Merge pull request #637 from bashtage/rls-5.4.0

MAINT: Small updates for 5.4.0 release

Author: bashtage

.github/workflows/generate-documentation.yml

@@ -19,13 +19,13 @@ jobs:
         python-version: [3.8]
     steps:
     - name: Checkout
-      uses: actions/checkout@v2
+      uses: actions/checkout@v3
       with:
         fetch-depth: 0
     - name: Install pandoc
-      uses: r-lib/actions/setup-pandoc@v1
+      uses: r-lib/actions/setup-pandoc@v2
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v3
       with:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies

arch/bootstrap/base.py

@@ -833,7 +833,6 @@ def conf_int(
 
         if not _reuse:
             if reuse:
-
                 warn = (
                     "The conditions to reuse the previous bootstrap has "
                     "not been satisfied. A new bootstrap will be used."

arch/bootstrap/multiple_comparison.py

@@ -316,8 +316,8 @@ def included(self) -> list[Hashable]:
             List of column indices or names of the included models
         """
         self._has_been_computed()
-        included = self._pvalues.Pvalue > self.size
-        included = list(self._pvalues.index[included])
+        incl_loc = self._pvalues.Pvalue > self.size
+        included = list(self._pvalues.index[incl_loc])
         included.sort()
         return included
 
@@ -332,8 +332,8 @@ def excluded(self) -> list[Hashable]:
             List of column indices or names of the excluded models
         """
         self._has_been_computed()
-        excluded = self._pvalues.Pvalue <= self.size
-        excluded = list(self._pvalues.index[excluded])
+        excl_loc = self._pvalues.Pvalue <= self.size
+        excluded = list(self._pvalues.index[excl_loc])
         excluded.sort()
         return excluded
 

arch/covariance/kernel.py

@@ -180,7 +180,6 @@ def __init__(
         weights: ArrayLike | None = None,
         force_int: bool = False,
     ):
-
         self._x_orig = ensure2d(x, "x")
         self._x = np.asarray(self._x_orig)
         self._center = center

arch/data/core_cpi/__init__.py

@@ -4,7 +4,6 @@
 
 
 def load() -> DataFrame:
-
     """
     Load the Core CPI data used in the examples
 

arch/data/crude/__init__.py

@@ -4,7 +4,6 @@
 
 
 def load() -> DataFrame:
-
     """
     Load the Core CPI data used in the examples
 

arch/data/default/__init__.py

@@ -4,7 +4,6 @@
 
 
 def load() -> DataFrame:
-
     """
     Load the AAA and BAA rates used in the examples
 

arch/data/frenchdata/__init__.py

@@ -4,7 +4,6 @@
 
 
 def load() -> DataFrame:
-
     """
     Load the Fama-French factor data used in the examples
 

arch/data/nasdaq/__init__.py

@@ -4,7 +4,6 @@
 
 
 def load() -> DataFrame:
-
     """
     Load the NASDAQ Composite data used in the examples
 

arch/data/sp500/__init__.py

@@ -4,7 +4,6 @@
 
 
 def load() -> DataFrame:
-
     """
     Load the S&P 500 data used in the examples
 

arch/data/vix/__init__.py

@@ -4,7 +4,6 @@
 
 
 def load() -> DataFrame:
-
     """
     Load the VIX Index data used in the examples
 

arch/data/wti/__init__.py

@@ -4,7 +4,6 @@
 
 
 def load() -> DataFrame:
-
     """
     Load the West Texas Intermediate crude oil price data used in the examples
 

arch/tests/covariance/test_covariance.py

@@ -126,7 +126,7 @@ def test_bartlett_auto(data: ArrayLike):
     assert int(nw.bandwidth) == expected_bw
     expected = 1.0 - np.arange(nw.bandwidth + 1) / (nw.bandwidth + 1)
     assert_allclose(nw.kernel_weights, expected)
-    resid = data - data.mean(0)
+    resid = data - np.asarray(data.mean(0))
     resid = np.asarray(resid)
     nobs = resid.shape[0]
     expected_cov = resid.T @ resid / nobs

arch/tests/unitroot/cointegration_data.py

@@ -32,7 +32,6 @@ def data(request) -> Tuple[Float64Array, Float64Array]:
 
 @pytest.fixture(scope="module", params=[True, False], ids=["pandas", "numpy"])
 def trivariate_data(request) -> Tuple[ArrayLike2D, ArrayLike2D]:
-
     rs = np.random.RandomState([922019, 12882912, 192010, 10189, 109981])
     nobs = 1000
     burn = 100

arch/tests/univariate/test_moment.py

@@ -47,7 +47,6 @@ def f(x, n):
         return (x**n) * exp(dist.loglikelihood(params, x, sigma2, True))
 
     for n in range(6):  # moments 0-5
-
         # complete moments
         m_quad = quad(f, -inf, inf, args=(n,))[0]
         m_method = dist.moment(n, params)

arch/tests/utility/test_cov.py

@@ -12,7 +12,6 @@
 class TestVarNW:
     @classmethod
     def setup_class(cls):
-
         cls.rng = RandomState(12345)
         cls.cpi = log(dataset_loader(macrodata)["cpi"])
         cls.inflation = diff(cls.cpi)

arch/unitroot/cointegration.py

@@ -1029,7 +1029,7 @@ def _final_statistics(self, theta: pd.Series) -> tuple[pd.Series, float, float]:
         center = 0.0
         tss_df = 0
         if "c" in self._trend:
-            center = self._y.mean()
+            center = float(self._y.mean())
             tss_df = 1
         y_centered = self._y - center
         ssr = resid.T @ resid

arch/unitroot/critical_values/simulation/adf_z_critical_values_simulation.py

@@ -8,7 +8,7 @@
 Remote clusters can be used by modifying the call to Client.
 """
 import datetime
-from typing import cast
+from typing import Literal, cast
 
 from ipyparallel import Client
 from numpy import array, nan, ndarray, percentile, savez
@@ -29,7 +29,9 @@
     from numpy.random import RandomState
 
 
-def wrapper(n: int, trend: str, b: int, seed: int = 0) -> ndarray:
+def wrapper(
+    n: int, trend: Literal["n", "c", "ct", "ctt"], b: int, seed: int = 0
+) -> ndarray:
     """
     Wraps and blocks the main simulation so that the maximum amount of memory
     can be controlled on multi processor systems when executing in parallel

arch/unitroot/critical_values/simulation/engle_granger_simulation.py

@@ -228,7 +228,7 @@ def block(
 
     shuffle(remaining_configs)
     if njobs == 1:
-        for ((idx, rg), trend) in remaining_configs:
+        for (idx, rg), trend in remaining_configs:
             partial_block(rg, trend=trend, idx=idx)
     else:
         Parallel(verbose=50, n_jobs=njobs)(

arch/unitroot/critical_values/simulation/phillips-ouliaris-simulation-process.py

@@ -20,14 +20,14 @@
 META = {"z_a": "negative", "z_t": "negative", "p_u": "positive", "p_z": "positive"}
 CRITICAL_VALUES = (1, 5, 10)
 PLOT = False
-WINS: Dict[int, int] = defaultdict(lambda: 0)
+WINS: dict[int, int] = defaultdict(lambda: 0)
 # 1. Load data
 # 2. Compute critical values
 
 
 class PvalueResult(NamedTuple):
-    large_p: List[float]
-    small_p: List[float]
+    large_p: list[float]
+    small_p: list[float]
     tau_max: float
     tau_star: float
     tau_min: float
@@ -75,7 +75,7 @@ def xval(lhs: np.ndarray, rhs: np.ndarray, log: bool = True, folds: int = 5) ->
 
 def estimate_cv_regression(
     results: pd.DataFrame, statistic: str
-) -> Tuple[Dict[int, List[float]], float]:
+) -> tuple[dict[int, list[float]], float]:
     # For percentiles 1, 5 and 10, regress on a constant, and powers of 1/T
     out = {}
     quantiles = np.asarray(results.index)

arch/univariate/mean.py

@@ -546,7 +546,7 @@ def _check_specification(self) -> None:
             if isinstance(self._x, pd.Series):
                 self._x = pd.DataFrame(self._x)
             elif self._x.ndim == 1:
-                self._x = self._x[:, None]
+                self._x = np.asarray(self._x)[:, None]
             if self._x.ndim != 2 or self._x.shape[0] != self._y.shape[0]:
                 raise ValueError(
                     "x must be nobs by n, where nobs is the same as "

