def test_cfitz_pandas():
# 1d
dta = macrodata.load_pandas().data
index = Index(dates_from_range('1959Q1', '2009Q3'))
dta.index = index
cycle, trend = cffilter(dta["infl"])
ndcycle, ndtrend = cffilter(dta['infl'].values)
assert_allclose(cycle.values, ndcycle, rtol=1e-14)
assert_equal(cycle.index[0], datetime(1959, 3, 31))
assert_equal(cycle.index[-1], datetime(2009, 9, 30))
assert_equal(cycle.name, "infl")
#2d
cycle, trend = cffilter(dta[["infl","unemp"]])
ndcycle, ndtrend = cffilter(dta[['infl', 'unemp']].values)
assert_allclose(cycle.values, ndcycle, rtol=1e-14)
assert_equal(cycle.index[0], datetime(1959, 3, 31))
assert_equal(cycle.index[-1], datetime(2009, 9, 30))
assert_equal(cycle.columns.values, ["infl", "unemp"])
def test_cfitz_pandas():
# 1d
dta = macrodata.load_pandas().data
index = DatetimeIndex(start='1959-01-01', end='2009-10-01', freq='Q')
dta.index = index
cycle, trend = cffilter(dta["infl"])
ndcycle, ndtrend = cffilter(dta['infl'].values)
assert_allclose(cycle.values, ndcycle, rtol=1e-14)
assert_equal(cycle.index[0], datetime(1959, 3, 31))
assert_equal(cycle.index[-1], datetime(2009, 9, 30))
assert_equal(cycle.name, "infl")
#2d
cycle, trend = cffilter(dta[["infl","unemp"]])
ndcycle, ndtrend = cffilter(dta[['infl', 'unemp']].values)
assert_allclose(cycle.values, ndcycle, rtol=1e-14)
assert_equal(cycle.index[0], datetime(1959, 3, 31))
assert_equal(cycle.index[-1], datetime(2009, 9, 30))
assert_equal(cycle.columns.values, ["infl", "unemp"])
def test_cfitz_pandas():
# 1d
dta = macrodata.load_pandas().data
index = date_range(start='1959-01-01', end='2009-10-01', freq='Q')
dta.index = index
cycle, trend = cffilter(dta["infl"])
ndcycle, ndtrend = cffilter(dta['infl'].values)
assert_allclose(cycle.values, ndcycle, rtol=1e-14)
assert_equal(cycle.index[0], datetime(1959, 3, 31))
assert_equal(cycle.index[-1], datetime(2009, 9, 30))
assert_equal(cycle.name, "infl")
# 2d
cycle, trend = cffilter(dta[["infl", "unemp"]])
ndcycle, ndtrend = cffilter(dta[['infl', 'unemp']].values)
assert_allclose(cycle.values, ndcycle, rtol=1e-14)
assert_equal(cycle.index[0], datetime(1959, 3, 31))
assert_equal(cycle.index[-1], datetime(2009, 9, 30))
assert_equal(cycle.columns.values, ["infl", "unemp"])
def test_cfitz_pandas():
# 1d
dta = macrodata.load_pandas().data
index = Index(dates_from_range('1959Q1', '2009Q3'))
dta.index = index
cycle, trend = cffilter(dta["infl"])
ndcycle, ndtrend = cffilter(dta['infl'].values)
assert_allclose(cycle.values, ndcycle, rtol=1e-14)
assert_equal(cycle.index[0], datetime(1959, 3, 31))
assert_equal(cycle.index[-1], datetime(2009, 9, 30))
assert_equal(cycle.name, "infl")
#2d
cycle, trend = cffilter(dta[["infl","unemp"]])
ndcycle, ndtrend = cffilter(dta[['infl', 'unemp']].values)
assert_allclose(cycle.values, ndcycle, rtol=1e-14)
assert_equal(cycle.index[0], datetime(1959, 3, 31))
assert_equal(cycle.index[-1], datetime(2009, 9, 30))
assert_equal(cycle.columns.values, ["infl", "unemp"])
def test_cfitz_filter():
# Test Christiano-Fitzgerald Filter. Results taken from R.
# NOTE: The Stata mata code and the matlab code it's based on are wrong.
cfilt_res = array([[0.712599537179426,0.439563468233128],
[1.06824041304411,0.352886666575907],
[1.19422467791128,0.257297004260607],
[0.970845473140327,0.114504692143872],
[0.467026976628563,-0.070734782329146],
[-0.089153511514031,-0.238609685132605],
[-0.452339254128573,-0.32376584042956],
[-0.513231214461187,-0.314288554228112],
[-0.352372578720063,-0.258815055101336],
[-0.160282602521333,-0.215076844089567],
[-0.0918782593827686,-0.194120745417214],
[-0.168083823205437,-0.158327420072693],
[-0.291595204965808,-0.0742727139742986],
[-0.348638756841307,0.037008291163602],
[-0.304328040874631,0.108196527328748],
[-0.215933150969686,0.0869231107437175],
[-0.165632621390694,-0.0130556619786275],
[-0.182326839507151,-0.126570926191824],
[-0.223737786804725,-0.205535321806185],
[-0.228939291453403,-0.269110078201836],
[-0.185518327227038,-0.375976507132174],
[-0.143900152461529,-0.53760115656157],
[-0.162749541550174,-0.660065018626038],
[-0.236263634756884,-0.588542352053736],
[-0.275785854309211,-0.236867929421996],
[-0.173666515108109,0.303436335579219],
[0.0963135720251639,0.779772338801993],
[0.427070069032285,0.929108075350647],
[0.629034743259998,0.658330841002647],
[0.557941248993624,0.118500049361018],
[0.227866624051603,-0.385048321099911],
[-0.179878859883227,-0.582223992561493],
[-0.428263000051965,-0.394053702908091],
[-0.381640684645912,0.0445437406977307],
[-0.0942745548364887,0.493997792757968],
[0.238132391504895,0.764519811304315],
[0.431293754256291,0.814755206427316],
[0.455010435813661,0.745567043101108],
[0.452800768971269,0.709401694610443],
[0.615754619329312,0.798293251119636],
[1.00256335412457,0.975856845059388],
[1.44841039351691,1.09097252730799],
[1.64651971120370,0.967823457118036],
[1.35534532901802,0.522397724737059],
[0.580492790312048,-0.16941343361609],
[-0.410746188031773,-0.90760401289056],
[-1.26148406066881,-1.49592867122591],
[-1.75784179124566,-1.87404167409849],
[-1.94478553960064,-2.14586210891112],
[-2.03751202708559,-2.465855239868],
[-2.20376059354166,-2.86294187189049],
[-2.39722338315852,-3.15004697654831],
[-2.38032366161537,-3.01390466643222],
[-1.91798022532025,-2.23395210271226],
[-0.982318490353716,-0.861346053067472],
[0.199047030343412,0.790266582335616],
[1.28582776574786,2.33731327460104],
[2.03565905376430,3.54085486821911],
[2.41201557412526,4.36519456268955],
[2.52011070482927,4.84810517685452],
[2.45618479815452,4.92906708807477],
[2.22272146945388,4.42591058990048],
[1.78307567169034,3.20962906108388],
[1.18234431860844,1.42568060336985],
[0.590069172333348,-0.461896808688991],
[0.19662302949837,-1.89020992539465],
[0.048307034171166,-2.53490571941987],
[-0.0141956981899000,-2.50020338531674],
[-0.230505187108187,-2.20625973569823],
[-0.700947410386801,-2.06643697511048],
[-1.27085123163060,-2.21536883679783],
[-1.64082547897928,-2.49016921117735],
[-1.62286182971254,-2.63948740221362],
[-1.31609762181362,-2.54685250637904],
[-1.03085567704873,-2.27157435428923],
[-1.01100120380112,-1.90404507430561],
[-1.19823958399826,-1.4123209792214],
[-1.26398933608383,-0.654000086153317],
[-0.904710628949692,0.447960016248203],
[-0.151340093679588,1.73970411237156],
[0.592926881165989,2.85741581650685],
