some new features

.venv/lib/python3.12/site-packages/statsmodels/tsa/mlemodel.py

@@ -0,0 +1,86 @@
+"""Base Classes for Likelihood Models in time series analysis
+
+Warning: imports numdifftools
+
+
+
+Created on Sun Oct 10 15:00:47 2010
+
+Author: josef-pktd
+License: BSD
+
+"""
+
+
+try:
+    import numdifftools as ndt
+except ImportError:
+    pass
+
+from statsmodels.base.model import LikelihoodModel
+
+
+#copied from sandbox/regression/mle.py
+#TODO: I take it this is only a stub and should be included in another
+# model class?
+class TSMLEModel(LikelihoodModel):
+    """
+    univariate time series model for estimation with maximum likelihood
+
+    Note: This is not working yet
+    """
+
+    def __init__(self, endog, exog=None):
+        #need to override p,q (nar,nma) correctly
+        super().__init__(endog, exog)
+        #set default arma(1,1)
+        self.nar = 1
+        self.nma = 1
+        #self.initialize()
+
+    def geterrors(self, params):
+        raise NotImplementedError
+
+    def loglike(self, params):
+        """
+        Loglikelihood for timeseries model
+
+        Parameters
+        ----------
+        params : array_like
+            The model parameters
+
+        Notes
+        -----
+        needs to be overwritten by subclass
+        """
+        raise NotImplementedError
+
+    def score(self, params):
+        """
+        Score vector for Arma model
+        """
+        #return None
+        #print params
+        jac = ndt.Jacobian(self.loglike, stepMax=1e-4)
+        return jac(params)[-1]
+
+    def hessian(self, params):
+        """
+        Hessian of arma model.  Currently uses numdifftools
+        """
+        #return None
+        Hfun = ndt.Jacobian(self.score, stepMax=1e-4)
+        return Hfun(params)[-1]
+
+    def fit(self, start_params=None, maxiter=5000, method='fmin', tol=1e-08):
+        '''estimate model by minimizing negative loglikelihood
+
+        does this need to be overwritten ?
+        '''
+        if start_params is None and hasattr(self, '_start_params'):
+            start_params = self._start_params
+        #start_params = np.concatenate((0.05*np.ones(self.nar + self.nma), [1]))
+        mlefit = super().fit(start_params=start_params,
+                maxiter=maxiter, method=method, tol=tol)
+        return mlefit