def calculate_fundamental_sensitivity_to_removal(jac, moments_cov,
params_cov_opt):
"""calculate the fundamental sensitivity to removal.
The sensitivity measure is calculated for each parameter wrt each moment.
It answers the following question: How much precision would be lost if the kth
moment was excluded from the estimation with if the optimal weighting matrix is
used?
Args:
jac (np.ndarray or pandas.DataFrame): The jacobian of simulate_moments with
respect to params, evaluated at the point estimates.
weights (np.ndarray or pandas.DataFrame): The weighting matrix used for
msm estimation.
moments_cov (numpy.ndarray or pandas.DataFrame): The covariance matrix of the
empirical moments.
params_cov_opt (numpy.ndarray or pandas.DataFrame): The covariance matrix of the
parameter estimates. Note that this needs to be the parameter covariance
matrix using the formula for asymptotically optimal MSM.
Returns:
np.ndarray or pd.DataFrame: Sensitivity measure with shape (n_params, n_moments)
"""
_jac, _moments_cov, _params_cov_opt, names = process_pandas_arguments(
jac=jac,
moments_cov=moments_cov,
params_cov_opt=params_cov_opt,
)
m5 = []
for k in range(len(_moments_cov)):
g_k = np.copy(_jac)
g_k = np.delete(g_k, k, axis=0)
s_k = np.copy(_moments_cov)
s_k = np.delete(s_k, k, axis=0)
s_k = np.delete(s_k, k, axis=1)
sigma_k = _sandwich(g_k, robust_inverse(s_k, INVALID_SENSITIVITY_MSG))
sigma_k = robust_inverse(sigma_k, INVALID_SENSITIVITY_MSG)
m5k = sigma_k - _params_cov_opt
m5k = m5k.diagonal()
m5.append(m5k)
m5 = np.array(m5).T
params_variances = np.diagonal(_params_cov_opt)
e5 = m5 / params_variances.reshape(-1, 1)
if names:
e5 = pd.DataFrame(e5,
index=names.get("params"),
columns=names.get("moments"))
return e5
def calculate_fundamental_sensitivity_to_noise(jac, weights, moments_cov,
params_cov_opt):
"""calculate the fundamental sensitivity to noise.
The sensitivity measure is calculated for each parameter wrt each moment.
It answers the following question: How much precision would be lost if the kth
moment was subject to a little additional noise if the optimal weighting matrix
is used?
Args:
jac (np.ndarray or pandas.DataFrame): The jacobian of simulate_moments with
respect to params, evaluated at the point estimates.
weights (np.ndarray or pandas.DataFrame): The weighting matrix used for
msm estimation.
moments_cov (numpy.ndarray or pandas.DataFrame): The covariance matrix of the
empirical moments.
params_cov_opt (numpy.ndarray or pandas.DataFrame): The covariance matrix of the
parameter estimates. Note that this needs to be the parameter covariance
matrix using the formula for asymptotically optimal MSM.
Returns:
np.ndarray or pd.DataFrame: Sensitivity measure with shape (n_params, n_moments)
"""
_jac, _weights, _moments_cov, _params_cov_opt, names = process_pandas_arguments(
jac=jac,
weights=weights,
moments_cov=moments_cov,
params_cov_opt=params_cov_opt)
m2 = []
for k in range(len(_weights)):
mask_matrix_o = np.zeros(shape=_weights.shape)
mask_matrix_o[k, k] = 1
meat = _sandwich_plus(_jac, _weights, mask_matrix_o)
m2k = _params_cov_opt @ meat @ _params_cov_opt
m2k = np.diagonal(m2k)
m2.append(m2k)
m2 = np.array(m2).T
moments_variances = np.diagonal(_moments_cov)
params_variances = np.diagonal(_params_cov_opt)
e2 = m2 / params_variances.reshape(-1, 1)
e2 = e2 * moments_variances
if names:
e2 = pd.DataFrame(e2,
index=names.get("params"),
columns=names.get("moments"))
return e2
def test_process_pandas_arguments_all_pd(inputs):
*arrays, names = process_pandas_arguments(**inputs)
for arr in arrays:
assert isinstance(arr, np.ndarray)
expected_names = {"moments": list(range(5)), "params": ["a", "b", "c"]}
for key, value in expected_names.items():
assert names[key].tolist() == value
def calculate_actual_sensitivity_to_removal(jac, weights, moments_cov,
params_cov):
"""calculate the actual sensitivity to removal.
The sensitivity measure is calculated for each parameter wrt each moment.
It answers the following question: How much precision would be lost if the kth
moment was excluded from the estimation if "weights" is used as weighting
matrix?
Args:
sensitivity_to_bias (np.ndarray or pandas.DataFrame): See
``calculate_sensitivity_to_bias`` for details.
weights (np.ndarray or pandas.DataFrame): The weighting matrix used for
msm estimation.
moments_cov (numpy.ndarray or pandas.DataFrame): The covariance matrix of the
empirical moments.
params_cov (numpy.ndarray or pandas.DataFrame): The covariance matrix of the
parameter estimates.