[0.851660587507523,3.4410446351716],
[0.480324393352127,3.36870271362297],
[-0.165153230782417,2.82003806696544],
[-0.459235919375844,2.12858991660866],
[0.0271158842479935,1.55840980891556],
[1.18759188180671,1.17980298478623],
[2.43238266962309,0.904011534980672],
[3.08277213720132,0.595286911949837],
[2.79953663720953,0.148014782859571],
[1.73694442845833,-0.496297332023011],
[0.357638079951977,-1.33108149877570],
[-0.891418825216945,-2.22650083183366],
[-1.77646467793627,-2.89359299718574],
[-2.24614790863088,-2.97921619243347],
[-2.29048879096607,-2.30003092779280],
[-1.87929656465888,-1.05298381273274],
[-1.04510101454788,0.215837488618531],
[0.00413338508394524,0.937866257924888],
[0.906870625251025,0.92664365343019],
[1.33869057593416,0.518564571494679],
[1.22659678454440,0.288096869652890],
[0.79380139656044,0.541053084632774],
[0.38029431865832,1.01905199983437],
[0.183929413600038,1.10529586616777],
[0.140045425897033,0.393618564826736],
[0.0337313182352219,-0.86431819007665],
[-0.269208622829813,-1.85638085246792],
[-0.687276639992166,-1.82275359004533],
[-1.00161592325614,-0.692695765071617],
[-1.06320089194036,0.803577361347341],
[-0.927152307196776,1.67366338751788],
[-0.786802101366614,1.42564362251793],
[-0.772970884572502,0.426446388877964],
[-0.81275662801789,-0.437721213831647],
[-0.686831250382476,-0.504255468075149],
[-0.237936463020255,0.148656301898438],
[0.459631879129522,0.832925905720478],
[1.12717379822508,0.889455302576383],
[1.48640453200855,0.268042676202216],
[1.46515245776211,-0.446505038539178],
[1.22993484959115,-0.563868578181134],
[1.0272100765927,0.0996849952196907],
[0.979191212438404,1.05053652824665],
[1.00733490030391,1.51658415000556],
[0.932192535457706,1.06262774912638],
[0.643374300839414,-0.0865180803476065],
[0.186885168954461,-1.24799408923277],
[-0.290842337365465,-1.80035611156538],
[-0.669446735516495,-1.58847333561510],
[-0.928915624595538,-0.932116966867929],
[-1.11758635926997,-0.307879396807850],
[-1.26832454569756,-0.00856199983957032],
[-1.35755577149251,-0.0303537516690989],
[-1.34244112665546,-0.196807620887435],
[-1.22227976023299,-0.342062643495923],
[-1.04601473486818,-0.390474392372016],
[-0.85158508717846,-0.322164402093596],
[-0.605033439160543,-0.126930141915954],
[-0.218304303942818,0.179551077808122],
[0.352173017779006,0.512327303000081],
[1.01389600097229,0.733397490572755],
[1.55149778750607,0.748740387440165],
[1.75499674757591,0.601759717901009],
[1.56636057468633,0.457705308377562],
[1.12239792537274,0.470849913286519],
[0.655802600286141,0.646142040378738],
[0.335285115340180,0.824103600255079],
[0.173454596506888,0.808068498175582],
[0.0666753011315252,0.521488214487996],
[-0.0842367474816212,0.0583493276173476],
[-0.285604762631464,-0.405958418332253],
[-0.465735422869919,-0.747800086512926],
[-0.563586691231348,-0.94982272350799],
[-0.598110322024572,-1.04736894794361],
[-0.65216025756061,-1.04858365218822],
[-0.789663117801624,-0.924145633093637],
[-0.984704045337959,-0.670740724179446],
[-1.12449565589348,-0.359476803003931],
[-1.07878318723543,-0.092290938944355],
[-0.775555435407062,0.102132527529259],
[-0.231610677329856,0.314409560305622],
[0.463192794235131,0.663523546243286],
[1.17416973448423,1.13156902460931],
[1.74112278814906,1.48967153067024],
