diff --git a/chainladder/development/constant.py b/chainladder/development/constant.py
index e223c25b..b423c109 100644
--- a/chainladder/development/constant.py
+++ b/chainladder/development/constant.py
@@ -3,6 +3,7 @@
 # file, You can obtain one at https://mozilla.org/MPL/2.0/.
 from chainladder.development.base import DevelopmentBase
 import pandas as pd
+import numpy as np
 
 
 class DevelopmentConstant(DevelopmentBase):
@@ -61,7 +62,7 @@ def fit(self, X, y=None, sample_weight=None):
         obj = obj.iloc[..., :1, :-1]*0+1
         if callable(self.patterns):
             if self.callable_axis == 0:
-                ldf = obj.index.apply(self.patterns, axis=1)                
+                ldf = obj.index.apply(self.patterns, axis=1)
                 ldf = (
                     pd.concat(ldf.apply(pd.DataFrame, index=[0]).values, axis=0)
                     .fillna(1)[obj.ddims].values)
@@ -75,7 +76,11 @@ def fit(self, X, y=None, sample_weight=None):
             else:
                 raise ValueError('callable axis needs to be 0 or 1')
         else:
-            ldf = xp.array([float(self.patterns[item]) for item in obj.ddims])
+            extra_dims = [x for x in self.patterns.keys() if x > np.max(obj.ddims)]
+            if extra_dims:
+                obj.values = xp.concatenate([obj.values]+[obj.iloc[...,-1:].values]*len(extra_dims), -1)
+                obj.ddims = np.concatenate((obj.ddims, extra_dims), 0,)
+            ldf = xp.array([float(self.patterns.get(item,1)) for item in obj.ddims])
             ldf = ldf[None, None, None, :]
         if self.style == "cdf":
             ldf = xp.concatenate((ldf[..., :-1] / ldf[..., 1:], ldf[..., -1:]), -1)
diff --git a/chainladder/development/tests/test_constant.py b/chainladder/development/tests/test_constant.py
index 1c84fc41..fc541c34 100644
--- a/chainladder/development/tests/test_constant.py
+++ b/chainladder/development/tests/test_constant.py
@@ -55,4 +55,245 @@ def paid_cdfs(x):
     with pytest.raises(ValueError):
         xerror = cl.DevelopmentConstant(patterns=paid_cdfs, callable_axis=2, style='cdf').fit(agway)
     lhs = cl.DevelopmentConstant(patterns=paid_cdfs, callable_axis=1, style='cdf').fit(agway).cdf_
-    assert np.all(abs(lhs.values[0,:,0,:]-patterns.values[:,:-1]) < atol)
\ No newline at end of file
+    assert np.all(abs(lhs.values[0,:,0,:]-patterns.values[:,:-1]) < atol)
+
+def test_constant_pattern_no_tail():
+    reported_patterns = {
+        12: 4.0,
+        24: 2.9,
+        36: 1.8,
+        48: 1.4,
+        60: 1.2,
+        72: 1.1,
+        84: 1.03,
+        96: 1.02,
+        # 108: 1.005,
+    }
+    auto_bi = cl.load_sample("friedland_auto_bi_insurer")
+    reported_BI_claim = cl.DevelopmentConstant(
+        patterns=reported_patterns, style="cdf"
+    ).fit_transform(auto_bi["Reported Claims"])
+
+    assert np.all(
+        np.round(reported_BI_claim.cdf_.to_frame().values.flatten(), 6)
+        == np.array([4.0, 2.9, 1.8, 1.4, 1.2, 1.1, 1.03, 1.02])
+    )
+
+
+def test_constant_pattern_has_tail():
+    reported_patterns = {
+        12: 4.0,
+        24: 2.9,
+        36: 1.8,
+        48: 1.4,
+        60: 1.2,
+        72: 1.1,
+        84: 1.03,
+        96: 1.02,
+        108: 1.005,
+    }
+    auto_bi = cl.load_sample("friedland_auto_bi_insurer")
+    reported_BI_claim = cl.DevelopmentConstant(
+        patterns=reported_patterns, style="cdf"
+    ).fit_transform(auto_bi["Reported Claims"])
+
+    assert np.all(
+        np.round(reported_BI_claim.cdf_.to_frame().values.flatten(), 6)
+        == np.array([4.0, 2.9, 1.8, 1.4, 1.2, 1.1, 1.03, 1.02, 1.005])
+    )
+
+
+def test_constant_pattern_exact_cdf(raa):
+    reported_patterns = {
+        12: 1.1,
+        24: 1.1,
+        36: 1.1,
+        48: 1.1,
+        60: 1.1,
+        72: 1.1,
+        84: 1.1,
+        96: 1.1,
+        108: 1.1,
+        120: 1.1,
+    }
+
+    result = cl.DevelopmentConstant(
+        patterns=reported_patterns, style="cdf"
+    ).fit_transform(raa)
+
+    assert np.all(