Returns:
np.ndarray or pd.DataFrame: Sensitivity measure with shape (n_params, n_moments)
"""
m4 = []
_jac, _weights, _moments_cov, _params_cov, names = process_pandas_arguments(
jac=jac,
weights=weights,
moments_cov=moments_cov,
params_cov=params_cov)
for k in range(len(_weights)):
weight_tilde_k = np.copy(_weights)
weight_tilde_k[k, :] = 0
weight_tilde_k[:, k] = 0
sigma_tilde_k = cov_robust(_jac, weight_tilde_k, _moments_cov)
m4k = sigma_tilde_k - _params_cov
m4k = m4k.diagonal()
m4.append(m4k)
m4 = np.array(m4).T
params_variances = np.diagonal(_params_cov)
e4 = m4 / params_variances.reshape(-1, 1)
if names:
e4 = pd.DataFrame(e4,
index=names.get("params"),
columns=names.get("moments"))
return e4
def calculate_actual_sensitivity_to_noise(sensitivity_to_bias, weights,
moments_cov, params_cov):
"""calculate the actual sensitivity to noise.
The sensitivity measure is calculated for each parameter wrt each moment.
It answers the following question: How much precision would be lost if the kth
moment was subjet to a little additional noise if "weights" is used as
weighting matrix?
Args:
sensitivity_to_bias (np.ndarray or pandas.DataFrame): See
``calculate_sensitivity_to_bias`` for details.
weights (np.ndarray or pandas.DataFrame): The weighting matrix used for
msm estimation.
moments_cov (numpy.ndarray or pandas.DataFrame): The covariance matrix of the
empirical moments.
params_cov (numpy.ndarray or pandas.DataFrame): The covariance matrix of the
parameter estimates.
Returns:
np.ndarray or pd.DataFrame: Sensitivity measure with shape (n_params, n_moments)
"""
if isinstance(sensitivity_to_bias, pd.DataFrame):
sensitivity_to_bias = sensitivity_to_bias.to_numpy()
_weights, _moments_cov, _params_cov, names = process_pandas_arguments(
weights=weights, moments_cov=moments_cov, params_cov=params_cov)
m3 = []
for k in range(len(_weights)):
mask_matrix_o = np.zeros(shape=_weights.shape)
mask_matrix_o[k, k] = 1
m3k = _sandwich(sensitivity_to_bias.T, mask_matrix_o)
m3k = np.diagonal(m3k)
m3.append(m3k)
m3 = np.array(m3).T
moments_variances = np.diagonal(_moments_cov)
params_variances = np.diagonal(_params_cov)
e3 = m3 / params_variances.reshape(-1, 1)
e3 = e3 * moments_variances
if names:
e3 = pd.DataFrame(e3,
index=names.get("params"),
columns=names.get("moments"))
return e3
def cov_jacobian(jac):
"""Covariance based on outer product of jacobian of loglikeobs.
Args:
jac (numpy.ndarray): 2d array jacobian matrix of dimension (nobs, nparams)
Returns:
numpy.ndarray: covariance matrix of size (nparams, nparams)
Resources: Marno Verbeek - A guide to modern econometrics.
"""
_jac, names = process_pandas_arguments(jac=jac)
info_matrix = np.dot((_jac.T), _jac)
cov = robust_inverse(info_matrix, msg=INVALID_INFERENCE_MSG)
if "params" in names:
cov = pd.DataFrame(cov, columns=names["params"], index=names["params"])
return cov
def test_process_pandas_arguments_incompatible_names(inputs):
inputs["jac"].columns = ["c", "d", "e"]
with pytest.raises(ValueError):
process_pandas_arguments(**inputs)
def calculate_sensitivity_to_weighting(jac, weights, moments_cov, params_cov):
"""calculate the sensitivity to weighting.
The sensitivity measure is calculated for each parameter wrt each moment.
It answers the following question: How would the precision change if the weight of
the kth moment is increased a little?
Args:
sensitivity_to_bias (np.ndarray or pandas.DataFrame): See
``calculate_sensitivity_to_bias`` for details.
weights (np.ndarray or pandas.DataFrame): The weighting matrix used for
msm estimation.
moments_cov (numpy.ndarray or pandas.DataFrame): The covariance matrix of the
empirical moments.
params_cov (numpy.ndarray or pandas.DataFrame): The covariance matrix of the
parameter estimates.
Returns:
np.ndarray or pd.DataFrame: Sensitivity measure with shape (n_params, n_moments)
"""
_jac, _weights, _moments_cov, _params_cov, names = process_pandas_arguments(
jac=jac,
weights=weights,
moments_cov=moments_cov,
params_cov=params_cov)
gwg_inverse = _sandwich(_jac, _weights)
gwg_inverse = robust_inverse(gwg_inverse, INVALID_SENSITIVITY_MSG)
m6 = []
for k in range(len(_weights)):
mask_matrix_o = np.zeros(shape=_weights.shape)
mask_matrix_o[k, k] = 1
m6k_1 = gwg_inverse @ _sandwich(_jac, mask_matrix_o) @ _params_cov
m6k_2 = (gwg_inverse @ _jac.T @ mask_matrix_o @ _moments_cov @ _weights
@ _jac @ gwg_inverse)
m6k_3 = (gwg_inverse @ _jac.T @ _weights @ _moments_cov @ mask_matrix_o
@ _jac @ gwg_inverse)
m6k_4 = _params_cov @ _sandwich(_jac, mask_matrix_o) @ gwg_inverse
m6k = -m6k_1 + m6k_2 + m6k_3 - m6k_4
m6k = m6k.diagonal()
m6.append(m6k)
m6 = np.array(m6).T
weights_diagonal = np.diagonal(_weights)
params_variances = np.diagonal(_params_cov)
e6 = m6 / params_variances.reshape(-1, 1)
e6 = e6 * weights_diagonal
if names:
e6 = pd.DataFrame(e6,
index=names.get("params"),
columns=names.get("moments"))
return e6