arch/univariate/recursions_python.py

@@ -766,7 +766,6 @@ def update(
         sigma2: Float64Array,
         var_bounds: Float64Array,
     ) -> None:
-
         loc = 0
         sigma2[t] = parameters[loc]
         loc += 1
@@ -814,7 +813,6 @@ def update(
         sigma2: Float64Array,
         var_bounds: Float64Array,
     ) -> None:
-
         backcast = self.backcast
 
         sigma2[t] = parameters[0]
@@ -854,7 +852,6 @@ def update(
         sigma2: Float64Array,
         var_bounds: Float64Array,
     ) -> None:
-
         sigma2[t] = self.params[0]
         if t == 0:
             sigma2[t] += self.backcast
@@ -896,7 +893,6 @@ def update_weights(self, theta: float) -> None:
     def initialize_update(
         self, parameters: Float64Array, backcast: float | Float64Array, nobs: int
     ) -> None:
-
         self.update_weights(parameters[2 + self.asym])
         alpha = parameters[1]
         if self.asym:
@@ -919,7 +915,6 @@ def update(
         sigma2: Float64Array,
         var_bounds: Float64Array,
     ) -> None:
-
         omega = parameters[0]
         if t > 0:
             self.resids2[t - 1] = resids[t - 1] * resids[t - 1]
@@ -1009,7 +1004,6 @@ def update(
         sigma2: Float64Array,
         var_bounds: Float64Array,
     ) -> None:
-
         w = self.combination_weights
         mus = self.smoothing_parameters
         if t == 0:

arch/univariate/volatility.py

@@ -1234,7 +1234,6 @@ def _analytic_forecast(
         start: int,
         horizon: int,
     ) -> VarianceForecast:
-
         sigma2, forecasts = self._one_step_forecast(
             parameters, resids, backcast, var_bounds, horizon, start
         )
@@ -1298,7 +1297,6 @@ def _simulate_paths(
         scaled_shock: Float64Array,
         asym_scaled_shock: Float64Array,
     ) -> tuple[Float64Array, Float64Array, Float64Array]:
-
         power = self.power
         p, o, q = self.p, self.o, self.q
         omega = parameters[0]
@@ -1346,7 +1344,6 @@ def _simulation_forecast(
         simulations: int,
         rng: RNGType,
     ) -> VarianceForecast:
-
         sigma2, forecasts = self._one_step_forecast(
             parameters, resids, backcast, var_bounds, horizon, start
         )
@@ -2344,7 +2341,6 @@ def compute_variance(
         backcast: float | Float64Array,
         var_bounds: Float64Array,
     ) -> Float64Array:
-
         nobs = resids.shape[0]
         kmax = self.kmax
         w = self._ewma_combination_weights()
@@ -2698,7 +2694,6 @@ def _analytic_forecast(
         start: int,
         horizon: int,
     ) -> VarianceForecast:
-
         _, forecasts = self._one_step_forecast(
             parameters, resids, backcast, var_bounds, horizon, start
         )
@@ -3035,7 +3030,6 @@ def bounds(self, resids: Float64Array) -> list[tuple[float, float]]:
         return bounds
 
     def constraints(self) -> tuple[Float64Array, Float64Array]:
-
         # omega > 0 <- 1
         # 0 <= d <= 1 <- 2
         # 0 <= phi <= (1 - d) / 2 <- 2
@@ -3600,7 +3594,6 @@ def _simulate_paths(
         shock: Float64Array,
         abs_shock: Float64Array,
     ) -> tuple[Float64Array, Float64Array, Float64Array]:
-
         if self._est_delta:
             delta = parameters[-1]
         else:

ci/azure/azure_template_posix.yml

@@ -41,6 +41,8 @@ jobs:
         MATPLOTLIB: 3.3.0
         NUMPY: 1.19.0
         PANDAS: 1.1.0
+      python_311:
+        python.version: '3.11'
       python_310:
         python.version: '3.10'
         test.install: true
@@ -61,6 +63,7 @@ jobs:
         NUMPY: 1.21.0
         USE_NUMBA: false
         MATPLOTLIB: 3.3.0
+        PANDAS: 1.5.0
         PYTEST_PATTERN: "(slow or not slow)"
       python_39_numpy_118:
         python.version: '3.9'

ci/azure/azure_template_windows.yml

@@ -22,6 +22,9 @@ jobs:
         python.version: '3.9'
       python310_win_latest:
         python.version: '3.10'
+      # Disabled since numba not available
+      # python311_win_latest:
+      #   python.version: '3.11'
     maxParallel: 10
 
   steps: