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<h1 id="generated-statsmodels-tsa-ar-model-autoregresults-wald-test-terms--page-root">statsmodels.tsa.ar_model.AutoRegResults.wald_test_terms<a class="headerlink" href="#generated-statsmodels-tsa-ar-model-autoregresults-wald-test-terms--page-root" title="Permalink to this headline">¶</a></h1>
<dl class="py method">
<dt class="sig sig-object py" id="statsmodels.tsa.ar_model.AutoRegResults.wald_test_terms">
<span class="sig-prename descclassname"><span class="pre">AutoRegResults.</span></span><span class="sig-name descname"><span class="pre">wald_test_terms</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">skip_single</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_constraints</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">combine_terms</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#statsmodels.tsa.ar_model.AutoRegResults.wald_test_terms" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute a sequence of Wald tests for terms over multiple columns.</p>
<p>This computes joined Wald tests for the hypothesis that all
coefficients corresponding to a <cite>term</cite> are zero.
<cite>Terms</cite> are defined by the underlying formula or by string matching.</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>skip_single</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.9)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If true, then terms that consist only of a single column and,
therefore, refers only to a single parameter is skipped.
If false, then all terms are included.</p>
</dd>
<dt><strong>extra_constraints</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.21)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray</span></code></a></span></dt><dd><p>Additional constraints to test. Note that this input has not been
tested.</p>
</dd>
<dt><strong>combine_terms</strong><span class="classifier">{<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.9)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>], <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.9)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a>}</span></dt><dd><p>Each string in this list is matched to the name of the terms or
the name of the exogenous variables. All columns whose name
includes that string are combined in one joint test.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">WaldTestResults</span></code></dt><dd><p>The result instance contains <cite>table</cite> which is a pandas DataFrame
with the test results: test statistic, degrees of freedom and
pvalues.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">res_ols</span> <span class="o">=</span> <span class="n">ols</span><span class="p">(</span><span class="s2">"np.log(Days+1) ~ C(Duration, Sum)*C(Weight, Sum)"</span><span class="p">,</span> <span class="n">data</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">res_ols</span><span class="o">.</span><span class="n">wald_test_terms</span><span class="p">()</span>
<span class="go">&lt;class 'statsmodels.stats.contrast.WaldTestResults'&gt;</span>
<span class="go">                                          F                P&gt;F  df constraint  df denom</span>
<span class="go">Intercept                        279.754525  2.37985521351e-22              1        51</span>
<span class="go">C(Duration, Sum)                   5.367071    0.0245738436636              1        51</span>
<span class="go">C(Weight, Sum)                    12.432445  3.99943118767e-05              2        51</span>
<span class="go">C(Duration, Sum):C(Weight, Sum)    0.176002      0.83912310946              2        51</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">res_poi</span> <span class="o">=</span> <span class="n">Poisson</span><span class="o">.</span><span class="n">from_formula</span><span class="p">(</span><span class="s2">"Days ~ C(Weight) * C(Duration)"</span><span class="p">,</span>                                            <span class="n">data</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">cov_type</span><span class="o">=</span><span class="s1">'HC0'</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">wt</span> <span class="o">=</span> <span class="n">res_poi</span><span class="o">.</span><span class="n">wald_test_terms</span><span class="p">(</span><span class="n">skip_single</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>                                          <span class="n">combine_terms</span><span class="o">=</span><span class="p">[</span><span class="s1">'Duration'</span><span class="p">,</span> <span class="s1">'Weight'</span><span class="p">])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">wt</span><span class="p">)</span>
<span class="go">                            chi2             P&gt;chi2  df constraint</span>
<span class="go">Intercept              15.695625  7.43960374424e-05              1</span>
<span class="go">C(Weight)              16.132616  0.000313940174705              2</span>
<span class="go">C(Duration)             1.009147     0.315107378931              1</span>
<span class="go">C(Weight):C(Duration)   0.216694     0.897315972824              2</span>
<span class="go">Duration               11.187849     0.010752286833              3</span>
<span class="go">Weight                 30.263368  4.32586407145e-06              4</span>
</pre></div>
</div>
</dd></dl>



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