[2.00320855757084,1.42571085941843],
[1.8529912317336,0.802460519079555],
[1.30747261947211,-0.169219078629572],
[0.540237070403222,-1.01621539672694],
[-0.177136817092375,-1.3130784867977],
[-0.611981468823591,-0.982477824460773],
[-0.700240028737747,-0.344919609255406],
[-0.572396497740112,0.125083535035390],
[-0.450934466600975,0.142553112732280],
[-0.494020014254326,-0.211429053871656],
[-0.701707589094918,-0.599602868825992],
[-0.94721339346157,-0.710669870591623],
[-1.09297139748946,-0.47846194092245],
[-1.08850658866583,-0.082258450179988],
[-0.976082880696692,0.235758921309309],
[-0.81885695346771,0.365298185204303],
[-0.63165529525553,0.384725179378064],
[-0.37983149226421,0.460240196164378],
[-0.0375551354277652,0.68580913832794],
[0.361996927427804,0.984470835955107],
[0.739920615366072,1.13195975020298],
[1.03583478061534,0.88812510421667],
[1.25614938962160,0.172561520611839],
[1.45295030231799,-0.804979390544485],
[1.64887158748426,-1.55662011197859],
[1.78022721495313,-1.52921975346218],
[1.71945683859668,-0.462240366424548],
[1.36728880239190,1.31213774341268],
[0.740173894315912,2.88362740582926],
[-0.0205364331835904,3.20319080963167],
[-0.725643970956428,1.75222466531151],
[-1.23900506689782,-0.998432917440275],
[-1.52651897508678,-3.72752870885448],
[-1.62857516631435,-5.00551707196292],
[-1.59657420180451,-4.18499132634584],
[-1.45489013276495,-1.81759097305637],
[-1.21309542313047,0.722029457352468]])
dta = macrodata.load_pandas().data[['tbilrate','infl']].values[1:]
cyc, trend = cffilter(dta)
assert_almost_equal(cyc, cfilt_res, 8)
#do 1d
cyc, trend = cffilter(dta[:,1])
assert_almost_equal(cyc, cfilt_res[:,1], 8)
def test_cfitz_filter():
# Test Christiano-Fitzgerald Filter. Results taken from R.
# NOTE: The Stata mata code and the matlab code it's based on are wrong.
cfilt_res = array([[0.712599537179426,0.439563468233128],
[1.06824041304411,0.352886666575907],
[1.19422467791128,0.257297004260607],
[0.970845473140327,0.114504692143872],
[0.467026976628563,-0.070734782329146],
[-0.089153511514031,-0.238609685132605],
[-0.452339254128573,-0.32376584042956],
[-0.513231214461187,-0.314288554228112],
[-0.352372578720063,-0.258815055101336],
[-0.160282602521333,-0.215076844089567],
[-0.0918782593827686,-0.194120745417214],
[-0.168083823205437,-0.158327420072693],
[-0.291595204965808,-0.0742727139742986],
[-0.348638756841307,0.037008291163602],
[-0.304328040874631,0.108196527328748],
[-0.215933150969686,0.0869231107437175],
[-0.165632621390694,-0.0130556619786275],
[-0.182326839507151,-0.126570926191824],
[-0.223737786804725,-0.205535321806185],
[-0.228939291453403,-0.269110078201836],
[-0.185518327227038,-0.375976507132174],
[-0.143900152461529,-0.53760115656157],
[-0.162749541550174,-0.660065018626038],
[-0.236263634756884,-0.588542352053736],
[-0.275785854309211,-0.236867929421996],
[-0.173666515108109,0.303436335579219],
[0.0963135720251639,0.779772338801993],
[0.427070069032285,0.929108075350647],
[0.629034743259998,0.658330841002647],
[0.557941248993624,0.118500049361018],
[0.227866624051603,-0.385048321099911],
[-0.179878859883227,-0.582223992561493],
[-0.428263000051965,-0.394053702908091],
[-0.381640684645912,0.0445437406977307],
[-0.0942745548364887,0.493997792757968],
[0.238132391504895,0.764519811304315],
[0.431293754256291,0.814755206427316],
