Merge pull request #169 from bashtage/fix-small-issues

Fix small issues

Author: bashtage

doc/source/conf.py

@@ -207,16 +207,16 @@
 
 # Example configuration for intersphinx: refer to the Python standard library.
 intersphinx_mapping = {
-    'statsmodels': ('http://www.statsmodels.org/dev/', None),
-    'matplotlib': ('http://matplotlib.org/', None),
+    'statsmodels': ('https://www.statsmodels.org/dev/', None),
+    'matplotlib': ('https://matplotlib.org/', None),
     'scipy': ('https://docs.scipy.org/doc/scipy/reference/', None),
     'python': ('https://docs.python.org/3', None),
     'numpy': ('https://docs.scipy.org/doc/numpy', None),
-    'pandas': ('http://pandas.pydata.org/pandas-docs/stable/', None),
-    'xarray': ('http://xarray.pydata.org/en/stable/', None)
+    'pandas': ('https://pandas.pydata.org/pandas-docs/stable/', None),
+    'xarray': ('https://xarray.pydata.org/en/stable/', None)
 }
 
-extlinks = {'issue': ('https://github.com/bashtage/arch/issues/%s', 'GH')}
+extlinks = {'issue': ('https://github.com/bashtage/linearmodels/issues/%s', 'GH')}
 
 
 doctest_global_setup = """

doc/source/iv/index.rst

@@ -8,10 +8,9 @@ specification tests. Other components of the models such as covariance
 estimators, GMM weighting estimators and the results are presented in
 :ref:`iv-module-reference`.
 
-:ref:`Basic Examples <iv/examples/basic-examples.ipynb>` shows basic usage
-through examples from Wooldridge's introductory text book and
-:ref:`Advanced Examples <iv/examples/advanced-examples.ipynb>` shows more
-advanced examples following the examples in Cameron and Trivedi's Stata
+`Basic Examples`_ shows basic usage through examples from Wooldridge's
+introductory text book and `Advanced Examples`_
+shows more advanced examples following the examples in Cameron and Trivedi's Stata
 introduction.
 
 :ref:`iv-mathematical-notation` contains a concise explanation of the formulas
@@ -28,3 +27,6 @@ tests.
    examples/using-formulas.ipynb
    reference
    mathematical-formula
+
+.. _Basic Examples: iv/examples/basic-examples.ipynb
+.. _Advanced Examples: iv/examples/advanced-examples.ipynb

doc/source/panel/index.rst

@@ -7,14 +7,11 @@ reference to available estimation methods.
 after estimating a model as well as model specification tests. The covariance
 estimators are presented in :ref:`panel-module-reference-covariance`.
 
-:ref:`Basic Examples <panel/examples/examples.ipynb>` shows basic usage
-of the models using examples from Wooldridge's text books.
-:ref:`Using Formulas <panel/examples/using-formulas.ipynb>` shows how models
-can be specified using R-like formulas using patsy.
-:ref:`Data Formats <panel/examples/data-formats.ipynb>` described the
+`Basic Examples`_ shows basic usage of the models using examples from
+Wooldridge's text books. `Using Formulas`_ shows how models can be specified
+using R-like formulas using patsy. `Data Formats`_ described the
 alternative formats that can be used when specifying models.
 
-
 :ref:`panel-mathematical-notation` contains a concise explanation of the formulas
 used in estimating parameters, estimating covariances and conducting
 hypothesis tests.
@@ -36,4 +33,9 @@ until release 0.20.
    pandas
    reference
    mathematical-formula
-   faq
+   faq
+
+
+.. _Basic Examples: panel/examples/examples.ipynb
+.. _Using Formulas: panel/examples/using-formulas.ipynb
+.. _Data Formats: panel/examples/data-formats.ipynb

examples/iv_advanced-examples.ipynb

@@ -25,9 +25,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from linearmodels import IV2SLS, IVLIML, IVGMM, IVGMMCUE"
@@ -113,9 +111,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from statsmodels.api import OLS, add_constant\n",
@@ -176,9 +172,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "ivmod = IV2SLS(data.ldrugexp, data[controls], data.hi_empunion, data[['ssiratio','multlc']])\n",
@@ -207,9 +201,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "ivmod = IV2SLS(data.ldrugexp, data[controls], data.hi_empunion, data[['ssiratio','multlc']])\n",
@@ -230,9 +222,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "ivmod =IVGMM(data.ldrugexp, data[controls], data.hi_empunion, data[['ssiratio','multlc']])\n",
@@ -250,9 +240,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "ivmod = IVGMM(data.ldrugexp, data[controls], data.hi_empunion, data[['ssiratio','multlc']], \n",
@@ -560,7 +548,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.3"
+   "version": "3.7.0"
   },
   "nbsphinx": {
    "allow_errors": true

examples/iv_basic-examples.ipynb

@@ -318,9 +318,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "dep = ['wage']\n",
@@ -406,7 +404,7 @@
    "source": [
     "### Formula interface\n",
     "\n",
-    "This model was large and so it might be simpler to use a formula. While formulas are discussed in [detail in another notebook](iv/examples/using-formulas.ipynb), they use the formula language of patsy with an augmentation to specify the endogenous and instrumental variables. The generic form is\n",
+    "This model was large and so it might be simpler to use a formula. While formulas are discussed in [detail in another notebook](using-formulas.ipynb), they use the formula language of patsy with an augmentation to specify the endogenous and instrumental variables. The generic form is\n",
     "\n",
     "```\n",
     "dependent ~ exog + [endog ~ instr]\n",
@@ -467,9 +465,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "data = mroz.load()\n",
@@ -562,9 +558,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "u = res.resids\n",
@@ -597,9 +591,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "instr = ['fatheduc','motheduc','huseduc']\n",
@@ -746,7 +738,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.3"
+   "version": "3.7.0"
   }
  },
  "nbformat": 4,

