["tau"])
json.dump(init_dict, open("new/init_dict.respy.json", "w"))
# Added more fine grained scaling. Needs to be aligned across old/new with identity or flag
# False first and then we want to allow for more nuanced check.
init_dict["SCALING"] = {}
init_dict["SCALING"]["flag"] = init_dict["PRECONDITIONING"][
"type"] == "gradient"
init_dict["SCALING"]["minimum"] = init_dict["PRECONDITIONING"]["minimum"]
# More flexible parallelism. We removed the extra section onn parallelism.
init_dict["PARALLELISM"] = {}
init_dict["PARALLELISM"]["flag"] = init_dict["PROGRAM"]["procs"] > 1
init_dict["PARALLELISM"]["procs"] = init_dict["PROGRAM"]["procs"]
# We had a section that enforced the same step size for the derivative calculation in each.
init_dict["DERIVATIVES"] = {}
init_dict["DERIVATIVES"]["version"] = "FORWARD-DIFFERENCES"
init_dict["DERIVATIVES"]["eps"] = eps
# Cleanup
del init_dict["PROGRAM"]["procs"]
del init_dict["SCIPY-BFGS"]["eps"]
del init_dict["FORT-BFGS"]["eps"]
del init_dict["PRECONDITIONING"]
# Ambiguity was not yet available
del init_dict["AMBIGUITY"]
json.dump(init_dict, open("old/init_dict.respy.json", "w"))
def prepare_release_tests_9(constr):
""" This function prepares the initialization files so that they can be processed by both
releases under investigation. The idea is to have all hand-crafted modifications grouped in
this function only.
"""
def transform_to_logit(shares):
""" This function transform
"""
num_types = len(shares)
denominator = 1.0 / shares[0]
coeffs = []
for i in range(num_types):
coeffs += [np.log(shares[i] * denominator)]
return coeffs
# This script is also imported (but not used) for the creation of the virtual environments.
# Thus, the imports might not be valid when starting with a clean slate.
import numpy as np
sys.path.insert(0, "../../../respy/tests")
from respy.tests.codes.auxiliary import get_valid_shares
from respy.tests.codes.random_model import generate_init
# Prepare fresh subdirectories
for which in ["old", "new"]:
if os.path.exists(which):
shutil.rmtree(which)
os.mkdir(which)
constr["flag_estimation"] = True
init_dict = generate_init(constr)
# I now need to determine the number of types and sample the fixed type probabilites.
num_types = int((len(init_dict["TYPE SHARES"]["coeffs"]) / 2) + 1)
shares = get_valid_shares(num_types)
new_dict = init_dict.copy()
coeffs = transform_to_logit(shares)
new_dict["TYPE SHARES"]["coeffs"] = []
new_dict["TYPE SHARES"]["fixed"] = []
new_dict["TYPE SHARES"]["bounds"] = []
for coeff in coeffs[1:]:
new_dict["TYPE SHARES"]["coeffs"] += [coeff]
new_dict["TYPE SHARES"]["coeffs"] += [0.0]
new_dict["TYPE SHARES"]["fixed"] += [True, True]
new_dict["TYPE SHARES"]["bounds"] += [[None, None], [None, None]]
json.dump(new_dict, open("new/init_dict.respy.json", "w"))
old_dict = init_dict.copy()
old_dict["TYPE_SHIFTS"] = old_dict["TYPE SHIFTS"]
del old_dict["TYPE SHIFTS"]
old_dict["TYPE_SHARES"] = {}
old_dict["TYPE_SHARES"]["coeffs"] = shares
old_dict["TYPE_SHARES"]["fixed"] = [True] * num_types
old_dict["TYPE_SHARES"]["bounds"] = [[0.00, None]] * num_types
json.dump(old_dict, open("old/init_dict.respy.json", "w"))