[0.455010435813661,0.745567043101108],
[0.452800768971269,0.709401694610443],
[0.615754619329312,0.798293251119636],
[1.00256335412457,0.975856845059388],
[1.44841039351691,1.09097252730799],
[1.64651971120370,0.967823457118036],
[1.35534532901802,0.522397724737059],
[0.580492790312048,-0.16941343361609],
[-0.410746188031773,-0.90760401289056],
[-1.26148406066881,-1.49592867122591],
[-1.75784179124566,-1.87404167409849],
[-1.94478553960064,-2.14586210891112],
[-2.03751202708559,-2.465855239868],
[-2.20376059354166,-2.86294187189049],
[-2.39722338315852,-3.15004697654831],
[-2.38032366161537,-3.01390466643222],
[-1.91798022532025,-2.23395210271226],
[-0.982318490353716,-0.861346053067472],
[0.199047030343412,0.790266582335616],
[1.28582776574786,2.33731327460104],
[2.03565905376430,3.54085486821911],
[2.41201557412526,4.36519456268955],
[2.52011070482927,4.84810517685452],
[2.45618479815452,4.92906708807477],
[2.22272146945388,4.42591058990048],
[1.78307567169034,3.20962906108388],
[1.18234431860844,1.42568060336985],
[0.590069172333348,-0.461896808688991],
[0.19662302949837,-1.89020992539465],
[0.048307034171166,-2.53490571941987],
[-0.0141956981899000,-2.50020338531674],
[-0.230505187108187,-2.20625973569823],
[-0.700947410386801,-2.06643697511048],
[-1.27085123163060,-2.21536883679783],
[-1.64082547897928,-2.49016921117735],
[-1.62286182971254,-2.63948740221362],
[-1.31609762181362,-2.54685250637904],
[-1.03085567704873,-2.27157435428923],
[-1.01100120380112,-1.90404507430561],
[-1.19823958399826,-1.4123209792214],
[-1.26398933608383,-0.654000086153317],
[-0.904710628949692,0.447960016248203],
[-0.151340093679588,1.73970411237156],
[0.592926881165989,2.85741581650685],
[0.851660587507523,3.4410446351716],
[0.480324393352127,3.36870271362297],
[-0.165153230782417,2.82003806696544],
[-0.459235919375844,2.12858991660866],
[0.0271158842479935,1.55840980891556],
[1.18759188180671,1.17980298478623],
[2.43238266962309,0.904011534980672],
[3.08277213720132,0.595286911949837],
[2.79953663720953,0.148014782859571],
[1.73694442845833,-0.496297332023011],
[0.357638079951977,-1.33108149877570],
[-0.891418825216945,-2.22650083183366],
[-1.77646467793627,-2.89359299718574],
[-2.24614790863088,-2.97921619243347],
[-2.29048879096607,-2.30003092779280],
[-1.87929656465888,-1.05298381273274],
[-1.04510101454788,0.215837488618531],
[0.00413338508394524,0.937866257924888],
[0.906870625251025,0.92664365343019],
[1.33869057593416,0.518564571494679],
[1.22659678454440,0.288096869652890],
[0.79380139656044,0.541053084632774],
[0.38029431865832,1.01905199983437],
[0.183929413600038,1.10529586616777],
[0.140045425897033,0.393618564826736],
[0.0337313182352219,-0.86431819007665],
[-0.269208622829813,-1.85638085246792],
[-0.687276639992166,-1.82275359004533],
[-1.00161592325614,-0.692695765071617],
[-1.06320089194036,0.803577361347341],
[-0.927152307196776,1.67366338751788],
[-0.786802101366614,1.42564362251793],
[-0.772970884572502,0.426446388877964],
[-0.81275662801789,-0.437721213831647],
[-0.686831250382476,-0.504255468075149],
[-0.237936463020255,0.148656301898438],
[0.459631879129522,0.832925905720478],
[1.12717379822508,0.889455302576383],
[1.48640453200855,0.268042676202216],
[1.46515245776211,-0.446505038539178],
[1.22993484959115,-0.563868578181134],
[1.0272100765927,0.0996849952196907],