examples/system_examples.ipynb

@@ -58,9 +58,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from linearmodels.datasets import munnell\n",
@@ -100,9 +98,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "grouped = data.groupby(['REGION','YR'])\n",
@@ -133,9 +129,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from collections import OrderedDict\n",
@@ -159,9 +153,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
@@ -700,7 +692,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.3"
+   "version": "3.7.0"
   }
  },
  "nbformat": 4,

linearmodels/asset_pricing/model.py

@@ -708,8 +708,8 @@ def fit(self, center=True, use_cue=False, steps=2, disp=10, max_iter=1000,
         # 1. Starting Values - use 2 pass
         mod = LinearFactorModel(self.portfolios, self.factors, risk_free=self._risk_free)
         res = mod.fit()
-        betas = res.betas.values.ravel()
-        lam = res.risk_premia.values
+        betas = np.asarray(res.betas).ravel()
+        lam = np.asarray(res.risk_premia)
         mu = self.factors.ndarray.mean(0)
         sv = np.r_[betas, lam, mu][:, None]
         g = self._moments(sv, excess_returns)

linearmodels/asset_pricing/results.py

@@ -89,9 +89,9 @@ def summary(self):
         table.extend_right(SimpleTable(vals, stubs=stubs))
         smry.tables.append(table)
 
-        rp = self.risk_premia.values[:, None]
-        se = self.risk_premia_se.values[:, None]
-        tstats = (self.risk_premia / self.risk_premia_se).values
+        rp = np.asarray(self.risk_premia)[:, None]
+        se = np.asarray(self.risk_premia_se)[:, None]
+        tstats = np.asarray(self.risk_premia / self.risk_premia_se)
         pvalues = 2 - 2 * stats.norm.cdf(np.abs(tstats))
         ci = rp + se * stats.norm.ppf([[0.025, 0.975]])
         param_data = np.c_[rp,
@@ -160,8 +160,8 @@ def full_summary(self):
         first = True
         for row in params.index:
             smry.tables.append(SimpleTable(['']))
-            smry.tables.append(self._single_table(params.loc[row].values[:, None],
-                                                  se.loc[row].values[:, None],
+            smry.tables.append(self._single_table(np.asarray(params.loc[row])[:, None],
+                                                  np.asarray(se.loc[row])[:, None],
                                                   row, param_names, first))
             first = False
 

linearmodels/iv/data.py

@@ -101,7 +101,7 @@ def __init__(self, x, var_name='x', nobs=None, convert_dummies=True, drop_first=
                 x = expand_categoricals(x, drop_first)
 
             self._pandas = x
-            self._ndarray = self._pandas.values
+            self._ndarray = np.asarray(self._pandas)
             if all_numeric or convert_dummies:
                 self._ndarray = self._ndarray.astype(np.float64)
             self._labels = {i: list(label) for i, label in zip(range(x.ndim), x.axes)}

linearmodels/iv/model.py

@@ -278,7 +278,7 @@ def predict(self, params, *, exog=None, endog=None, data=None, eval_env=4):
             exog = parser.exog
             endog = parser.endog
         exog_endog = concat([exog, endog], 1)
-        x = exog_endog.values
+        x = asarray(exog_endog)
         params = atleast_2d(asarray(params))
         if params.shape[0] == 1:
             params = params.T
@@ -500,7 +500,7 @@ def _post_estimation(self, params, cov_estimator, cov_type):
         index = self._index
         eps = self.resids(params)
         y = self.dependent.pandas
-        fitted = DataFrame(y.values - eps, y.index, ['fitted_values'])
+        fitted = DataFrame(asarray(y) - eps, y.index, ['fitted_values'])
         weps = self.wresids(params)
         cov = cov_estimator.cov
         debiased = cov_estimator.debiased
@@ -824,7 +824,7 @@ def fit(self, *, iter_limit=2, tol=1e-4, initial_weight=None,
         wmat = inv(wz.T @ wz / nobs) if initial_weight is None else initial_weight
         sv = IV2SLS(self.dependent, self.exog, self.endog, self.instruments,
                     weights=self.weights)
-        _params = params = sv.fit().params.values[:, None]
+        _params = params = asarray(sv.fit().params)[:, None]
         # _params = params = self.estimate_parameters(wx, wy, wz, wmat)
 
         iters, norm = 1, 10 * tol + 1
@@ -1117,7 +1117,7 @@ def fit(self, *, starting=None, display=False, cov_type='robust', opt_options=No
             res = IVGMM(self.dependent, exog, endog, instr,
                         weights=self.weights, weight_type=self._weight_type,
                         **self._weight_config).fit()
-            starting = res.params.values
+            starting = asarray(res.params)
         else:
             starting = asarray(starting)
             if len(starting) != self.exog.shape[1] + self.endog.shape[1]: