def test_bking1d():
# Test Baxter King band-pass filter. Results are taken from Stata
bking_results = array([
7.320813, 2.886914, -6.818976, -13.49436, -13.27936, -9.405913,
-5.691091, -5.133076, -7.273468, -9.243364, -8.482916, -4.447764,
2.406559, 10.68433, 19.46414, 28.09749, 34.11066, 33.48468, 24.64598,
9.952399, -4.265528, -12.59471, -13.46714, -9.049501, -3.011248,
.5655082, 2.897976, 7.406077, 14.67959, 18.651, 13.05891, -2.945415,
-24.08659, -41.86147, -48.68383, -43.32689, -31.66654, -20.38356,
-13.76411, -9.978693, -3.7704, 10.27108, 31.02847, 51.87613, 66.93117,
73.51951, 73.4053, 69.17468, 59.8543, 38.23899, -.2604809, -49.0107,
-91.1128, -112.1574, -108.3227, -86.51453, -59.91258, -40.01185,
-29.70265, -22.76396, -13.08037, 1.913622, 20.44045, 37.32873,
46.79802, 51.95937, 59.67393, 70.50803, 81.27311, 83.53191, 67.72536,
33.78039, -6.509092, -37.31579, -46.05207, -29.81496, 1.416417,
28.31503, 32.90134, 8.949259, -35.41895, -84.65775, -124.4288,
-144.6036, -140.2204, -109.2624, -53.6901, 15.07415, 74.44268,
104.0403, 101.0725, 76.58291, 49.27925, 36.15751, 36.48799, 37.60897,
27.75998, 4.216643, -23.20579, -39.33292, -36.6134, -20.90161,
-4.143123, 5.48432, 9.270075, 13.69573, 22.16675, 33.01987, 41.93186,
47.12222, 48.62164, 47.30701, 40.20537, 22.37898, -7.133002, -43.3339,
-78.51229, -101.3684, -105.2179, -90.97147, -68.30824, -48.10113,
-35.60709, -31.15775, -31.82346, -32.49278, -28.22499, -14.42852,
10.1827, 36.64189, 49.43468, 38.75517, 6.447761, -33.15883, -62.60446,
-72.87829, -66.54629, -52.61205, -38.06676, -26.19963, -16.51492,
-7.007577, .6125674, 7.866972, 14.8123, 22.52388, 30.65265, 39.47801,
49.05027, 59.02925, 72.88999, 95.08865, 125.8983, 154.4283, 160.7638,
130.6092, 67.84406, -7.070272, -68.08128, -99.39944, -104.911,
-100.2372, -98.11596, -104.2051, -114.0125, -113.3475, -92.98669,
-51.91707, -.7313812, 43.22938, 64.62762, 64.07226, 59.35707, 67.06026,
91.87247, 124.4591, 151.2402, 163.0648, 154.6432
])
X = macrodata.load_pandas().data['realinv'].values
Y = bkfilter(X, 6, 32, 12)
assert_almost_equal(Y, bking_results, 4)
def test_bking_pandas():
# 1d
dta = macrodata.load_pandas().data
index = Index(dates_from_range('1959Q1', '2009Q3'))
dta.index = index
filtered = bkfilter(dta["infl"])
nd_filtered = bkfilter(dta['infl'].values)
assert_equal(filtered.values, nd_filtered)
assert_equal(filtered.index[0], datetime(1962, 3, 31))
assert_equal(filtered.index[-1], datetime(2006, 9, 30))
assert_equal(filtered.name, "infl")
#2d
filtered = bkfilter(dta[["infl","unemp"]])
nd_filtered = bkfilter(dta[['infl', 'unemp']].values)
assert_equal(filtered.values, nd_filtered)
assert_equal(filtered.index[0], datetime(1962, 3, 31))
assert_equal(filtered.index[-1], datetime(2006, 9, 30))
assert_equal(filtered.columns.values, ["infl", "unemp"])
def test_bking_pandas():
# 1d
dta = macrodata.load_pandas().data
index = DatetimeIndex(start='1959-01-01', end='2009-10-01', freq='Q')
dta.index = index
filtered = bkfilter(dta["infl"])
nd_filtered = bkfilter(dta['infl'].values)
assert_equal(filtered.values, nd_filtered)
assert_equal(filtered.index[0], datetime(1962, 3, 31))
assert_equal(filtered.index[-1], datetime(2006, 9, 30))
assert_equal(filtered.name, "infl")
#2d
filtered = bkfilter(dta[["infl","unemp"]])
nd_filtered = bkfilter(dta[['infl', 'unemp']].values)
assert_equal(filtered.values, nd_filtered)
assert_equal(filtered.index[0], datetime(1962, 3, 31))
assert_equal(filtered.index[-1], datetime(2006, 9, 30))
assert_equal(filtered.columns.values, ["infl", "unemp"])
def test_bking_pandas():
# 1d
dta = macrodata.load_pandas().data
index = date_range(start='1959-01-01', end='2009-10-01', freq='Q')
dta.index = index
filtered = bkfilter(dta["infl"])
nd_filtered = bkfilter(dta['infl'].values)
assert_equal(filtered.values, nd_filtered)
assert_equal(filtered.index[0], datetime(1962, 3, 31))
assert_equal(filtered.index[-1], datetime(2006, 9, 30))
assert_equal(filtered.name, "infl")
# 2d
filtered = bkfilter(dta[["infl", "unemp"]])
nd_filtered = bkfilter(dta[['infl', 'unemp']].values)
assert_equal(filtered.values, nd_filtered)
assert_equal(filtered.index[0], datetime(1962, 3, 31))
assert_equal(filtered.index[-1], datetime(2006, 9, 30))
assert_equal(filtered.columns.values, ["infl", "unemp"])
def test_bking_pandas():
# 1d
dta = macrodata.load_pandas().data
index = Index(dates_from_range('1959Q1', '2009Q3'))
dta.index = index
filtered = bkfilter(dta["infl"])
nd_filtered = bkfilter(dta['infl'].values)
assert_equal(filtered.values, nd_filtered)
assert_equal(filtered.index[0], datetime(1962, 3, 31))
assert_equal(filtered.index[-1], datetime(2006, 9, 30))
assert_equal(filtered.name, "infl")
#2d
filtered = bkfilter(dta[["infl","unemp"]])
nd_filtered = bkfilter(dta[['infl', 'unemp']].values)
assert_equal(filtered.values, nd_filtered)
assert_equal(filtered.index[0], datetime(1962, 3, 31))
assert_equal(filtered.index[-1], datetime(2006, 9, 30))
assert_equal(filtered.columns.values, ["infl", "unemp"])
def test_bking1d():
"""
Test Baxter King band-pass filter. Results are taken from Stata
"""
bking_results = array([7.320813, 2.886914, -6.818976, -13.49436,
-13.27936, -9.405913, -5.691091, -5.133076, -7.273468,
-9.243364, -8.482916, -4.447764, 2.406559, 10.68433,
19.46414, 28.09749, 34.11066, 33.48468, 24.64598, 9.952399,
-4.265528, -12.59471, -13.46714, -9.049501, -3.011248,
.5655082, 2.897976, 7.406077, 14.67959, 18.651, 13.05891,
-2.945415, -24.08659, -41.86147, -48.68383, -43.32689,
-31.66654, -20.38356, -13.76411, -9.978693, -3.7704, 10.27108,
31.02847, 51.87613, 66.93117, 73.51951, 73.4053, 69.17468,
59.8543, 38.23899, -.2604809, -49.0107, -91.1128, -112.1574,
-108.3227, -86.51453, -59.91258, -40.01185, -29.70265,
-22.76396, -13.08037, 1.913622, 20.44045, 37.32873, 46.79802,
51.95937, 59.67393, 70.50803, 81.27311, 83.53191, 67.72536,
33.78039, -6.509092, -37.31579, -46.05207, -29.81496, 1.416417,
28.31503,
32.90134, 8.949259, -35.41895, -84.65775, -124.4288, -144.6036,
-140.2204, -109.2624, -53.6901, 15.07415, 74.44268, 104.0403,
101.0725, 76.58291, 49.27925, 36.15751, 36.48799, 37.60897,
27.75998, 4.216643, -23.20579, -39.33292, -36.6134, -20.90161,
