Merge pull request #764 from bashtage/update-ci

CI: Add Python 3.13 to run

arch/tests/unitroot/test_unitroot.py

@@ -15,7 +15,12 @@
 import scipy.stats as stats
 from statsmodels.datasets import macrodata, modechoice, nile, randhie, sunspots
 from statsmodels.regression.linear_model import OLS
-from statsmodels.tsa.stattools import _autolag, lagmat
+from statsmodels.tsa.stattools import lagmat
+
+try:
+    from statsmodels.tsa.stattools import _autolag
+except ImportError:
+    from statsmodels.tsa.stattools._stattools import _autolag
 
 from arch.unitroot import ADF, DFGLS, KPSS, PhillipsPerron, VarianceRatio, ZivotAndrews
 from arch.unitroot.critical_values.dickey_fuller import tau_2010

arch/tests/univariate/test_distribution.py

@@ -58,7 +58,7 @@ def test_studentst(self, seed):
         # Direct calculation of PDF, then log
         constant = np.exp(gammaln(0.5 * (v + 1)) - gammaln(0.5 * v))
         pdf = constant / np.sqrt(np.pi * (v - 2) * self.sigma2)
-        pdf *= (1 + self.resids ** 2.0 / (self.sigma2 * (v - 2))) ** (-(v + 1) / 2)
+        pdf *= (1 + self.resids**2.0 / (self.sigma2 * (v - 2))) ** (-(v + 1) / 2)
         ll2 = np.log(pdf).sum()
         assert_almost_equal(ll1, ll2)
 
@@ -87,14 +87,14 @@ def test_skewstudent(self, seed):
         # Direct calculation of PDF, then log
         const_c = gamma((eta + 1) / 2) / ((np.pi * (eta - 2)) ** 0.5 * gamma(eta / 2))
         const_a = 4 * lam * const_c * (eta - 2) / (eta - 1)
-        const_b = (1 + 3 * lam ** 2 - const_a ** 2) ** 0.5
+        const_b = (1 + 3 * lam**2 - const_a**2) ** 0.5
 
-        resids = self.resids / self.sigma2 ** 0.5
+        resids = self.resids / self.sigma2**0.5
         power = -(eta + 1) / 2
         pdf = (
             const_b
             * const_c
-            / self.sigma2 ** 0.5
+            / self.sigma2**0.5
             * (
                 1
                 + 1

arch/tests/univariate/test_mean.py

@@ -1354,7 +1354,7 @@ def test_param_cov():
 def test_plot_bad_index():
     import matplotlib.pyplot as plt
 
-    idx = sorted(f"{a}{b}{c}" for a, b, c, in product(*([ascii_lowercase] * 3)))
+    idx = sorted(f"{a}{b}{c}" for a, b, c in product(*([ascii_lowercase] * 3)))
     sp500_copy = SP500.copy()
     sp500_copy.index = idx[: sp500_copy.shape[0]]
     res = ConstantMean(sp500_copy).fit(disp=False)

arch/unitroot/critical_values/simulation/dfgls_critical_values_simulation.py

@@ -136,7 +136,7 @@ def dfgsl_simulation(
         results = np.zeros((len(percentiles), len(T), EX_NUM))
 
         for i in range(EX_NUM):
-            print(f"Experiment Number {i + 1} of {EX_NUM} " "(trend {tr})")
+            print(f"Experiment Number {i + 1} of {EX_NUM} (trend {tr})")
             now = datetime.datetime.now()
             parallel, p_func, n_jobs = parallel_func(
                 wrapper, n_jobs=NUM_JOBS, verbose=2

arch/unitroot/unitroot.py

@@ -120,7 +120,7 @@
 
 
 def _is_reduced_rank(
-    x: Union[Float64Array, DataFrame]
+    x: Union[Float64Array, DataFrame],
 ) -> tuple[bool, Union[int, None]]:
     """
     Check if a matrix has reduced rank preferring quick checks

arch/univariate/base.py

@@ -45,8 +45,13 @@
 )
 from arch.utility.testing import WaldTestStatistic
 
+MPL_LT_310 = False
 try:
+    from matplotlib import __version__
     from matplotlib.figure import Figure
+    from packaging.version import Version
+
+    MPL_LT_310 = Version(__version__) < Version("3.10.0")
 except ImportError:
     pass
 
@@ -1642,7 +1647,7 @@ def hedgehog_plot(
 
         fig, ax = plt.subplots(1, 1)
         use_date = isinstance(self._dep_var.index, pd.DatetimeIndex)
-        plot_fn = ax.plot_date if use_date else ax.plot
+        plot_fn = ax.plot_date if use_date and MPL_LT_310 else ax.plot
         x_values = np.array(self._dep_var.index)
         if plot_mean:
             y_values = np.asarray(self._dep_var)

arch/univariate/distribution.py

@@ -2,6 +2,7 @@
 Distributions to use in ARCH models.  All distributions must inherit from
 :class:`Distribution` and provide the same methods with the same inputs.
 """
+
 from abc import ABCMeta, abstractmethod
 from collections.abc import Sequence
 from typing import Callable, Optional, Union
@@ -100,8 +101,8 @@ def _check_constraints(
         for p, n, b in zip(params, self.name, bounds):
             if not (b[0] <= p <= b[1]):
                 raise ValueError(
-                    "{} does not satisfy the bounds requirement "
-                    "of ({}, {})".format(n, *b)
+                    f"{n} does not satisfy the bounds requirement of "
+                    f"({b[0]}, {b[1]})"
                 )
         return asarray(params)
 
