Add MAMean (MA(q)) model with volatility & forecasting support

arch/tests/univariate/test_mean.py

@@ -1,5 +1,5 @@
 from arch.compat.pandas import MONTH_END
-
+from arch.univariate.mean import MAMean
 from io import StringIO
 import itertools
 from itertools import product
@@ -104,6 +104,7 @@ def simulated_data(request):
     HARX(SP500, lags=[1, 5]),
     ConstantMean(SP500),
     ZeroMean(SP500),
+    MAMean(SP500,lags=2)
 ]
 
 mean_models = [
@@ -232,6 +233,30 @@ def test_constant_mean(self):
         with pytest.raises(ValueError, match="horizon must be an integer >= 1"):
             res.forecast(horizon=0, start=20)
 
+    def test_ma_mean(self):
+        with np.errstate(over="ignore", invalid="ignore"):
+            ma = MAMean(self.y, lags=2)
+            assert ma.name == "MA"
+            assert_equal(ma.num_params, 3)
+            assert_equal(ma.constant, True)
+            bounds = ma.bounds()
+            assert_equal(len(bounds), 3)
+            params = np.array(
+                [0.5, 0.3, -0.2, 0.1, 0.5, 0.2]
+            )  # mu, theta_1, theta_2 + GARCH
+
+            ma.volatility = GARCH()
+            sim_data = ma.simulate(params, self.T)
+            assert isinstance(sim_data, pd.DataFrame)
+            assert sim_data.shape == (self.T, 3)
+
+            res = ma.fit(disp=DISPLAY)
+            assert isinstance(res.__str__(), str)
+            assert isinstance(ma.__repr__(), str)
+
+            forecasts = res.forecast(horizon=5, start=5)
+            assert isinstance(forecasts, ARCHModelForecast)
+
     def test_zero_mean(self):
         zm = ZeroMean(self.y)
         parameters = np.array([1.0])

arch/univariate/__init__.py

@@ -15,6 +15,7 @@
     ARCHInMean,
     ConstantMean,
     ZeroMean,
+    MAMean,
     arch_model,
 )
 from arch.univariate.volatility import (
@@ -43,6 +44,7 @@
     "ARCHInMean",
     "ARX",
     "ConstantMean",
+    "MAMean",
     "ConstantVariance",
     "Distribution",
     "EGARCH",