ó įp7]c@`sŒdZddlmZmZmZddlZddlZddlmZddl Z ddl Z ddl Z ddlmZmZddlmZddlmZdd lmZdd lmZmZmZmZejjejje ƒƒZ!ejj"d d ƒZ#e j$e!ej%e#ƒZ&ejj"d d ƒZ'e j$ejj"e!e'ƒƒZ(de)fd„ƒYZ*de)fd„ƒYZ+de)fd„ƒYZ,de,fd„ƒYZ-de-fd„ƒYZ.de-fd„ƒYZ/de,fd„ƒYZ0de0fd„ƒYZ1de,fd„ƒYZ2d e2fd!„ƒYZ3d"e2fd#„ƒYZ4d$e2fd%„ƒYZ5d&e,fd'„ƒYZ6d(e6fd)„ƒYZ7d*e6fd+„ƒYZ8d,e6fd-„ƒYZ9d.e6fd/„ƒYZ:d0e)fd1„ƒYZ;d2e;fd3„ƒYZ<d4e;fd5„ƒYZ=d6e;fd7„ƒYZ>d8e;fd9„ƒYZ?d:e;fd;„ƒYZ@d<e;fd=„ƒYZAd>e;fd?„ƒYZBd@e;fdA„ƒYZCdBe;fdC„ƒYZDdDe;fdE„ƒYZEdFe;fdG„ƒYZFdHe;fdI„ƒYZGdJe;fdK„ƒYZHdLe;fdM„ƒYZIdNe;fdO„ƒYZJdPe;fdQ„ƒYZKdRe;fdS„ƒYZLdTe;fdU„ƒYZMdVe;fdW„ƒYZNdXe;fdY„ƒYZOdZe;fd[„ƒYZPd\e;fd]„ƒYZQd^e;fd_„ƒYZRd`e;fda„ƒYZSdbe;fdc„ƒYZTdde;fde„ƒYZUdfe;fdg„ƒYZVdhe;fdi„ƒYZWdje;fdk„ƒYZXdle;fdm„ƒYZYdne;fdo„ƒYZZdpe;fdq„ƒYZ[dre;fds„ƒYZ\dte;fdu„ƒYZ]dve;fdw„ƒYZ^dxe;fdy„ƒYZ_dze;fd{„ƒYZ`d|e;fd}„ƒYZad~e;fd„ƒYZbd€e;fd„ƒYZcd‚e;fdƒ„ƒYZdd„e;fd…„ƒYZed†e;fd‡„ƒYZfdˆe;fd‰„ƒYZgdŠe;fd‹„ƒYZhdŒe;fd„ƒYZidŽe;fd„ƒYZjde;fd‘„ƒYZkd’e;fd“„ƒYZld”e;fd•„ƒYZmd–e;fd—„ƒYZnd˜e;fd™„ƒYZodše;fd›„ƒYZpdœe;fd„ƒYZqdže;fdŸ„ƒYZrd e;fd”„ƒYZsd¢e;fd£„ƒYZtd¤e;fd„„ƒYZud¦e;fd§„ƒYZvdØe;fd©„ƒYZwdŖ„Zxd«„Zyd¬„Zzd­„Z{d®„Z|dƄZ}e j~jd°„ƒZ€d±„Zd²„Z‚eƒd³„Z„e j~j…d“„ƒZ†dS(µsG Tests for SARIMAX models Author: Chad Fulton License: Simplified-BSD i(tdivisiontabsolute_importtprint_functionN(t PLATFORM_WIN(tsarimaxttools(t arima_modeli(tresults_sarimax(t add_constant(t assert_equaltassert_almost_equalt assert_raisestassert_allclosetresultssresults_realgdpar_stata.csvsresults_sarimax_coverage.csvtTestSARIMAXStatsmodelscB`s\eZdZed„ƒZd„Zd„Zd„Zd„Zd„Z d„Z d„Z RS( s™ Test ARIMA model using SARIMAX class against statsmodels ARIMA class Notes ----- Standard errors are quite good for the OPG case. c C`stj|_|jd}tj|dd ƒ|_|jjddƒ|_tj |dd dddt d t ƒ|_ |j jddƒ|_ dS( Ntdatatorderitdispi’’’’ttrendtctsimple_differencingthamilton_representation(iii(iii( Rtwpi1_stationaryttruetarimatARIMAtmodel_atfittresult_aRtSARIMAXtTruetmodel_btresult_b(tclstendog((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyt setup_class-s   cC`st|jj|jjƒdS(N(R R tllfR(tself((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyt test_loglike:scC`st|jj|jjƒdS(N(R R taicR(R%((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyttest_aic=scC`st|jj|jjƒdS(N(R R tbicR(R%((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyttest_bic@scC`st|jj|jjƒdS(N(R R thqicR(R%((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyt test_hqicCscC`sM|jjjƒ}d|d|d|d(R t_cov_params_approxRtdiagonalR Rtbse(R%tcpaR5((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyttest_bseMscC`s6ddljj}|j|jƒ|j|jƒdS(Ni(tstatsmodels.tools._testingRt_testingtcheck_ttest_tvaluesR tcheck_ftest_pvalues(R%tsmt((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyt test_t_testSs( t__name__t __module__t__doc__t classmethodR#R&R(R*R,R1R7R=(((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR$s      tTestRealGDPARStatacB`s/eZdZed„ƒZd„Zd„ZRS(sĖ Includes tests of filtered states and standardized forecast errors. Notes ----- Could also test the usual things like standard errors, etc. but those are well-tested elsewhere. c C`sŠtjtdƒjƒdj}tj|dddddtƒ|_d d d d d ddddddddg }|jj |ƒ|_ dS(NtvalueiRi iRtnRg.*˜ēwŚ?gd ¤r° Č?gć™D{ďæg rŠmÖ …ægŲEņ¦¢ægØø?Qø¢½?g³1Dˆ Yš?gf4 fægœŖ÷+ĻIĮ?g‚Ėߔ$¶?gMŹQ> ʱ?g=é+å[»ægŖʐrų?(i ii( tnptlogtrealgdp_resultstdifftvaluesRRRtmodeltfilterR (R!tdlgdpR.