def test_valid_weight_shape(data):
# Same size
n = np.prod(data.y.shape)
weights = 1 + np.random.random_sample(n)
mod = PanelOLS(data.y, data.x, weights=weights)
mod.fit()
w = mod.weights.values2d
missing = PanelData(data.y).isnull | PanelData(data.x).isnull
expected = weights[~missing.squeeze()][:, None]
expected = expected / expected.mean()
assert_equal(w, expected)
# Per time
n = data.y.shape[0]
weights = 1 + np.random.random_sample(n)
mod = PanelOLS(data.y, data.x, weights=weights)
mod.fit()
w = mod.weights.values2d
expected = weights[:, None] @ np.ones((1, data.y.shape[1]))
expected = expected.T.ravel()
expected = expected[~missing.squeeze()][:, None]
expected = expected / expected.mean()
assert_equal(w, expected)
# Per entity
n = data.y.shape[1]
weights = 1 + np.random.random_sample(n)
mod = PanelOLS(data.y, data.x, weights=weights)
mod.fit()
w = mod.weights.values2d
expected = np.ones((data.y.shape[0], 1)) @ weights[None, :]
expected = expected.T.ravel()
expected = expected[~missing.squeeze()][:, None]
expected = expected / expected.mean()
assert_equal(w, expected)
weights = 1 + np.random.random_sample(data.y.shape)
mod = PanelOLS(data.y, data.x, weights=weights)
mod.fit()
w = mod.weights.values2d
expected = weights.T.ravel()
expected = expected[~missing.squeeze()][:, None]
expected = expected / expected.mean()
assert_equal(w, expected)
def test_valid_weight_shape(data):
# Same size
n = np.prod(data.y.shape)
weights = 1 + np.random.random_sample(n)
mod = PanelOLS(data.y, data.x, weights=weights)
mod.fit()
w = mod.weights.values2d
missing = PanelData(data.y).isnull | PanelData(data.x).isnull
expected = weights[~missing.squeeze()][:, None]
expected = expected / expected.mean()
assert_equal(w, expected)
# Per time
if isinstance(data.x, pd.DataFrame):
n = len(data.y.index.levels[1])
k = len(data.y.index.levels[0])
elif isinstance(data.x, np.ndarray):
n = data.y.shape[0]
k = data.y.shape[1]
else:
n = data.y.shape[1]
k = data.y.shape[2]
weights = 1 + np.random.random_sample(n)
mod = PanelOLS(data.y, data.x, weights=weights)
mod.fit()
w = mod.weights.values2d
expected = weights[:, None] @ np.ones((1, k))
expected = expected.T.ravel()
expected = expected[~missing.squeeze()][:, None]
expected = expected / expected.mean()
assert_equal(w, expected)
# Per entity
if isinstance(data.x, pd.DataFrame):
n = len(data.y.index.levels[0])
k = len(data.y.index.levels[1])
elif isinstance(data.x, np.ndarray):
n = data.y.shape[1]
k = data.y.shape[0]
else:
n = data.y.shape[2]
k = data.y.shape[1]
weights = 1 + np.random.random_sample(n)
mod = PanelOLS(data.y, data.x, weights=weights)
mod.fit()
w = mod.weights.values2d
expected = np.ones((k, 1)) @ weights[None, :]
expected = expected.T.ravel()
expected = expected[~missing.squeeze()][:, None]
expected = expected / expected.mean()
assert_equal(w, expected)
weights = 1 + np.random.random_sample(data.y.shape)
mod = PanelOLS(data.y, data.x, weights=weights)
mod.fit()
w = mod.weights.values2d
expected = weights.T.ravel()
expected = expected[~missing.squeeze()][:, None]
expected = expected / expected.mean()
assert_equal(w, expected)