+        np.round(result.cdf_.to_frame().values.flatten(), 6)
+        == np.array([1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1])
+    )
+
+
+def test_constant_pattern_exact_ldf(raa):
+    reported_patterns = {
+        12: 1.1,
+        24: 1.1,
+        36: 1.1,
+        48: 1.1,
+        60: 1.1,
+        72: 1.1,
+        84: 1.1,
+        96: 1.1,
+        108: 1.1,
+        120: 1.1,
+    }
+
+    result = cl.DevelopmentConstant(
+        patterns=reported_patterns, style="ldf"
+    ).fit_transform(raa)
+
+    assert np.all(
+        np.round(result.cdf_.to_frame().values.flatten(), 6)
+        == np.array(
+            [
+                2.593742,
+                2.357948,
+                2.143589,
+                1.948717,
+                1.771561,
+                1.61051,
+                1.4641,
+                1.331,
+                1.21,
+                1.1,
+            ]
+        )
+    )
+
+
+def test_constant_pattern_short_cdf(raa):
+    reported_patterns = {
+        12: 1.1,
+        24: 1.1,
+        36: 1.1,
+        48: 1.1,
+        60: 1.1,
+        72: 1.1,
+        # 84: 1.1,
+        # 96: 1.1,
+        # 108: 1.1,
+        # 120: 1.1,
+    }
+
+    result = cl.DevelopmentConstant(
+        patterns=reported_patterns, style="cdf"
+    ).fit_transform(raa)
+
+    assert np.all(
+        np.round(result.cdf_.to_frame().values.flatten(), 6)
+        == np.array([1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.0, 1.0, 1.0])
+    )
+
+
+def test_constant_pattern_short_ldf(raa):
+    reported_patterns = {
+        12: 1.1,
+        24: 1.1,
+        36: 1.1,
+        48: 1.1,
+        60: 1.1,
+        72: 1.1,
+        # 84: 1.1,
+        # 96: 1.1,
+        # 108: 1.1,
+        # 120: 1.1,
+    }
+
+    result = cl.DevelopmentConstant(
+        patterns=reported_patterns, style="ldf"
+    ).fit_transform(raa)
+
+    assert np.all(
+        np.round(result.cdf_.to_frame().values.flatten(), 6)
+        == np.array([1.771561, 1.61051, 1.4641, 1.331, 1.21, 1.1, 1.0, 1.0, 1.0])
+    )
+
+
+def test_constant_pattern_long_cdf(raa):
+    reported_patterns = {
+        12: 1.1,
+        24: 1.1,
+        36: 1.1,
+        48: 1.1,
+        60: 1.1,
+        72: 1.1,
+        84: 1.1,
+        96: 1.1,
+        108: 1.1,
+        120: 1.1,
+        132: 1.1,
+    }
+
+    result = cl.DevelopmentConstant(
+        patterns=reported_patterns, style="cdf"
+    ).fit_transform(raa)
+    assert np.all(
+        np.round(result.cdf_.to_frame().values.flatten(), 6)
+        == np.array([1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1])
+    )
+
+
+def test_constant_pattern_long_ldf(raa):
+    reported_patterns = {
+        12: 1.1,
+        24: 1.1,
+        36: 1.1,
+        48: 1.1,
+        60: 1.1,
+        72: 1.1,
+        84: 1.1,
+        96: 1.1,
+        108: 1.1,
+        120: 1.1,
+        132: 1.1,
+    }
+
+    result = cl.DevelopmentConstant(
+        patterns=reported_patterns, style="ldf"
+    ).fit_transform(raa)
+
+    assert np.all(
+        np.round(result.cdf_.to_frame().values.flatten(), 6)
+        == np.array(
+            [
+                2.853117,
+                2.593742,
+                2.357948,
+                2.143589,
+                1.948717,
+                1.771561,
+                1.61051,
+                1.4641,
+                1.331,
+                1.21,
+                1.1
+            ]
+        )
+    )
+
+
+def test_constant_incr():
+    raa_incr = cl.load_sample("raa").cum_to_incr()
+    reported_patterns = {
+        12: 4.0,
+        24: 2.9,
+        36: 1.8,
+        48: 1.4,
+        60: 1.2,
+        72: 1.1,
+        84: 1.03,
+        96: 1.02,
+        108: 1.005,
+    }
+
+    result = cl.DevelopmentConstant(
+        patterns=reported_patterns, style="cdf"
+    ).fit_transform(raa_incr)
+
+    assert np.all(
+        np.round(result.cdf_.to_frame().values.flatten(), 6)
+        == np.array([4.0, 2.9, 1.8, 1.4, 1.2, 1.1, 1.03, 1.02, 1.005])
+    )
\ No newline at end of file