Merge pull request statsmodels#5178 from bashtage/fix-arima-predict-test

TST: Fix scale in test

Author: josef-pkt

statsmodels/tsa/tests/test_arima.py

@@ -2106,57 +2106,49 @@ def test_arima_diff2():
 
 def test_arima111_predict_exog_2127():
     # regression test for issue #2127
-    ef =  [ 0.03005,  0.03917,  0.02828,  0.03644,  0.03379,  0.02744,
-            0.03343,  0.02621,  0.0305 ,  0.02455,  0.03261,  0.03507,
-            0.02734,  0.05373,  0.02677,  0.03443,  0.03331,  0.02741,
-            0.03709,  0.02113,  0.03343,  0.02011,  0.03675,  0.03077,
-            0.02201,  0.04844,  0.05518,  0.03765,  0.05433,  0.03049,
-            0.04829,  0.02936,  0.04421,  0.02457,  0.04007,  0.03009,
-            0.04504,  0.05041,  0.03651,  0.02719,  0.04383,  0.02887,
-            0.0344 ,  0.03348,  0.02364,  0.03496,  0.02549,  0.03284,
-            0.03523,  0.02579,  0.0308 ,  0.01784,  0.03237,  0.02078,
-            0.03508,  0.03062,  0.02006,  0.02341,  0.02223,  0.03145,
-            0.03081,  0.0252 ,  0.02683,  0.0172 ,  0.02225,  0.01579,
-            0.02237,  0.02295,  0.0183 ,  0.02356,  0.02051,  0.02932,
-            0.03025,  0.0239 ,  0.02635,  0.01863,  0.02994,  0.01762,
-            0.02837,  0.02421,  0.01951,  0.02149,  0.02079,  0.02528,
-            0.02575,  0.01634,  0.02563,  0.01719,  0.02915,  0.01724,
-            0.02804,  0.0275 ,  0.02099,  0.02522,  0.02422,  0.03254,
-            0.02095,  0.03241,  0.01867,  0.03998,  0.02212,  0.03034,
-            0.03419,  0.01866,  0.02623,  0.02052]
-    ue =  [  4.9,   5. ,   5. ,   5. ,   4.9,   4.7,   4.8,   4.7,   4.7,
-             4.6,   4.6,   4.7,   4.7,   4.5,   4.4,   4.5,   4.4,   4.6,
-             4.5,   4.4,   4.5,   4.4,   4.6,   4.7,   4.6,   4.7,   4.7,
-             4.7,   5. ,   5. ,   4.9,   5.1,   5. ,   5.4,   5.6,   5.8,
-             6.1,   6.1,   6.5,   6.8,   7.3,   7.8,   8.3,   8.7,   9. ,
-             9.4,   9.5,   9.5,   9.6,   9.8,  10. ,   9.9,   9.9,   9.7,
-             9.8,   9.9,   9.9,   9.6,   9.4,   9.5,   9.5,   9.5,   9.5,
-             9.8,   9.4,   9.1,   9. ,   9. ,   9.1,   9. ,   9.1,   9. ,
-             9. ,   9. ,   8.8,   8.6,   8.5,   8.2,   8.3,   8.2,   8.2,
-             8.2,   8.2,   8.2,   8.1,   7.8,   7.8,   7.8,   7.9,   7.9,
-             7.7,   7.5,   7.5,   7.5,   7.5,   7.3,   7.2,   7.2,   7.2,
-             7. ,   6.7,   6.6,   6.7,   6.7,   6.3,   6.3]
-
-    # rescaling results in convergence failure
-    #model = sm.tsa.ARIMA(np.array(ef)*100, (1,1,1), exog=ue)
-    model = ARIMA(ef, (1,1,1), exog=ue)
+    ef = [0.03005, 0.03917, 0.02828, 0.03644, 0.03379, 0.02744,
+          0.03343, 0.02621, 0.03050, 0.02455, 0.03261, 0.03507,
+          0.02734, 0.05373, 0.02677, 0.03443, 0.03331, 0.02741,
+          0.03709, 0.02113, 0.03343, 0.02011, 0.03675, 0.03077,
