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Statsmodels: statistical modeling and econometrics in Python
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Difference in local and deployed version of app using VAR from statsmodels.tsa.vector_ar.var_model #9340

Closed ejuliaschulz closed 3 weeks ago

ejuliaschulz commented 3 weeks ago

Describe the bug

I build and fit a VAR model to obtain the best model order based on the AIC criterion. When running this code locally (Windows 11 Pro, statsmodel version 0.14.2, python 3.11), my best model order is calculated as 8, with AIC of -599.87597. I use this code in a deployed app with streamlit, which seems to use Linux, and also Python 3.11, same module versions as I use locally. In this app, the exact same code (same module versions) produces a different output, the best model order changes to 9 with AIC of -600.3351. Here are two screenshots of the same code output, once in the deployed app (Linux?), and once in the local version of my app (Windows)

Deployed app image

Local app image

Code Sample, a copy-pastable example if possible

from statsmodels.tsa.vector_ar.var_model import VAR
import numpy as np
import pandas as pd

df = pd.DataFrame({'Variable 1': {'2014-02-01 00:00:00': 0.56, '2014-03-01 00:00:00': 0.56, '2014-04-01 00:00:00': 0.56, '2014-05-01 00:00:00': 0.56, '2014-06-01 00:00:00': 0.56, '2014-07-01 00:00:00': 0.56, '2014-08-01 00:00:00': 0.56, '2014-09-01 00:00:00': 0.56, '2014-10-01 00:00:00': 0.56, '2014-11-01 00:00:00': 0.5622405804992034, '2014-12-01 00:00:00': 0.5639382971491507, '2015-01-01 00:00:00': 0.5673876773197045, '2015-02-01 00:00:00': 0.5700568829892957, '2015-03-01 00:00:00': 0.5721369904866512, '2015-04-01 00:00:00': 0.5737669411810388, '2015-05-01 00:00:00': 0.5750496582196891, '2015-06-01 00:00:00': 0.5760625345224498, '2015-07-01 00:00:00': 0.5756424602322934, '2015-08-01 00:00:00': 0.5744974670957123, '2015-09-01 00:00:00': 0.5735874822657767, '2015-10-01 00:00:00': 0.5720558590821788, '2015-11-01 00:00:00': 0.5708356839624799, '2015-12-01 00:00:00': 0.5692592414961524, '2016-01-01 00:00:00': 0.5671976576424296, '2016-02-01 00:00:00': 0.5651503917621239, '2016-03-01 00:00:00': 0.562712946683977, '2016-04-01 00:00:00': 0.5615675880399065, '2016-05-01 00:00:00': 0.5606523000073893, '2016-06-01 00:00:00': 0.5603213278513066, '2016-07-01 00:00:00': 0.560256982625514, '2016-08-01 00:00:00': 0.5596049403490181, '2016-09-01 00:00:00': 0.5584830634367512, '2016-10-01 00:00:00': 0.5555846136385878, '2016-11-01 00:00:00': 0.5534666169589654, '2016-12-01 00:00:00': 0.551172362907164, '2017-01-01 00:00:00': 0.5483039274652103, '2017-02-01 00:00:00': 0.5459952666095429, '2017-03-01 00:00:00': 0.545364238228167, '2017-04-01 00:00:00': 0.5450207275070252, '2017-05-01 00:00:00': 0.5447632357790988, '2017-06-01 00:00:00': 0.5443097704025691, '2017-07-01 00:00:00': 