((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR#ds   cC`sNxGtdƒD]9}ttjdd|d|jjj|ddƒq WdS(Ni isu%dR-gķµ ÷Ę°>(trangeR RGtilocR tfilter_resultstfiltered_state(R%ti((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyttest_filtered_stateqs cC`s/ttjdd|jjjdddƒdS(NitrstdiR-gü©ńŅMbP?(R RGRNR ROtstandardized_forecasts_error(R%((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyt!test_standardized_forecasts_errorys(R>R?R@RAR#RRRU(((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyRB[s tSARIMAXStataTestscB`s5eZd„Zd„Zd„Zd„Zd„ZRS(cC`s!t|jj|jddƒdS(Ntloglikei(R tresultR$R(R%((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR&‚s cC`s!t|jj|jddƒdS(NR'i(R RXR'R(R%((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR(ˆs cC`s!t|jj|jddƒdS(NR)i(R RXR)R(R%((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR*Žs cC`s[d|jjdtjtj|jjƒƒ|jjjd}t|jj|dƒdS(Niž’’’iii( RXR$RERFtnobs_effectiveR.tshapeR R+(R%R+((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR,”s   cC`s5|jj}d|j_|jj}t||ƒdS(N(RXRTtNonet_standardized_forecasts_errorR (R%t cython_sfet python_sfe((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyRUŸs   (R>R?R&R(R*R,RU(((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyRVs     RcB`s&eZdZed„ƒZd„ZRS(s? ARIMA model Stata arima documentation, Example 1 cO`s³||_|d}|jdtƒ|jdtƒtj|dd dd||Ž|_d|dd |d d }tj||d|d |d f}|jj|ƒ|_ dS(NRRRRiRRt params_arit params_meant params_matparams_variance(iii( Rt setdefaultRRRRJREtr_RKRX(R!RtargstkwargsR"t interceptR.((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR#¬s  cC`s5|jjddƒ}t|j|jjddƒdS(NRi’’’’R-gü©ńŅMbP?(RJRR R.RX(R%RX((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR1æs(R>R?R@RAR#R1(((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR¦stTestARIMAStationarycB`sAeZdZed„ƒZd„Zd„Zd„Zd„ZRS(sr Notes ----- Standard errors are very good for the OPG and complex step approximation cases. cC`stt|ƒjtjƒdS(N(tsuperRhR#RR(R!((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR#ĻscC`s…t|jjdƒt|jjtƒt|jjtƒt|jjd|j dddƒt|jjd|j dddƒdS(Ntopgit se_ar_opgR-gHƼšņ×z>it se_ma_opg( R RXtcov_typet_cov_approx_complex_stepRt_cov_approx_centeredtFalseR R5R(R%((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR7Õs $cC`s_|jjdtƒjƒd}t|d|jdddƒt|d|jdddƒdS( NR2gą?it se_ar_oimR-gHƼšņ×z>it se_ma_oim(RXR3RR4R R(R%R5((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyttest_bse_approxŽs cC`sV|jjjƒd}t|d|jdddƒt|d|jdddƒdS( Ngą?iRqR-gü©ńŅMbP?iRrg{®Gįz„?(RXtcov_params_oimR4R R(R%toim_bse((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyt test_bse_oimöscC`s‹|jjjƒd}|jj}|jƒd}tj|jd|jdf}t|dd!|ddƒt|dd!|ddƒdS( Ngą?t se_ar_robustt se_ma_robustiiR-g{®Gįz„?gü©ńŅMbP?(RXtcov_params_robust_oimR4tcov_params_robust_approxRERdRR (R%trobust_oim_bsetcpratrobust_approx_bsettrue_robust_bse((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyttest_bse_robustüs ( R>R?R@RAR#R7RsRvR(((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyRhĒs   tTestARIMADiffusecB`s8eZdZed„ƒZd„Zd„Zd„ZRS(s… Notes ----- Standard errors are very good for the OPG and quite good for the complex step approximation cases. cK`s;d|diRl( R RXRmRnRRoRpR R5R(R%((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR7s $cC`s_|jjdtƒjƒd}t|d|jdddƒt|d|jdddƒdS( NR2gą?iRqR-g-Cėā6?iRr(RXR3RR4R R(R%R5((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyRs"s cC`sY|jjƒjƒd}t|d|jdddƒt|d|jdddƒdS( Ngą?iRqR-g{®Gįz„?iRrgš™™™™™¹?(RXt_cov_params_oimR4R R(R%R5((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyRv:s(R>R?R@RAR#R7RsRv(((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR€s  tAdditiveSeasonalcB`s&eZdZed„ƒZd„ZRS(s^ ARIMA model with additive seasonal effects Stata arima documentation, Example 2 c O`sÅ||_tj|dƒ}|jdtƒ|jdtƒtj|dddd fdd||Ž|_d|d d|d d}tj||d |d |d f}|jj |ƒ|_ dS(NRRRRiiRRR_R`RaRb(iiii( RRERFRcRRRRJRdRKRX(R!RReRfR"RgR.((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR#Gs 'cC`s5|jjddƒ}t|j|jjddƒdS(NRi’’’’R-gü©ńŅMbP?(RJRR R.RX(R%RX((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR1[s(R>R?R@RAR#R1(((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR†AstTestAdditiveSeasonalcB`s8eZdZed„ƒZd„Zd„Zd„ZRS(s… Notes ----- Standard errors are very good for the OPG and quite good for the complex step approximation cases. cC`stt|ƒjtjƒdS(N(RiR‡R#Rt wpi1_seasonal(R!((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR#kscC`sˆt|jjdƒt|jjtƒt|jjtƒt|jjd|j dddƒt|jjdd!|j ddd ƒdS( NRjiRkR-gķµ ÷Ę°>iiRlgńh戵ųä>( R RXRmRnRRoRpR R5R(R%((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR7qs $ cC`sb|jjdtƒjƒd}t|d|jdddƒt|dd!|jd ddƒdS( NR2gą?iRqR-g-Cėā6?iiRr(RXR3RR4R R(R%R5((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyRs{s cC`s\|jjƒjƒd}t|d|jdddƒt|dd!|jddd ƒdS( Ngą?iRqR-g{®Gįz„?iiRrgš™™™™™¹?(RXR…R4R R(R%R5((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyRv”s(R>R?R@RAR#R7RsRv(((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR‡cs  tAirlinecB`s&eZdZed„ƒZd„ZRS(s` Multiplicative SARIMA model: "Airline" model Stata arima documentation, Example 3 c O`s”||_tj|dƒ}|jdtƒ|jdtƒtj|ddddd d ||Ž|_tj|d |d |d f}|jj |ƒ|_ dS(NRRRRiitseasonal_orderi RRDRatparams_seasonal_maRb(iii(iiii ( RRERFRcRRRRJRdRKRX(R!RReRfR"R.((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR#”s c C`sUtjƒCtjdƒ|jjddƒ}t|j|jjddƒWdQXdS(NtignoreRi’’’’R-g-Cėā6?(twarningstcatch_warningst simplefilterRJRR R.RX(R%RX((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR1³s   (R>R?R@RAR#R1(((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR‰›stTestAirlineHamiltoncB`s8eZdZed„ƒZd„Zd„Zd„ZRS(sr Notes ----- Standard errors are very good for the OPG and complex step approximation cases. cC`stt|ƒjtjƒdS(N(RiRR#Rtair2_stationary(R!((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR#ĘscC`s…t|jjdƒt|jjtƒt|jjtƒt|jjd|j dddƒt|jjd|j dddƒdS(NRjiRlR-gķµ ÷Ę°>itse_seasonal_ma_opg( R RXRmRnRRoRpR R5R(R%((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR7Ģs $cC`s_|jjdtƒjƒd}t|d|jdddƒt|d|jdddƒdS( NR2gą?iRrR-gķµ ÷Ę°>itse_seasonal_ma_oim(RXR3RR4R R(R%R5((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyRsÖs cC`sV|jjjƒd}t|d|jdddƒt|d|jdddƒdS(Ngą?iRrR-gš™™™™™¹?iR“(RXRtR4R R(R%Ru((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyRvńs(R>R?R@RAR#R7RsRv(((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR¾s  tTestAirlineHarveycB`s8eZdZed„ƒZd„Zd„Zd„ZRS(sr Notes ----- Standard errors are very good for the OPG and complex step approximation cases. cC`s#tt|ƒjtjdtƒdS(NR(RiR”R#RR‘Rp(R!