[0.979191212438404,1.05053652824665],
[1.00733490030391,1.51658415000556],
[0.932192535457706,1.06262774912638],
[0.643374300839414,-0.0865180803476065],
[0.186885168954461,-1.24799408923277],
[-0.290842337365465,-1.80035611156538],
[-0.669446735516495,-1.58847333561510],
[-0.928915624595538,-0.932116966867929],
[-1.11758635926997,-0.307879396807850],
[-1.26832454569756,-0.00856199983957032],
[-1.35755577149251,-0.0303537516690989],
[-1.34244112665546,-0.196807620887435],
[-1.22227976023299,-0.342062643495923],
[-1.04601473486818,-0.390474392372016],
[-0.85158508717846,-0.322164402093596],
[-0.605033439160543,-0.126930141915954],
[-0.218304303942818,0.179551077808122],
[0.352173017779006,0.512327303000081],
[1.01389600097229,0.733397490572755],
[1.55149778750607,0.748740387440165],
[1.75499674757591,0.601759717901009],
[1.56636057468633,0.457705308377562],
[1.12239792537274,0.470849913286519],
[0.655802600286141,0.646142040378738],
[0.335285115340180,0.824103600255079],
[0.173454596506888,0.808068498175582],
[0.0666753011315252,0.521488214487996],
[-0.0842367474816212,0.0583493276173476],
[-0.285604762631464,-0.405958418332253],
[-0.465735422869919,-0.747800086512926],
[-0.563586691231348,-0.94982272350799],
[-0.598110322024572,-1.04736894794361],
[-0.65216025756061,-1.04858365218822],
[-0.789663117801624,-0.924145633093637],
[-0.984704045337959,-0.670740724179446],
[-1.12449565589348,-0.359476803003931],
[-1.07878318723543,-0.092290938944355],
[-0.775555435407062,0.102132527529259],
[-0.231610677329856,0.314409560305622],
[0.463192794235131,0.663523546243286],
[1.17416973448423,1.13156902460931],
[1.74112278814906,1.48967153067024],
[2.00320855757084,1.42571085941843],
[1.8529912317336,0.802460519079555],
[1.30747261947211,-0.169219078629572],
[0.540237070403222,-1.01621539672694],
[-0.177136817092375,-1.3130784867977],
[-0.611981468823591,-0.982477824460773],
[-0.700240028737747,-0.344919609255406],
[-0.572396497740112,0.125083535035390],
[-0.450934466600975,0.142553112732280],
[-0.494020014254326,-0.211429053871656],
[-0.701707589094918,-0.599602868825992],
[-0.94721339346157,-0.710669870591623],
[-1.09297139748946,-0.47846194092245],
[-1.08850658866583,-0.082258450179988],
[-0.976082880696692,0.235758921309309],
[-0.81885695346771,0.365298185204303],
[-0.63165529525553,0.384725179378064],
[-0.37983149226421,0.460240196164378],
[-0.0375551354277652,0.68580913832794],
[0.361996927427804,0.984470835955107],
[0.739920615366072,1.13195975020298],
[1.03583478061534,0.88812510421667],
[1.25614938962160,0.172561520611839],
[1.45295030231799,-0.804979390544485],
[1.64887158748426,-1.55662011197859],
[1.78022721495313,-1.52921975346218],
[1.71945683859668,-0.462240366424548],
[1.36728880239190,1.31213774341268],
[0.740173894315912,2.88362740582926],
[-0.0205364331835904,3.20319080963167],
[-0.725643970956428,1.75222466531151],
[-1.23900506689782,-0.998432917440275],
[-1.52651897508678,-3.72752870885448],
[-1.62857516631435,-5.00551707196292],
[-1.59657420180451,-4.18499132634584],
[-1.45489013276495,-1.81759097305637],
[-1.21309542313047,0.722029457352468]])
dta = macrodata.load_pandas().data[['tbilrate','infl']].values[1:]
cyc, trend = cffilter(dta)
assert_almost_equal(cyc, cfilt_res, 8)
#do 1d
cyc, trend = cffilter(dta[:,1])
assert_almost_equal(cyc, cfilt_res[:,1], 8)