0.5437569172905139, '2017-08-01 00:00:00': 0.5420997470512489, '2017-09-01 00:00:00': 0.5399143646574641, '2017-10-01 00:00:00': 0.5383202182911042, '2017-11-01 00:00:00': 0.5371102504716583, '2017-12-01 00:00:00': 0.5348746840588304, '2018-01-01 00:00:00': 0.5325060924257059, '2018-02-01 00:00:00': 0.5310115652773095, '2018-03-01 00:00:00': 0.5302256810053324, '2018-04-01 00:00:00': 0.5289805305871615, '2018-05-01 00:00:00': 0.5277844135439355, '2018-06-01 00:00:00': 0.5262275194581321, '2018-07-01 00:00:00': 0.5253820106236555, '2018-08-01 00:00:00': 0.5263056128184319, '2018-09-01 00:00:00': 0.5268444922709329, '2018-10-01 00:00:00': 0.5256755903180412, '2018-11-01 00:00:00': 0.5245404695363587, '2018-12-01 00:00:00': 0.523832374272646, '2019-01-01 00:00:00': 0.5234658988564947, '2019-02-01 00:00:00': 0.5228127182843988, '2019-03-01 00:00:00': 0.5219301737619219, '2019-04-01 00:00:00': 0.5208641381730857, '2019-05-01 00:00:00': 0.5196513097771639, '2019-06-01 00:00:00': 0.5183210471537155, '2019-07-01 00:00:00': 0.5162568368937079, '2019-08-01 00:00:00': 0.5140054687914045, '2019-09-01 00:00:00': 0.5124043746214663, '2019-10-01 00:00:00': 0.5113234994748499, '2019-11-01 00:00:00': 0.5108587995034134}, 'Variable 2': {'2014-02-01 00:00:00': 0.76, '2014-03-01 00:00:00': 0.7822222222222223, '2014-04-01 00:00:00': 0.7977049180327868, '2014-05-01 00:00:00': 0.8052574525745256, '2014-06-01 00:00:00': 0.8036934792955734, '2014-07-01 00:00:00': 0.8108240090207303, '2014-08-01 00:00:00': 0.8182076739929706, '2014-09-01 00:00:00': 0.8234447797372412, '2014-10-01 00:00:00': 0.8226490185282981, '2014-11-01 00:00:00': 0.8265366456010516, '2014-12-01 00:00:00': 0.8338582363360735, '2015-01-01 00:00:00': 0.8287344877468826, '2015-02-01 00:00:00': 0.829002314107386, '2015-03-01 00:00:00': 0.8229350083570376, '2015-04-01 00:00:00': 0.8202536653143172, '2015-05-01 00:00:00': 0.8222593883355219, '2015-06-01 00:00:00': 0.8197510274015803, '2015-07-01 00:00:00': 0.8238751148765261, '2015-08-01 00:00:00': 0.8210595148281693, '2015-09-01 00:00:00': 0.8127518307866864, '2015-10-01 00:00:00': 0.808159104203408, '2015-11-01 00:00:00': 0.8125597544301224, '2015-12-01 00:00:00': 0.8120447635746545, '2016-01-01 00:00:00': 0.8176623391101475, '2016-02-01 00:00:00': 0.8201392287124698, '2016-03-01 00:00:00': 0.8221173615095082, '2016-04-01 00:00:00': 0.8156783205618962, '2016-05-01 00:00:00': 0.8105327033783633, '2016-06-01 00:00:00': 0.8164352965231777, '2016-07-01 00:00:00': 0.8131441629925701, '2016-08-01 00:00:00': 0.8105127243434443, '2016-09-01 00:00:00': 0.8104100981659207, '2016-10-01 00:00:00': 0.8103280265136469, '2016-11-01 00:00:00': 0.8102623879282007, '2016-12-01 00:00:00': 0.8082090774208655, '2017-01-01 00:00:00': 0.8025654304685461, '2017-02-01 00:00:00': 0.794050133677398, '2017-03-01 00:00:00': 0.7772366149106914, '2017-04-01 00:00:00': 0.7737887190485899, '2017-05-01 