diff --git a/pymc/distributions/continuous.py b/pymc/distributions/continuous.py
index d3bfe644c4..9d4b770140 100644
--- a/pymc/distributions/continuous.py
+++ b/pymc/distributions/continuous.py
@@ -2427,6 +2427,12 @@ def dist(cls, nu, *args, **kwargs):
         nu = at.as_tensor_variable(floatX(nu))
         return super().dist([nu], *args, **kwargs)
 
+    def get_moment(rv, size, nu):
+        moment = nu
+        if not rv_size_is_none(size):
+            moment = at.full(size, moment)
+        return moment
+
     def logcdf(value, nu):
         """
         Compute the log of the cumulative distribution function for ChiSquared distribution
diff --git a/pymc/tests/test_distributions_moments.py b/pymc/tests/test_distributions_moments.py
index 36fe591e69..a01b7cc469 100644
--- a/pymc/tests/test_distributions_moments.py
+++ b/pymc/tests/test_distributions_moments.py
@@ -7,6 +7,7 @@
 from pymc.distributions import (
     Beta,
     Cauchy,
+    ChiSquared,
     Exponential,
     Gamma,
     HalfCauchy,
@@ -173,6 +174,20 @@ def test_beta_moment(alpha, beta, size, expected):
     assert_moment_is_expected(model, expected)
 
 
+@pytest.mark.parametrize(
+    "nu, size, expected",
+    [
+        (1, None, 1),
+        (1, 5, np.full(5, 1)),
+        (np.arange(1, 6), None, np.arange(1, 6)),
+    ],
+)
+def test_chisquared_moment(nu, size, expected):
+    with Model() as model:
+        ChiSquared("x", nu=nu, size=size)
+    assert_moment_is_expected(model, expected)
+
+
 @pytest.mark.parametrize(
     "lam, size, expected",
     [