-4.143123, 5.48432, 9.270075, 13.69573, 22.16675, 33.01987,
41.93186, 47.12222, 48.62164, 47.30701, 40.20537, 22.37898,
-7.133002, -43.3339, -78.51229, -101.3684, -105.2179,
-90.97147,
-68.30824, -48.10113, -35.60709, -31.15775, -31.82346,
-32.49278, -28.22499, -14.42852, 10.1827, 36.64189, 49.43468,
38.75517, 6.447761, -33.15883, -62.60446, -72.87829, -66.54629,
-52.61205, -38.06676, -26.19963, -16.51492, -7.007577,
.6125674,
7.866972, 14.8123, 22.52388, 30.65265, 39.47801, 49.05027,
59.02925,
72.88999, 95.08865, 125.8983, 154.4283, 160.7638, 130.6092,
67.84406, -7.070272, -68.08128, -99.39944, -104.911,
-100.2372, -98.11596, -104.2051, -114.0125, -113.3475,
-92.98669, -51.91707, -.7313812, 43.22938, 64.62762, 64.07226,
59.35707, 67.06026, 91.87247, 124.4591, 151.2402, 163.0648,
154.6432])
X = macrodata.load().data['realinv']
Y = bkfilter(X, 6, 32, 12)
assert_almost_equal(Y,bking_results,4)
def test_bking2d():
# Test Baxter-King band-pass filter with 2d input
bking_results = array([[7.320813,-.0374475], [2.886914,-.0430094],
[-6.818976,-.053456], [-13.49436,-.0620739], [-13.27936,-.0626929],
[-9.405913,-.0603022], [-5.691091,-.0630016], [-5.133076,-.0832268],
[-7.273468,-.1186448], [-9.243364,-.1619868], [-8.482916,-.2116604],
[-4.447764,-.2670747], [2.406559,-.3209931], [10.68433,-.3583075],
[19.46414,-.3626742], [28.09749,-.3294618], [34.11066,-.2773388],
[33.48468,-.2436127], [24.64598,-.2605531], [9.952399,-.3305166],
[-4.265528,-.4275561], [-12.59471,-.5076068], [-13.46714,-.537573],
[-9.049501,-.5205845], [-3.011248,-.481673], [.5655082,-.4403994],
[2.897976,-.4039957], [7.406077,-.3537394], [14.67959,-.2687359],
[18.651,-.1459743], [13.05891,.0014926], [-2.945415,.1424277],
[-24.08659,.2451936], [-41.86147,.288541], [-48.68383,.2727282],
[-43.32689,.1959127], [-31.66654,.0644874], [-20.38356,-.1158372],
[-13.76411,-.3518627], [-9.978693,-.6557535], [-3.7704,-1.003754],
[10.27108,-1.341632], [31.02847,-1.614486], [51.87613,-1.779089],
[66.93117,-1.807459], [73.51951,-1.679688], [73.4053,-1.401012],
[69.17468,-.9954996], [59.8543,-.511261], [38.23899,-.0146745],
[-.2604809,.4261311], [-49.0107,.7452514], [-91.1128,.8879492],
[-112.1574,.8282748], [-108.3227,.5851508], [-86.51453,.2351699],
[-59.91258,-.1208998], [-40.01185,-.4297895], [-29.70265,-.6821963],
[-22.76396,-.9234254], [-13.08037,-1.217539], [1.913622,-1.57367],
[20.44045,-1.927008], [37.32873,-2.229565], [46.79802,-2.463154],
[51.95937,-2.614697], [59.67393,-2.681357], [70.50803,-2.609654],
[81.27311,-2.301618], [83.53191,-1.720974], [67.72536,-.9837123],
[33.78039,-.2261613], [-6.509092,.4546985], [-37.31579,1.005751],
[-46.05207,1.457224], [-29.81496,1.870815], [1.416417,2.263313],
[28.31503,2.599906], [32.90134,2.812282], [8.949259,2.83358],
[-35.41895,2.632667], [-84.65775,2.201077], [-124.4288,1.598951],
[-144.6036,.9504762], [-140.2204,.4187932], [-109.2624,.1646726],
[-53.6901,.2034265], [15.07415,.398165], [74.44268,.5427476],
[104.0403,.5454975], [101.0725,.4723354], [76.58291,.4626823],
[49.27925,.5840143], [36.15751,.7187981], [36.48799,.6058422],