00:00:00': 0.777031406265935, '2017-06-01 00:00:00': 0.773624762756274, '2017-07-01 00:00:00': 0.7748999186832886, '2017-08-01 00:00:00': 0.7759200043700653, '2017-09-01 00:00:00': 0.7807362657182619, '2017-10-01 00:00:00': 0.784589180392712, '2017-11-01 00:00:00': 0.7896715214079071, '2017-12-01 00:00:00': 0.7977374419713747, '2018-01-01 00:00:00': 0.7981899636686708, '2018-02-01 00:00:00': 0.8065521201207929, '2018-03-01 00:00:00': 0.8032416488480094, '2018-04-01 00:00:00': 0.8065933573481534, '2018-05-01 00:00:00': 0.8003098284823494, '2018-06-01 00:00:00': 0.8002478623330613, '2018-07-01 00:00:00': 0.7961982661924835, '2018-08-01 00:00:00': 0.7929585978026886, '2018-09-01 00:00:00': 0.7903668685453406, '2018-10-01 00:00:00': 0.7942935065893102, '2018-11-01 00:00:00': 0.795434808004322, '2018-12-01 00:00:00': 0.7923478404900027, '2019-01-01 00:00:00': 0.79187827167239, '2019-02-01 00:00:00': 0.7855026095213731, '2019-03-01 00:00:00': 0.7824020845761124, '2019-04-01 00:00:00': 0.7899216735610429, '2019-05-01 00:00:00': 0.7919373401140887, '2019-06-01 00:00:00': 0.7907376714888356, '2019-07-01 00:00:00': 0.7937127383862557, '2019-08-01 00:00:00': 0.8044939941739666, '2019-09-01 00:00:00': 0.8175951987076252, '2019-10-01 00:00:00': 0.8260761607105328, '2019-11-01 00:00:00': 0.7428609148753197}, 'Variable 3': {'2014-02-01 00:00:00': 0.73, '2014-03-01 00:00:00': 0.7355555555555554, '2014-04-01 00:00:00': 0.7455737704918032, '2014-05-01 00:00:00': 0.7436856368563685, '2014-06-01 00:00:00': 0.7574631128034269, '2014-07-01 00:00:00': 0.7635718622603868, '2014-08-01 00:00:00': 0.7727908521079994, '2014-09-01 00:00:00': 0.7769265339645449, '2014-10-01 00:00:00': 0.7753264702560229, '2014-11-01 00:00:00': 0.7830953537143132, '2014-12-01 00:00:00': 0.7889819365901924, '2015-01-01 00:00:00': 0.7934957351470745, '2015-02-01 00:00:00': 0.8012213058591142, '2015-03-01 00:00:00': 0.7988738003009945, '2015-04-01 00:00:00': 0.7887425796020084, '2015-05-01 00:00:00': 0.7807696455386262, '2015-06-01 00:00:00': 0.79288861127886, '2015-07-01 00:00:00': 0.7963736702644841, '2015-08-01 00:00:00': 0.7950802967346366, '2015-09-01 00:00:00': 0.7879824042589837, '2015-10-01 00:00:00': 0.7843524429233086, '2015-11-01 00:00:00': 0.7734011480450893, '2015-12-01 00:00:00': 0.7686931271861905, '2016-01-01 00:00:00': 0.7729747210040915, '2016-02-01 00:00:00': 0.7763926895232527, '2016-03-01 00:00:00': 0.7791224016673299, '2016-04-01 00:00:00': 0.7913274312847312, '2016-05-01 00:00:00': 0.7870536783874261, '2016-06-01 00:00:00': 0.7876438559655086, '2016-07-01 00:00:00': 0.7841107109507285, '2016-08-01 00:00:00': 0.779283788408037, '2016-09-01 00:00:00': 0.7754239726653402, '2016-10-01 00:00:00': 0.7723372216713971, '2016-11-01 00:00:00': 0.7738705548293968, '2016-12-01 00:00:00': 0.7690945064681345, '2017-01-01 00:00:00': 0.7612730669722748, '2017-02-01 