From 88a118f5a5002d25d8cc2b9decd09082527bebee Mon Sep 17 00:00:00 2001
From: Gavin Chan <gavincyi@gmail.com>
Date: Wed, 15 Mar 2023 15:38:56 +0000
Subject: [PATCH] Rolling window forecast with rolling demean #633

---
 arch/univariate/base.py | 17 +++++++++++++----
 arch/univariate/mean.py |  3 +++
 2 files changed, 16 insertions(+), 4 deletions(-)

diff --git a/arch/univariate/base.py b/arch/univariate/base.py
index 1b8111dbbe..bd5cf4cb5b 100644
--- a/arch/univariate/base.py
+++ b/arch/univariate/base.py
@@ -467,6 +467,7 @@ def fix(
         params: Sequence[float] | ArrayLike1D,
         first_obs: int | DateLike | None = None,
         last_obs: int | DateLike | None = None,
+        demean: bool = False,
     ) -> ARCHModelFixedResult:
         """
         Allows an ARCHModelFixedResult to be constructed from fixed parameters.
@@ -481,6 +482,8 @@ def fix(
             First observation to use when fixing model
         last_obs : {int, str, datetime, Timestamp}
             Last observation to use when fixing model
+        demean: bool
+            Indicate to demean the fitting data y. Default is `False`.
 
         Returns
         -------
@@ -493,7 +496,7 @@ def fix(
         """
         v = self.volatility
 
-        self._adjust_sample(first_obs, last_obs)
+        self._adjust_sample(first_obs, last_obs, demean)
         resids = np.asarray(self.resids(self.starting_values()), dtype=float)
         sigma2 = np.zeros_like(resids)
         backcast = v.backcast(resids)
@@ -536,7 +539,10 @@ def fix(
 
     @abstractmethod
     def _adjust_sample(
-        self, first_obs: int | DateLike | None, last_obs: int | DateLike | None
+        self,
+        first_obs: int | DateLike | None,
+        last_obs: int | DateLike | None,
+        demean: bool,
     ) -> None:
         """
         Performs sample adjustment for estimation
@@ -547,6 +553,8 @@ def _adjust_sample(
             First observation to use when estimating model
         last_obs : {int, str, datetime, datetime64, Timestamp}
             Last observation to use when estimating model
+        demean: bool
+            Perform demean on fitting data y
 
         Notes
         -----
@@ -565,6 +573,7 @@ def fit(
         tol: float | None = None,
         options: dict[str, Any] | None = None,
         backcast: None | float | Float64Array = None,
+        demean: bool = False,
     ) -> ARCHModelResult:
         r"""
         Estimate model parameters
@@ -627,14 +636,14 @@ def fit(
             v.closed_form and d.num_params == 0 and isinstance(v, ConstantVariance)
         )
 
-        self._adjust_sample(first_obs, last_obs)
+        self._adjust_sample(first_obs, last_obs, demean)
 
         resids = np.asarray(self.resids(self.starting_values()), dtype=float)
         self._check_scale(resids)
         if self.scale != 1.0:
             # Scale changed, rescale data and reset model
             self._y = cast(np.ndarray, self.scale * np.asarray(self._y_original))
-            self._adjust_sample(first_obs, last_obs)
+            self._adjust_sample(first_obs, last_obs, demean)
             resids = np.asarray(self.resids(self.starting_values()), dtype=float)
 
         if backcast is None:
diff --git a/arch/univariate/mean.py b/arch/univariate/mean.py
index e36ba944e8..082227a5e2 100644
--- a/arch/univariate/mean.py
+++ b/arch/univariate/mean.py
@@ -672,6 +672,7 @@ def _adjust_sample(
         self,
         first_obs: None | int | DateLike,
         last_obs: None | int | DateLike,
+        demean: bool,
     ) -> None:
         index = self._y_series.index
         _first_obs_index = cutoff_to_index(first_obs, index, 0)
@@ -681,6 +682,8 @@ def _adjust_sample(
             raise ValueError("first_obs and last_obs produce in an empty array.")
         self._fit_indices = [_first_obs_index, _last_obs_index]
         self._fit_y = self._y[_first_obs_index:_last_obs_index]
+        if demean:
+            self._fit_y = np.mean(self._fit_y)
         reg = self.regressors
         self._fit_regressors = reg[_first_obs_index:_last_obs_index]
         self.volatility.start, self.volatility.stop = self._fit_indices