+          0.02201, 0.04844, 0.05518, 0.03765, 0.05433, 0.03049,
+          0.04829, 0.02936, 0.04421, 0.02457, 0.04007, 0.03009,
+          0.04504, 0.05041, 0.03651, 0.02719, 0.04383, 0.02887,
+          0.03440, 0.03348, 0.02364, 0.03496, 0.02549, 0.03284,
+          0.03523, 0.02579, 0.03080, 0.01784, 0.03237, 0.02078,
+          0.03508, 0.03062, 0.02006, 0.02341, 0.02223, 0.03145,
+          0.03081, 0.02520, 0.02683, 0.01720, 0.02225, 0.01579,
+          0.02237, 0.02295, 0.01830, 0.02356, 0.02051, 0.02932,
+          0.03025, 0.02390, 0.02635, 0.01863, 0.02994, 0.01762,
+          0.02837, 0.02421, 0.01951, 0.02149, 0.02079, 0.02528,
+          0.02575, 0.01634, 0.02563, 0.01719, 0.02915, 0.01724,
+          0.02804, 0.02750, 0.02099, 0.02522, 0.02422, 0.03254,
+          0.02095, 0.03241, 0.01867, 0.03998, 0.02212, 0.03034,
+          0.03419, 0.01866, 0.02623, 0.02052]
+    ue = [4.9, 5.0, 5.0, 5.0, 4.9, 4.7, 4.8, 4.7, 4.7,
+          4.6, 4.6, 4.7, 4.7, 4.5, 4.4, 4.5, 4.4, 4.6,
+          4.5, 4.4, 4.5, 4.4, 4.6, 4.7, 4.6, 4.7, 4.7,
+          4.7, 5.0, 5.0, 4.9, 5.1, 5.0, 5.4, 5.6, 5.8,
+          6.1, 6.1, 6.5, 6.8, 7.3, 7.8, 8.3, 8.7, 9.0,
+          9.4, 9.5, 9.5, 9.6, 9.8, 10., 9.9, 9.9, 9.7,
+          9.8, 9.9, 9.9, 9.6, 9.4, 9.5, 9.5, 9.5, 9.5,
+          9.8, 9.4, 9.1, 9.0, 9.0, 9.1, 9.0, 9.1, 9.0,
+          9.0, 9.0, 8.8, 8.6, 8.5, 8.2, 8.3, 8.2, 8.2,
+          8.2, 8.2, 8.2, 8.1, 7.8, 7.8, 7.8, 7.9, 7.9,
+          7.7, 7.5, 7.5, 7.5, 7.5, 7.3, 7.2, 7.2, 7.2,
+          7.0, 6.7, 6.6, 6.7, 6.7, 6.3, 6.3]
+
+    ue = np.array(ue) / 100
+    model = ARIMA(ef, (1, 1, 1), exog=ue)
     res = model.fit(transparams=False, pgtol=1e-8, iprint=0, disp=0)
-    assert_equal(res.mle_retvals['warnflag'],  0)
-    predicts = res.predict(start=len(ef), end = len(ef)+10,
-                           exog=ue[-11:], typ = 'levels')
+    assert_equal(res.mle_retvals['warnflag'], 0)
+    predicts = res.predict(start=len(ef), end=len(ef) + 10,
+                           exog=ue[-11:], typ='levels')
 
     # regression test, not verified numbers
-    # if exog=ue in predict, which values are used ?
-    predicts_res = np.array(
-          [ 0.02612291,  0.02361929,  0.024966  ,  0.02448193,  0.0248772 ,
-            0.0248762 ,  0.02506319,  0.02516542,  0.02531214,  0.02544654,
-            0.02559099,  0.02550931])
-
-    # if exog=ue[-11:] in predict
-    predicts_res = np.array(
-          [ 0.02591112,  0.02321336,  0.02436593,  0.02368773,  0.02389767,
-            0.02372018,  0.02374833,  0.02367407,  0.0236443 ,  0.02362868,
-            0.02362312])
+    predicts_res = np.array([
+        0.02591095, 0.02321325, 0.02436579, 0.02368759, 0.02389753,
+        0.02372, 0.0237481, 0.0236738, 0.023644, 0.0236283,
+        0.02362267])
 
     assert_allclose(predicts, predicts_res, atol=5e-6)