@@ -205,7 +206,7 @@ def loglikelihood(
         resids: ArrayLike,
         sigma2: ArrayLike,
         individual: bool = False,
-    ) -> Union[float , Float64Array]:
+    ) -> Union[float, Float64Array]:
         """
         Loglikelihood evaluation.
 
@@ -264,7 +265,7 @@ def ppf(
         self,
         pits: Union[float, Sequence[float], ArrayLike1D],
         parameters: Union[Sequence[float], ArrayLike1D, None] = None,
-    ) -> Union[float , Float64Array]:
+    ) -> Union[float, Float64Array]:
         """
         Inverse cumulative density function (ICDF)
 
@@ -414,7 +415,7 @@ def loglikelihood(
         resids: ArrayLike,
         sigma2: ArrayLike,
         individual: bool = False,
-    ) -> Union[float , Float64Array]:
+    ) -> Union[float, Float64Array]:
         r"""Computes the log-likelihood of assuming residuals are normally
         distributed, conditional on the variance
 
@@ -446,7 +447,7 @@ def loglikelihood(
             +\frac{x^{2}}{\sigma^{2}}\right)
 
         """
-        lls = -0.5 * (log(2 * pi) + log(sigma2) + resids ** 2.0 / sigma2)
+        lls = -0.5 * (log(2 * pi) + log(sigma2) + resids**2.0 / sigma2)
         if individual:
             return lls
         else:
@@ -557,7 +558,7 @@ def loglikelihood(
         resids: ArrayLike,
         sigma2: ArrayLike,
         individual: bool = False,
-    ) -> Union[float , Float64Array]:
+    ) -> Union[float, Float64Array]:
         r"""Computes the log-likelihood of assuming residuals are have a
         standardized (to have unit variance) Student's t distribution,
         conditional on the variance.
@@ -595,7 +596,7 @@ def loglikelihood(
         nu = parameters[0]
         lls = gammaln((nu + 1) / 2) - gammaln(nu / 2) - log(pi * (nu - 2)) / 2
         lls -= 0.5 * (log(sigma2))
-        lls -= ((nu + 1) / 2) * (log(1 + (resids ** 2.0) / (sigma2 * (nu - 2))))
+        lls -= ((nu + 1) / 2) * (log(1 + (resids**2.0) / (sigma2 * (nu - 2))))
 
         if individual:
             return lls
@@ -685,7 +686,7 @@ def partial_moment(
         nu = parameters[0]
         var = nu / (nu - 2)
         scale = 1.0 / sqrt(var)
-        moment = (scale ** n) * self._ord_t_partial_moment(n, z / scale, nu)
+        moment = (scale**n) * self._ord_t_partial_moment(n, z / scale, nu)
         return moment
 
     @staticmethod
@@ -729,9 +730,9 @@ def _ord_t_partial_moment(n: int, z: float, nu: float) -> float:
         elif n == 1:
             c = gamma(0.5 * (nu + 1)) / (sqrt(nu * pi) * gamma(0.5 * nu))
             e = 0.5 * (nu + 1)
-            moment = (0.5 * (c * nu) / (1 - e)) * ((1 + (z ** 2) / nu) ** (1 - e))
+            moment = (0.5 * (c * nu) / (1 - e)) * ((1 + (z**2) / nu) ** (1 - e))
         else:
-            t1 = (z ** (n - 1)) * (nu + z ** 2) * stats.t.pdf(z, nu)
+            t1 = (z ** (n - 1)) * (nu + z**2) * stats.t.pdf(z, nu)
             t2 = (n - 1) * nu * StudentsT._ord_t_partial_moment(n - 2, z, nu)
             moment = (1 / (n - nu)) * (t1 - t2)
         return moment
@@ -846,7 +847,7 @@ def loglikelihood(
         const_a = self.__const_a(parameters)
         const_b = self.__const_b(parameters)
 
-        resids = resids / sigma2 ** 0.5
+        resids = resids / sigma2**0.5
         lls = log(const_b) + const_c - log(sigma2) / 2
         if abs(lam) >= 1.0:
             lam = sign(lam) * (1.0 - 1e-6)
@@ -911,7 +912,7 @@ def simulate(
     def parameter_names(self) -> list[str]:
         return ["eta", "lambda"]
 
-    def __const_a(self, parameters: Union[Float64Array , Sequence[float]]) -> float:
+    def __const_a(self, parameters: Union[Float64Array, Sequence[float]]) -> float:
         """
         Compute a constant.
 
