Merge branch 'master' of https://github.com/RichardMM/arch into cgarch

.coveragerc

@@ -6,7 +6,7 @@ include = */arch/*
 omit =
     */_version.py
     */compat/*
-
+plugins = Cython.Coverage
 
 [report]
 # Regexes for lines to exclude from consideration
@@ -26,3 +26,4 @@ omit =
     *recursions.py
     *samplers.py
 ignore_errors = True
+

.travis.yml

@@ -28,15 +28,15 @@ matrix:
     - PYTHON=2.7
     - NUMBA=0.24
     - NUMPY=1.10
-    - SCIPY=0.16
+    - SCIPY=0.17
     - MATPLOTLIB=1.5
-    - PANDAS=0.16
+    - PANDAS=0.18
     - STATSMODELS_MASTER=true
   - python: 2.7
     env:
     - PYTHON=3.4
     - NUMPY=1.11
-    - SCIPY=0.17
+    - SCIPY=0.18
     - NUMBA=0.27
     - MATPLOTLIB=1.5
     - PANDAS=0.18
@@ -84,17 +84,18 @@ before_install:
   - conda list
   - export PYTHONHASHSEED=0
   - export MKL_NUM_THREADS=1
+  - export ARCH_CYTHON_COVERAGE=${COVERAGE}
 
 install:
-  - python setup.py install
+  - python setup.py develop
 
 script:
   - set -e
-  - flake8 arch
-  - pytest -n 2 --cov-config .coveragerc --cov=arch arch --durations=10
+  - pytest --cov-config .coveragerc --cov=arch arch --durations=10
   - python ci/performance.py
-  - if [[ ${DOCBUILD} = true ]]; then cd doc && make html && cd .. ; fi
+  - if [[ ${DOCBUILD} = true ]]; then python setup.py install && cd doc && make html && cd .. ; fi
   - if [[ ${DOCBUILD} = true && ${TRAVIS_BRANCH} = "master" ]]; then doctr deploy doc; fi
+  - flake8 arch
 
 after_success:
   - if [[ ${COVERAGE} = true ]]; then coveralls --rcfile=${SRCDIR}/.coveragerc; fi

arch/bootstrap/base.py

@@ -577,7 +577,7 @@ def apply(self, func, reps=1000, extra_kwargs=None):
         base = func(*self._args, **kwargs)
         try:
             num_params = base.shape[0]
-        except:
+        except (IndexError, AttributeError):
             num_params = 1
         results = np.zeros((reps, num_params))
         count = 0

arch/tests/univariate/test_mean.py

@@ -22,8 +22,10 @@
 from arch.univariate.volatility import ConstantVariance, GARCH, HARCH, ARCH, \
     RiskMetrics2006, EWMAVariance, EGARCH, FixedVariance
 from arch.univariate.distribution import Normal, StudentsT
+
 try:
     import matplotlib.pyplot  # noqa
+
     HAS_MATPLOTLIB = True
 except ImportError:
     HAS_MATPLOTLIB = False
@@ -715,7 +717,7 @@ def test_fixed_user_parameters_new_model(self):
         am = arch_model(self.y_series)
         res = am.fit(disp=DISPLAY)
         new_am = arch_model(self.y_series)
-        fixed_res = new_am .fix(res.params)
+        fixed_res = new_am.fix(res.params)
         assert_series_equal(res.conditional_volatility,
                             fixed_res.conditional_volatility)
         assert_series_equal(res.params, fixed_res.params)
@@ -728,7 +730,7 @@ def test_fixed_user_parameters_new_model(self):
         am = arch_model(self.y_series)
         res = am.fit(disp=DISPLAY, first_obs=100, last_obs=900)
         new_am = arch_model(self.y_series)
-        fixed_res = new_am .fix(res.params, first_obs=100, last_obs=900)
+        fixed_res = new_am.fix(res.params, first_obs=100, last_obs=900)
         assert_series_equal(res.params, fixed_res.params)
         assert_equal(res.aic, fixed_res.aic)
         assert_equal(res.bic, fixed_res.bic)
@@ -868,3 +870,16 @@ def test_constant_mean_fixed_variance(self):
         res = mod.fit()
         assert len(res.params) == 4
         assert 'scale' not in res.params.index
+
+    def test_optimization_options(self):
+        am = arch_model(None)
+        data = am.simulate([0.0, 0.1, 0.1, 0.85], 2500)
+        am = arch_model(data.data)
+        std = am.fit(disp='off')
+        loose = am.fit(tol=1e-2, disp='off')
+        assert std.loglikelihood != loose.loglikelihood
+        with warnings.catch_warnings(record=True) as w:
+            short = am.fit(options={'maxiter': 3}, disp='off')
+        assert len(w) == 1
+        assert std.loglikelihood != short.loglikelihood
+        assert short.convergence_flag != 0