00:00:00': 0.7470147519075939, '2017-03-01 00:00:00': 0.7416106791440739, '2017-04-01 00:00:00': 0.7412884897823692, '2017-05-01 00:00:00': 0.7370302258055261, '2017-06-01 00:00:00': 0.73762424381125, '2017-07-01 00:00:00': 0.7440999459408121, '2017-08-01 00:00:00': 0.7552807176765437, '2017-09-01 00:00:00': 0.7602248433250394, '2017-10-01 00:00:00': 0.7581797855852043, '2017-11-01 00:00:00': 0.7505435623846199, '2017-12-01 00:00:00': 0.7524349026305672, '2018-01-01 00:00:00': 0.7499478666416979, '2018-02-01 00:00:00': 0.7419581507720641, '2018-03-01 00:00:00': 0.7435665435734004, '2018-04-01 00:00:00': 0.7408532038778578, '2018-05-01 00:00:00': 0.746312612970632, '2018-06-01 00:00:00': 0.7447532789816597, '2018-07-01 00:00:00': 0.740342197398116, '2018-08-01 00:00:00': 0.7409253606458336, '2018-09-01 00:00:00': 0.7367402728583945, '2018-10-01 00:00:00': 0.7373922202380955, '2018-11-01 00:00:00': 0.7379137774393577, '2018-12-01 00:00:00': 0.7363310189195232, '2019-01-01 00:00:00': 0.7273212013281132, '2019-02-01 00:00:00': 0.7118675421163595, '2019-03-01 00:00:00': 0.7066490285747843, '2019-04-01 00:00:00': 0.7003730179354327, '2019-05-01 00:00:00': 0.7057160177022014, '2019-06-01 00:00:00': 0.698675610626293, '2019-07-01 00:00:00': 0.6990652886575816, '2019-08-01 00:00:00': 0.7023210319724209, '2019-09-01 00:00:00': 0.7098568275154657, '2019-10-01 00:00:00': 0.7098854620182623, '2019-11-01 00:00:00': 0.7059083689601774}, 'Variable 4': {'2014-02-01 00:00:00': 0.74, '2014-03-01 00:00:00': 0.74, '2014-04-01 00:00:00': 0.723606557377049, '2014-05-01 00:00:00': 0.7257723577235771, '2014-06-01 00:00:00': 0.7359543074726319, '2014-07-01 00:00:00': 0.7343403590944573, '2014-08-01 00:00:00': 0.7433648628949967, '2014-09-01 00:00:00': 0.7449594111151099, '2014-10-01 00:00:00': 0.7507439113745216, '2014-11-01 00:00:00': 0.7640207150378528, '2014-12-01 00:00:00': 0.7740807198648756, '2015-01-01 00:00:00': 0.7796470942252629, '2015-02-01 00:00:00': 0.7881871710698463, '2015-03-01 00:00:00': 0.790658423695772, '2015-04-01 00:00:00': 0.7925948716624064, '2015-05-01 00:00:00': 0.7879450155148408, '2015-06-01 00:00:00': 0.7945037008766296, '2015-07-01 00:00:00': 0.7956231264802287, '2015-08-01 00:00:00': 0.7985405456250583, '2015-09-01 00:00:00': 0.8008591684006369, '2015-10-01 00:00:00': 0.800685735080474, '2015-11-01 00:00:00': 0.8025624356705747, '2015-12-01 00:00:00': 0.8040587814026247, '2016-01-01 00:00:00': 0.8052526630688649, '2016-02-01 00:00:00': 0.8062057310441018, '2016-03-01 00:00:00': 0.8049608223295611, '2016-04-01 00:00:00': 0.7979517108720217, '2016-05-01 00:00:00': 0.7863389062666964, '2016-06-01 00:00:00': 0.7770567615901753, '2016-07-01 00:00:00': 0.7656312651926812, '2016-08-01 00:00:00': 0.7604999303262361, '2016-09-01 00:00:00': 0.7563966933417224, '2016-10-01 00:00:00': 0.755116543281882, '2016-11-01 00:00:00': 0.7580947447530586, '2016-12-01 00:00:00': 0.7504727039353692, '2017-01-01 00:00:00': 0.7423755354668953, '2017-02-01 00:00:00': 0.743900824361153, '2017-03-01 00:00:00': 0.7451209128913961, '2017-04-01 00:00:00': 0.7400958953888739, '2017-05-01 00:00:00': 0.7400767137614234, '2017-06-01 00:00:00': 0.746062007474736, '2017-07-01 00:00:00': 0.7468496729869601, '2017-08-01 00:00:00': 0.7474797812725159, '2017-09-01 00:00:00': 0.7599845058399765, '2017-10-01 00:00:00': 0.7639877790391211, '2017-11-01 00:00:00': 0.7631901954395647, '2017-12-01 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0.706926169796117}, 'Variable 5': {'2014-02-01 00:00:00': 0.65, '2014-03-01 00:00:00': 0.6333333333333334, '2014-04-01 00:00:00': 0.6401639344262295, '2014-05-01 00:00:00': 0.6604336043360434, '2014-06-01 00:00:00': 0.6722037125178486, '2014-07-01 00:00:00': 0.6851591638476884, '2014-08-01 00:00:00': 0.6990379164574595, '2014-09-01 00:00:00': 0.7088818754248837, '2014-10-01 00:00:00': 0.7114502173590359, '2014-11-01 00:00:00': 0.7245687674808299, '2014-12-01 00:00:00': 0.7323209187042266, '2015-01-01 00:00:00': 0.7404128051669034, '2015-02-01 00:00:00': 0.7297437040994432, '2015-03-01 00:00:00': 0.7193372840872788, '2015-04-01 00:00:00': 0.7132558560426701, '2015-05-01 00:00:00': 0.706412048785298, '2015-06-01 00:00:00': 0.6989618760525225, '2015-07-01 00:00:00': 0.6971366199512274, '2015-08-01 00:00:00': 0.6936591807809778, '2015-09-01 00:00:00': 0.6868488265867368, '2015-10-01 00:00:00': 0.6854663098027436, '2015-11-01 00:00:00': 0.6843649210274496, '2015-12-01 00:00:00': 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0.7059909361290391, '2019-10-01 00:00:00': 0.7047927486567783, '2019-11-01 00:00:00': 0.7038341987676929}, 'Variable 7': {'2014-02-01 00:00:00': 0.74, '2014-03-01 00:00:00': 0.7455555555555556, '2014-04-01 00:00:00': 0.7637704918032787, '2014-05-01 00:00:00': 0.7658807588075881, '2014-06-01 00:00:00': 0.7700809138505474, '2014-07-01 00:00:00': 0.7646378697198369, '2014-08-01 00:00:00': 0.7634641486208515, '2014-09-01 00:00:00': 0.7602284605138899, '2014-10-01 00:00:00': 0.7486254369505038, '2014-11-01 00:00:00': 0.7534145798652732, '2014-12-01 00:00:00': 0.757043375022721, '2015-01-01 00:00:00': 0.7641210759203562, '2015-02-01 00:00:00': 0.7590162182160856, '2015-03-01 00:00:00': 0.761314033560453, '2015-04-01 00:00:00': 0.7714063175225095, '2015-05-01 00:00:00': 0.7711169078602121, '2015-06-01 00:00:00': 0.7688423068263266, '2015-07-01 00:00:00': 0.7650047135260325, '2015-08-01 00:00:00': 0.7619598902935452, '2015-09-01 00:00:00': 0.7575166855721088, '2015-10-01 00:00:00': 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0.6554962422121093, '2019-07-01 00:00:00': 0.6593241953075084, '2019-08-01 00:00:00': 0.6603693565819088, '2019-09-01 00:00:00': 0.6682954873034151, '2019-10-01 00:00:00': 0.6746363911469803, '2019-11-01 00:00:00': 0.6777091134232021}})