@@ -930,7 +931,7 @@ def __const_a(self, parameters: Union[Float64Array , Sequence[float]]) -> float:
         c = self.__const_c(parameters)
         return float(4 * lam * exp(c) * (eta - 2) / (eta - 1))
 
-    def __const_b(self, parameters: Union[Float64Array , Sequence[float]]) -> float:
+    def __const_b(self, parameters: Union[Float64Array, Sequence[float]]) -> float:
         """
         Compute b constant.
 
@@ -946,7 +947,7 @@ def __const_b(self, parameters: Union[Float64Array , Sequence[float]]) -> float:
         """
         lam = float(parameters[1])
         a = self.__const_a(parameters)
-        return (1 + 3 * lam ** 2 - a ** 2) ** 0.5
+        return (1 + 3 * lam**2 - a**2) ** 0.5
 
     @staticmethod
     def __const_c(parameters: Union[Float64Array, Sequence[float]]) -> float:
@@ -999,7 +1000,7 @@ def ppf(
         self,
         pits: Union[float, Sequence[float], ArrayLike1D],
         parameters: Union[Sequence[float], ArrayLike1D, None] = None,
-    ) -> Union[float , Float64Array]:
+    ) -> Union[float, Float64Array]:
         parameters = self._check_constraints(parameters)
         scalar = isscalar(pits)
         if scalar:
@@ -1052,8 +1053,8 @@ def moment(
             )
             l_pmom = ((-1) ** k) * r_pmom
 
-            lhs = (1 - lam) * (lscale ** k) * (loc ** (n - k)) * l_pmom
-            rhs = (1 + lam) * (rscale ** k) * (loc ** (n - k)) * r_pmom
+            lhs = (1 - lam) * (lscale**k) * (loc ** (n - k)) * l_pmom
+            rhs = (1 + lam) * (rscale**k) * (loc ** (n - k)) * r_pmom
             moment += comb(n, k) * (lhs + rhs)
 
         return moment
@@ -1083,15 +1084,15 @@ def partial_moment(
             lhs = (
                 (1 - lam)
                 * (loc ** (n - k))
-                * (lscale ** k)
+                * (lscale**k)
                 * StudentsT._ord_t_partial_moment(k, z=(lbound - loc) / lscale, nu=eta)
             )
 
             if z > loc:
                 rhs = (
                     (1 + lam)
                     * (loc ** (n - k))
-                    * (rscale ** k)
+                    * (rscale**k)
                     * (
                         StudentsT._ord_t_partial_moment(k, z=(z - loc) / rscale, nu=eta)
                         - StudentsT._ord_t_partial_moment(k, z=0.0, nu=eta)
@@ -1285,7 +1286,7 @@ def partial_moment(
         parameters = self._check_constraints(parameters)
         nu = parameters[0]
         scale = 1.0 / sqrt(stats.gennorm(nu).var())
-        moment = (scale ** n) * self._ord_gennorm_partial_moment(n, z / scale, nu)
+        moment = (scale**n) * self._ord_gennorm_partial_moment(n, z / scale, nu)
         return moment
 
     @staticmethod

arch/univariate/mean.py

@@ -949,7 +949,7 @@ def forecast(
                 "Due to backcasting and/or data availability start cannot be less "
                 "than the index of the largest value in the right-hand-side "
                 "variables used to fit the first observation.  In this model, "
-                "this value is {}.".format(max(0, earliest - 1))
+                f"this value is {max(0, earliest - 1)}."
             )
         # Parse params
         params = np.asarray(params)

arch/univariate/volatility.py

@@ -508,8 +508,7 @@ def _bootstrap_forecast(
         std_resid = resids / np.sqrt(sigma2)
         if start < self._min_bootstrap_obs:
             raise ValueError(
-                "start must include more than {} "
-                "observations".format(self._min_bootstrap_obs)
+                f"start must include more than {self._min_bootstrap_obs} observations"
             )
         rng = BootstrapRng(std_resid, start, random_state=random_state).rng()
         return self._simulation_forecast(

ci/azure/azure_template_posix.yml

@@ -16,6 +16,8 @@ jobs:
     vmImage: ${{ parameters.vmImage }}
   strategy:
     matrix:
+      python_313:
+        python.version: '3.13'
       python_312:
         python.version: '3.12'
       python_311:

ci/azure/azure_template_windows.yml

@@ -26,6 +26,8 @@ jobs:
         python.version: '3.11'
       python312_win_latest:
         python.version: '3.12'
+      python313_win_latest:
+        python.version: '3.12'
     maxParallel: 10
 
   steps:

requirements-dev.txt

@@ -16,12 +16,12 @@ pytest-xdist
 pytest-cov
 
 # formatting
-black[jupyter]~=24.8.0
-isort~=5.0
+black[jupyter]~=24.10.0
+isort~=5.12
 colorama
 flake8
 mypy
-ruff
+ruff>=0.8.6
 pyupgrade>=3.4.0
 jupyterlab-code-formatter