arch/tests/univariate/test_volatility.py

@@ -7,7 +7,7 @@
 
 try:
     from arch.univariate import _recursions as rec
-except:
+except ImportError:
     from arch.univariate import recursions_python as rec
 from arch.univariate.volatility import GARCH, ARCH, HARCH, ConstantVariance, \
     EWMAVariance, RiskMetrics2006, EGARCH, FixedVariance, CGARCH

arch/unitroot/unitroot.py

@@ -45,9 +45,9 @@ def _autolag_ols(endog, exog, startlag, maxlag, method, modargs=(), regresults=F
 
     Parameters
     ----------
-    endog : array-like
+    endog : {ndarray, Series}
         nobs array containing endogenous variable
-    exog : array-like
+    exog : {ndarray, DataFrame}
         nobs by (startlag + maxlag) array containing lags and possibly other
         variables
     startlag : int
@@ -123,7 +123,7 @@ def _df_select_lags(y, trend, max_lags, method):
 
     Parameters
     ----------
-    y : array
+    y : ndarray
         The data for the lag selection exercise
     trend : {'nc','c','ct','ctt'}
         The trend order
@@ -172,7 +172,7 @@ def _estimate_df_regression(y, trend, lags):
 
     Parameters
     ----------
-    y : array
+    y : ndarray
         The data for the lag selection
     trend : {'nc','c','ct','ctt'}
         The trend order
@@ -384,7 +384,7 @@ class ADF(UnitRootTest):
 
     Parameters
     ----------
-    y : array
+    y : {ndarray, Series}
         The data to test for a unit root
     lags : int, optional
         The number of lags to use in the ADF regression.  If omitted or None,
@@ -529,7 +529,7 @@ class DFGLS(UnitRootTest):
 
     Parameters
     ----------
-    y : array
+    y : {ndarray, Series}
         The data to test for a unit root
     lags : int, optional
         The number of lags to use in the ADF regression.  If omitted or None,
@@ -692,7 +692,7 @@ class PhillipsPerron(UnitRootTest):
 
     Parameters
     ----------
-    y : array
+    y : {ndarray, Series}
         The data to test for a unit root
     lags : int, optional
         The number of lags to use in the Newey-West estimator of the long-run
@@ -870,7 +870,7 @@ class KPSS(UnitRootTest):
 
     Parameters
     ----------
-    y : array
+    y : {ndarray, Series}
         The data to test for stationarity
     lags : int, optional
         The number of lags to use in the Newey-West estimator of the long-run
@@ -963,7 +963,7 @@ class VarianceRatio(UnitRootTest):
 
     Parameters
     ----------
-    y : array
+    y : {ndarray, Series}
         The data to test for a random walk
     lags : int
         The number of periods to used in the multi-period variance, which is
@@ -1252,7 +1252,7 @@ def mackinnoncrit(num_unit_roots=1, regression="c", nobs=inf,
 
     Returns
     -------
-    crit_vals : array
+    crit_vals : ndarray
         Three critical values corresponding to 1%, 5% and 10% cut-offs.
 
     Notes
@@ -1311,7 +1311,7 @@ def kpss_crit(stat, trend='c'):
     -------
     pvalue : float
         The interpolated p-value
-    crit_val : array
+    crit_val : ndarray
         Three element array containing the 10%, 5% and 1% critical values,
         in order