[37.60897,.1221227], [27.75998,-.5891272], [4.216643,-1.249841],
[-23.20579,-1.594972], [-39.33292,-1.545968], [-36.6134,-1.275494],
[-20.90161,-1.035783], [-4.143123,-.9971732], [5.48432,-1.154264],
[9.270075,-1.29987], [13.69573,-1.240559], [22.16675,-.9662656],
[33.01987,-.6420301], [41.93186,-.4698712], [47.12222,-.4527797],
[48.62164,-.4407153], [47.30701,-.2416076], [40.20537,.2317583],
[22.37898,.8710276], [-7.133002,1.426177], [-43.3339,1.652785],
[-78.51229,1.488021], [-101.3684,1.072096], [-105.2179,.6496446],
[-90.97147,.4193682], [-68.30824,.41847], [-48.10113,.5253419],
[-35.60709,.595076], [-31.15775,.5509905], [-31.82346,.3755519],
[-32.49278,.1297979], [-28.22499,-.0916165], [-14.42852,-.2531037],
[10.1827,-.3220784], [36.64189,-.2660561], [49.43468,-.1358522],
[38.75517,-.0279508], [6.447761,.0168735], [-33.15883,.0315687],
[-62.60446,.0819507], [-72.87829,.2274033], [-66.54629,.4641401],
[-52.61205,.7211093], [-38.06676,.907773], [-26.19963,.9387103],
[-16.51492,.7940786], [-7.007577,.5026631], [.6125674,.1224996],
[7.866972,-.2714422], [14.8123,-.6273921], [22.52388,-.9124271],
[30.65265,-1.108861], [39.47801,-1.199206], [49.05027,-1.19908],
[59.02925,-1.139046], [72.88999,-.9775021], [95.08865,-.6592603],
[125.8983,-.1609712], [154.4283,.4796201], [160.7638,1.100565],
[130.6092,1.447148], [67.84406,1.359608], [-7.070272,.8931825],
[-68.08128,.2619787], [-99.39944,-.252208], [-104.911,-.4703874],
[-100.2372,-.4430657], [-98.11596,-.390683], [-104.2051,-.5647846],
[-114.0125,-.9397582], [-113.3475,-1.341633], [-92.98669,-1.567337],
[-51.91707,-1.504943], [-.7313812,-1.30576], [43.22938,-1.17151],
[64.62762,-1.136151], [64.07226,-1.050555], [59.35707,-.7308369],
[67.06026,-.1766731], [91.87247,.3898467], [124.4591,.8135461],
[151.2402,.9644226], [163.0648,.6865934], [154.6432,.0115685]])
X = macrodata.load_pandas().data[['realinv','cpi']].values.astype(np.float)
Y = bkfilter(X, 6, 32, 12)
assert_almost_equal(Y,bking_results,4)
def test_bking2d():
# Test Baxter-King band-pass filter with 2d input
bking_results = array([[7.320813,-.0374475], [2.886914,-.0430094],
[-6.818976,-.053456], [-13.49436,-.0620739], [-13.27936,-.0626929],
[-9.405913,-.0603022], [-5.691091,-.0630016], [-5.133076,-.0832268],
[-7.273468,-.1186448], [-9.243364,-.1619868], [-8.482916,-.2116604],
[-4.447764,-.2670747], [2.406559,-.3209931], [10.68433,-.3583075],
[19.46414,-.3626742], [28.09749,-.3294618], [34.11066,-.2773388],
[33.48468,-.2436127], [24.64598,-.2605531], [9.952399,-.3305166],
[-4.265528,-.4275561], [-12.59471,-.5076068], [-13.46714,-.537573],
[-9.049501,-.5205845], [-3.011248,-.481673], [.5655082,-.4403994],
[2.897976,-.4039957], [7.406077,-.3537394], [14.67959,-.2687359],
[18.651,-.1459743], [13.05891,.0014926], [-2.945415,.1424277],
[-24.08659,.2451936], [-41.86147,.288541], [-48.68383,.2727282],
[-43.32689,.1959127], [-31.66654,.0644874], [-20.38356,-.1158372],
[-13.76411,-.3518627], [-9.978693,-.6557535], [-3.7704,-1.003754],
[10.27108,-1.341632], [31.02847,-1.614486], [51.87613,-1.779089],
[66.93117,-1.807459], [73.51951,-1.679688], [73.4053,-1.401012],
[69.17468,-.9954996], [59.8543,-.511261], [38.23899,-.0146745],
[-.2604809,.4261311], [-49.0107,.7452514], [-91.1128,.8879492],
[-112.1574,.8282748], [-108.3227,.5851508], [-86.51453,.2351699],
[-59.91258,-.1208998], [-40.01185,-.4297895], [-29.70265,-.6821963],
[-22.76396,-.9234254], [-13.08037,-1.217539], [1.913622,-1.57367],
[20.44045,-1.927008], [37.32873,-2.229565], [46.79802,-2.463154],
[51.95937,-2.614697], [59.67393,-2.681357], [70.50803,-2.609654],
[81.27311,-2.301618], [83.53191,-1.720974], [67.72536,-.9837123],
[33.78039,-.2261613], [-6.509092,.4546985], [-37.31579,1.005751],
[-46.05207,1.457224], [-29.81496,1.870815], [1.416417,2.263313],
[28.31503,2.599906], [32.90134,2.812282], [8.949259,2.83358],
[-35.41895,2.632667], [-84.65775,2.201077], [-124.4288,1.598951],
[-144.6036,.9504762], [-140.2204,.4187932], [-109.2624,.1646726],
[-53.6901,.2034265], [15.07415,.398165], [74.44268,.5427476],
[104.0403,.5454975], [101.0725,.4723354], [76.58291,.4626823],
[49.27925,.5840143], [36.15751,.7187981], [36.48799,.6058422],
[37.60897,.1221227], [27.75998,-.5891272], [4.216643,-1.249841],
[-23.20579,-1.594972], [-39.33292,-1.545968], [-36.6134,-1.275494],
[-20.90161,-1.035783], [-4.143123,-.9971732], [5.48432,-1.154264],
[9.270075,-1.29987], [13.69573,-1.240559], [22.16675,-.9662656],
[33.01987,-.6420301], [41.93186,-.4698712], [47.12222,-.4527797],
[48.62164,-.4407153], [47.30701,-.2416076], [40.20537,.2317583],
[22.37898,.8710276], [-7.133002,1.426177], [-43.3339,1.652785],
[-78.51229,1.488021], [-101.3684,1.072096], [-105.2179,.6496446],
[-90.97147,.4193682], [-68.30824,.41847], [-48.10113,.5253419],
[-35.60709,.595076], [-31.15775,.5509905], [-31.82346,.3755519],
[-32.49278,.1297979], [-28.22499,-.0916165], [-14.42852,-.2531037],
[10.1827,-.3220784], [36.64189,-.2660561], [49.43468,-.1358522],
[38.75517,-.0279508], [6.447761,.0168735], [-33.15883,.0315687],
[-62.60446,.0819507], [-72.87829,.2274033], [-66.54629,.4641401],
[-52.61205,.7211093], [-38.06676,.907773], [-26.19963,.9387103],
[-16.51492,.7940786], [-7.007577,.5026631], [.6125674,.1224996],
[7.866972,-.2714422], [14.8123,-.6273921], [22.52388,-.9124271],
[30.65265,-1.108861], [39.47801,-1.199206], [49.05027,-1.19908],
[59.02925,-1.139046], [72.88999,-.9775021], [95.08865,-.6592603],
[125.8983,-.1609712], [154.4283,.4796201], [160.7638,1.100565],
[130.6092,1.447148], [67.84406,1.359608], [-7.070272,.8931825],
[-68.08128,.2619787], [-99.39944,-.252208], [-104.911,-.4703874],
[-100.2372,-.4430657], [-98.11596,-.390683], [-104.2051,-.5647846],
[-114.0125,-.9397582], [-113.3475,-1.341633], [-92.98669,-1.567337],
[-51.91707,-1.504943], [-.7313812,-1.30576], [43.22938,-1.17151],
[64.62762,-1.136151], [64.07226,-1.050555], [59.35707,-.7308369],
[67.06026,-.1766731], [91.87247,.3898467], [124.4591,.8135461],
[151.2402,.9644226], [163.0648,.6865934], [154.6432,.0115685]])
X = macrodata.load_pandas().data[['realinv','cpi']].values.astype(np.float)
Y = bkfilter(X, 6, 32, 12)
assert_almost_equal(Y,bking_results,4)