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Identity Weighting Matrix #477

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merged 3 commits into from
Feb 10, 2024
Merged

Identity Weighting Matrix #477

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6e57bbb
added identity weighting matrix
sidd3888 Feb 7, 2024
3e9d723
identity weight test
sidd3888 Feb 9, 2024
2fef810
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Feb 9, 2024
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2 changes: 1 addition & 1 deletion src/estimagic/estimation/estimate_msm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -149,7 +149,7 @@ def estimate_msm(
# Check and process inputs
# ==================================================================================

if weights not in ["diagonal", "optimal"]:
if weights not in ["diagonal", "optimal", "identity"]:
raise NotImplementedError("Custom weighting matrices are not yet implemented.")

is_optimized = optimize_options is False
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4 changes: 3 additions & 1 deletion src/estimagic/estimation/msm_weighting.py
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Expand Up @@ -78,7 +78,7 @@ def get_weighting_matrix(
Args:
moments_cov (pandas.DataFrame or numpy.ndarray): Square DataFrame or Array
with the covariance matrix of the moment conditions for msm estimation.
method (str): One of "optimal", "diagonal".
method (str): One of "optimal", "diagonal", or "identity".
empirical_moments (pytree): Pytree containing empirical moments. Used to get
the tree structure
clip_value (float): Bound at which diagonal elements of the moments_cov are
Expand Down Expand Up @@ -106,6 +106,8 @@ def get_weighting_matrix(
elif method == "diagonal":
diagonal_values = 1 / np.clip(np.diagonal(_internal_cov), clip_value, np.inf)
array_weights = np.diag(diagonal_values)
elif method == "identity":
array_weights = np.identity(_internal_cov.shape[0])
else:
raise ValueError(f"Invalid method: {method}")

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8 changes: 6 additions & 2 deletions tests/estimation/test_msm_weighting.py
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Expand Up @@ -22,7 +22,7 @@ def expected_values():
cov_np = np.diag([1, 2, 3])
cov_pd = pd.DataFrame(cov_np)

test_cases = itertools.product([cov_np, cov_pd], ["diagonal", "optimal"])
test_cases = itertools.product([cov_np, cov_pd], ["diagonal", "optimal", "identity"])


@pytest.mark.parametrize("moments_cov, method", test_cases)
Expand All @@ -38,7 +38,11 @@ def test_get_weighting_matrix(moments_cov, method):
assert calculated.columns.equals(moments_cov.columns)
calculated = calculated.to_numpy()

expected = np.diag(1 / np.array([1, 2, 3]))
if method == "identity":
expected = np.identity(cov_np.shape[0])
else:
expected = np.diag(1 / np.array([1, 2, 3]))

aaae(calculated, expected)


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