scikit-learn
diff --git a/‎doc/whats_new/v0.24.rst
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diff --git a/‎sklearn/multioutput.py
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diff --git a/‎sklearn/tests/test_multioutput.py
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@@ -48,6 +48,13 @@ Changelog
   `'use_encoded_value'` strategies.
   :pr:`19234` by `Guillaume Lemaitre <glemaitre>`.
 
+:mod:`sklearn.multioutput`
+..........................
+
+- |Fix| :class:`multioutput.MultiOutputRegressor` now works with estimators
+  that dynamically define `predict` during fitting, such as
+  :class:`ensemble.StackingRegressor`. :pr:`19308` by `Thomas Fan`_.
+
 :mod:`sklearn.semi_supervised`
 ..............................
 
 
@@ -198,7 +198,7 @@ def predict(self, X):
             Note: Separate models are generated for each predictor.
         """
         check_is_fitted(self)
-        if not hasattr(self.estimator, "predict"):
+        if not hasattr(self.estimators_[0], "predict"):
             raise ValueError("The base estimator should implement"
                              " a predict method")
 
 
@@ -10,6 +10,7 @@
 from sklearn import datasets
 from sklearn.base import clone
 from sklearn.datasets import make_classification
+from sklearn.datasets import load_linnerud
 from sklearn.ensemble import GradientBoostingRegressor, RandomForestClassifier
 from sklearn.exceptions import NotFittedError
 from sklearn.linear_model import Lasso
@@ -30,6 +31,7 @@
 from sklearn.dummy import DummyRegressor, DummyClassifier
 from sklearn.pipeline import make_pipeline
 from sklearn.impute import SimpleImputer
+from sklearn.ensemble import StackingRegressor
 
 
 def test_multi_target_regression():
@@ -658,3 +660,19 @@ def test_classifier_chain_tuple_invalid_order():
 
     with pytest.raises(ValueError, match='invalid order'):
         chain.fit(X, y)
+
+
+def test_multioutputregressor_ducktypes_fitted_estimator():
+    """Test that MultiOutputRegressor checks the fitted estimator for
+    predict. Non-regression test for #16549."""
+    X, y = load_linnerud(return_X_y=True)
+    stacker = StackingRegressor(
+        estimators=[("sgd", SGDRegressor(random_state=1))],
+        final_estimator=Ridge(),
+        cv=2
+    )
+
+    reg = MultiOutputRegressor(estimator=stacker).fit(X, y)
+
+    # Does not raise
+    reg.predict(X)