((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR#scC`s…t|jjdƒt|jjtƒt|jjtƒt|jjd|j dddƒt|jjd|j dddƒdS(NRjiRlR-gķµ ÷Ę°>iR’( R RXRmRnRRoRpR R5R(R%((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR7s $cC`s_|jjdtƒjƒd}t|d|jdddƒt|d|jdddƒdS( NR2gą?iRrR-gķµ ÷Ę°>iR“(RXR3RR4R R(R%R5((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyRss cC`sV|jjjƒd}t|d|jdddƒt|d|jdddƒdS(Ngą?iRrR-gš™™™™™¹?iR“(RXRtR4R R(R%Ru((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyRv+s(R>R?R@RAR#R7RsRv(((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR”ųs  tTestAirlineStateDifferencingcB`sJeZdZed„ƒZd„Zd„Zd„Zd„Zd„Z RS(s… Notes ----- Standard errors are very good for the OPG and quite good for the complex step approximation cases. cC`s)tt|ƒjtjdtdtƒdS(NRR(RiR•R#RR‘Rp(R!((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR#:scC`s!t|jj|jddƒdS(NR)i(R RXR)R(R%((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR*As cC`sA|jjddddddƒ}t|j|jjddƒdS( NtmethodtnmtmaxiteričRiR-gü©ńŅMbP?(RJRR R.RX(R%RX((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR1Js!cC`s…t|jjdƒt|jjtƒt|jjtƒt|jjd|j dddƒt|jjd|j dddƒdS(NRjiRlR-gķµ ÷Ę°>iR’( R RXRmRnRRoRpR R5R(R%((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR7Ps $cC`s_|jjdtƒjƒd}t|d|jdddƒt|d|jdddƒdS( NR2gą?iRrR-g-Cėā6?iR“(RXR3RR4R R(R%R5((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyRsZs cC`sV|jjjƒd}t|d|jdddƒt|d|jdddƒdS(Ngą?iRrR-gš™™™™™¹?iR“(RXRtR4R R(R%Ru((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyRvts( R>R?R@RAR#R*R1R7RsRv(((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR•2s 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This is likely because we're comparing against the model where the regression coefficients are also estimated by MLE. So this test should be considered just a very basic "sanity" test. cC`sĆttjƒ}t|ddƒd}|d|d<|d|dR?R@RAR#R1R¬R&R(R*R7RsRv(((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR„Ūs       tTestFriedmanPredictcB`sJeZdZed„ƒZd„Zd„Zd„Zd„Zd„Z RS(sõ ARMAX model: Friedman quantity theory of money, prediction Stata arima postestimation documentation, Example 1 - Dynamic forecasts This follows the given Stata example, although it is not truly forecasting because it compares using the actual data (which is available in the example but just not used in the parameter MLE estimation) against dynamic prediction of that data. Here `test_predict` matches the first case, and `test_dynamic_predict` matches the second. cC`stt|ƒjtjƒdS(N(RiR­R#Rtfriedman2_predict(R!((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR#RscC`sdS(N((R%((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR&[scC`sdS(N((R%((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR(^scC`sdS(N((R%((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR*ascC`s$t|jjƒ|jddƒdS(Ntpredicti(R RXRÆR(R%((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyt test_predictds cC`sIt|jddƒdd}t|jjd|ƒ|jddƒdS(NRRšiitdynamictdynamic_predicti(tlenRR RXRÆ(R%R±((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyttest_dynamic_predictjs( R>R?R@RAR#R&R(R*R°R“(((sLlib/python2.7/site-packages/statsmodels/tsa/statespace/tests/test_sarimax.pyR­Fs     tTestFriedmanForecastcB`sJeZdZed„ƒZd„Zd„Zd„Zd„Zd„Z RS(s ARMAX model: Friedman quantity theory of money, forecasts Variation on: Stata arima postestimation documentation, Example 1 - Dynamic forecasts This is a variation of the Stata example, in which the endogenous data is actually made to be missing so that the predict command must forecast. 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