#Build the model
model = VAR(df, freq='MS')
aic, bic = [], []
o_range = np.arange(1, 11)
for i in o_range:
    result = model.fit(i)
    aic.append(result.aic)
    bic.append(result.bic)

lags_metrics_df = pd.DataFrame({'AIC': aic,
                                'BIC': bic},
                               index=range(1, len(aic) + 1))

best_model_order = lags_metrics_df.idxmin(axis=0)['AIC']
print(best_model_order)
print(lags_metrics_df)

Expected Output

The same code should provide the same output with the same data. I asked the same question in the streamlit community but suspect it might be something that is rather related to statsmodel than streamlit?

Output of import statsmodels.api as sm; sm.show_versions()

INSTALLED VERSIONS ------------------ Python: 3.11.9.final.0 statsmodels =========== Installed: 0.14.2 (C:\Users\Julia.Schulz\Desktop\streamlit thing with newest modules\streamlit-app-venv\Lib\site-packages\statsmodels) Required Dependencies ===================== cython: Not installed numpy: 2.1.0 (C:\Users\Julia.Schulz\Desktop\streamlit thing with newest modules\streamlit-app-venv\Lib\site-packages\numpy) scipy: 1.14.1 (C:\Users\Julia.Schulz\Desktop\streamlit thing with newest modules\streamlit-app-venv\Lib\site-packages\scipy) pandas: 2.2.2 (C:\Users\Julia.Schulz\Desktop\streamlit thing with newest modules\streamlit-app-venv\Lib\site-packages\pandas) dateutil: 2.9.0.post0 (C:\Users\Julia.Schulz\Desktop\streamlit thing with newest modules\streamlit-app-venv\Lib\site-packages\dateutil) patsy: 0.5.6 (C:\Users\Julia.Schulz\Desktop\streamlit thing with newest modules\streamlit-app-venv\Lib\site-packages\patsy) Optional Dependencies ===================== matplotlib: Not installed cvxopt: Not installed joblib: Not installed Developer Tools ================ IPython: Not installed jinja2: 3.1.4 (C:\Users\Julia.Schulz\Desktop\streamlit thing with newest modules\streamlit-app-venv\Lib\site-packages\jinja2) sphinx: Not installed pygments: 2.18.0 (C:\Users\Julia.Schulz\Desktop\streamlit thing with newest modules\streamlit-app-venv\Lib\site-packages\pygments) pytest: Not installed virtualenv: Not installed
bashtage commented 3 weeks ago

I suspect that the model you are fitting is not well idenfified so that the regressor matrix, consisting of lagged values, is near-singlar.

You have 70 rows and 9 columns. Each time you lag you get 9 regressors, and lose one observation, so that

lag | obs | reg | dof
1     69  |  10 |  59
2     68  |  19 |  48
3     67  |  28 |  39
4     66  |  37 |  29
5     65  |  46 |  19
6     64  |  55 |  9
7     63  |  64 |  --

At 7 lags you have perfect fit which is why the AIC and BIC get silly values. These are not meaningful and are down to numerical issues, which is you get you different values. For a data set of this size, one would not usually trust IC for more than 2 lags since the number of regressors should usually not be larger than sqrt of the sample size (which would really be 0 or 1, but could probably look at 2).

ejuliaschulz commented 3 weeks ago

Thanks @bashtage, that is reasonable. However, it still does not really explain the difference in outputs for 2 different environments, no? Other users can reproduce my results in different environments with exactly the same outcome. Sorry if I get this wrong!

bashtage commented 3 weeks ago

When you are in the area of numerical instability it is completely platform and compiler specific. In your case, the error covariance matrix is exactly 0 (theoretically) but will not be numerically exactly 0 (will be ~ 1e-15, but will look sort of like random tiny values), and will differ across platforms. This error variance is used in the AIC and BIC.

ejuliaschulz commented 3 weeks ago

ok, thanks for the explanation!