U Kv f`_@sdZddlZddlmZddlmZddlm Z ddl m m Z ddl m mmZddlm mmZejZGdddZGdddeZdS)z Impulse reponse-related code N)cache_readonlyc @szeZdZdZdddZdddZd d Zd d Zdddd ddddddddd ddZdddd dddddddd ddZ dS)BaseIRAnalysisz Base class for plotting and computing IRF-related statistics, want to be able to handle known and estimated processes N FcCs||_||_|j|j|j|_|_|_||_|dkrF|j}t |}||_ ||_ | ||_|rt|j||d|_n|j||d|_|jjdd|_|r|jjdd|_n|jjdd|_|s||_|rt|||_nt|||_|rt|j|_ nt|j!|_ dS)N)PrZaxis)"modelperiodsneqsZk_arZnobslagsTorderZsigma_ulaZcholeskyrsvarZma_repirfsZ svar_ma_rep svar_irfsZ orth_ma_rep orth_irfsZcumsum cum_effectsZsvar_cum_effectsorth_cum_effectsZlong_run_effects lr_effectsnpdotZsvar_lr_effectsorth_lr_effectsutilZ comp_matrixZvar_rep_AZcoefs)selfrrrr rvecmsigmarA/tmp/pip-unpacked-wheel-2v6byqio/statsmodels/tsa/vector_ar/irf.py__init__s2   zBaseIRAnalysis.__init__cCs|r |jS|r|jS|jSdSN)rrr)rorthrrrr _choose_irfsKs zBaseIRAnalysis._choose_irfscOstdSr NotImplementedErrorrargskwargsrrrcovSszBaseIRAnalysis.covcOstdSr r#r%rrrcum_effect_covVszBaseIRAnalysis.cum_effect_cov皙?)rrTasym) impulseresponsesignif plot_paramsfigsizesubplot_params plot_stderr stderr_typereplseed componentc  Cs|j} |j}|j}|r"|r"td|||}|r8d}n|rBd}nd}|dkrTd}n| dkrftdn| d krz|j|d }| d kr|j||| || d }| d kr|j||| || | d}| dkr|j||| || | d}| dkr|j ||| || | d}t j |||||jj |||||| d }|S)a Plot impulse responses Parameters ---------- orth : bool, default False Compute orthogonalized impulse responses impulse : {str, int} variable providing the impulse response : {str, int} variable affected by the impulse signif : float (0 < signif < 1) Significance level for error bars, defaults to 95% CI subplot_params : dict To pass to subplot plotting funcions. Example: if fonts are too big, pass {'fontsize' : 8} or some number to your taste. plot_params : dict figsize : (float, float), default (10, 10) Figure size (width, height in inches) plot_stderr : bool, default True Plot standard impulse response error bands stderr_type : str 'asym': default, computes asymptotic standard errors 'mc': monte carlo standard errors (use rpl) repl : int, default 1000 Number of replications for Monte Carlo and Sims-Zha standard errors seed : int np.random.seed for Monte Carlo replications component: array or vector of principal component indices zFor SVAR system, set orth=Falsez"Impulse responses (orthogonalized)zImpulse responses (structural)zImpulse responsesFN)r+mcsz1sz2sz3z!s  zIRAnalysis.errband_mcc Cs|j}|j} |||} |j} |j||| ||d} t|} || \}}}|dk rt || | fkrt dt | dt | t || | krt dn|}t | }t | }t| D]}t| D]}| dd||f|||dd|||ff| t||||||ff|dd||f<| dd||f|||dd|||ff| t||||||ff|dd||f<qq||fS)a IRF Sims-Zha error band method 1. Assumes symmetric error bands around mean. Parameters ---------- orth : bool, default False Compute orthogonalized impulse responses repl : int, default 1000 Number of MC replications signif : float (0 < signif < 1) Significance level for error bars, defaults to 95% CI seed : int, default None np.random seed burn : int, default 100 Number of initial simulated obs to discard component : neqs x neqs array, default to largest for each Index of column of eigenvector/value to use for each error band Note: period of impulse (t=0) is not included when computing principle component References ---------- Sims, Christopher A., and Tao Zha. 1999. "Error Bands for Impulse Response". Econometrica 67: 1113-1155. r!r5r^r6r_NComponent array must be  x ,Atleast one of the components does not existrS)rrr"r irf_resimrZnorm_signif_level_eigval_decomp_SZrshaper=strargmaxcopyrXsqrt)rr!rr5r/r6r_r7rrrr reqWeigvaklowerupperr[jrrrr?1s.        \`zIRAnalysis.err_band_sz1c Cs|j}|j} |||} |j} |j||| |dd} || \} }}|dk rt|| | fkrxtdt | dt | t || | krtdn|}t || d| | f}t |D]`}t | D]R}t | D]D}| |||||fddf| |dd||f||dd||f<qqqtj |dd }t|d |dtd|d |df}t| }t| }t | D]}t | D]v}| dd||f||ddd||f|dd||f<| dd||f||ddd||f|dd||f<qzqn||fS) a IRF Sims-Zha error band method 2. This method Does not assume symmetric error bands around mean. Parameters ---------- orth : bool, default False Compute orthogonalized impulse responses repl : int, default 1000 Number of MC replications signif : float (0 < signif < 1) Significance level for error bars, defaults to 95% CI seed : int, default None np.random seed burn : int, default 100 Number of initial simulated obs to discard component : neqs x neqs array, default to largest for each Index of column of eigenvector/value to use for each error band Note: period of impulse (t=0) is not included when computing principle component References ---------- Sims, Christopher A., and Tao Zha. 1999. "Error Bands for Impulse Response". Econometrica 67: 1113-1155. r\raNrbrcrdrSrrrT)rrr"r rerfrrgr=rhrirWrXsortroundrj)rr!rr5r/r6r_r7rrrr rermrnrogammapr[rr gamma_sortindxrprqrrrr@js8      F,    8@zIRAnalysis.err_band_sz2c Cs4|j}|j} |||} |j} |j||| |dd} t| || | f} t|D]@}t| D]2}t| |dddd|fj | ||ddf<qZqNt| | | | | f}t| | | | | f}t| | | f}tj| t d}|dk r,t || kr t dt | t|| | kr(t dn|}t| D]<}tj| |dd ||<t||\||<||<||<q4t|| d| | f}t|D]}d}t| D]}t| D]}|||||| |d| f| |dd||f||dd||f<|| dkr|||||| df| |dd||f||dd||f<qqqtj|dd }t|d |dtd|d |df}t| }t| }t| D]}t| D]v}| dd||f||ddd||f|dd||f<| dd||f||ddd||f|dd||f<qq||fS) a IRF Sims-Zha error band method 3. Does not assume symmetric error bands around mean. Parameters ---------- orth : bool, default False Compute orthogonalized impulse responses repl : int, default 1000 Number of MC replications signif : float (0 < signif < 1) Significance level for error bars, defaults to 95% CI seed : int, default None np.random seed burn : int, default 100 Number of initial simulated obs to discard component : vector length neqs, default to largest for each Index of column of eigenvector/value to use for each error band Note: period of impulse (t=0) is not included when computing principle component References ---------- Sims, Christopher A., and Tao Zha. 1999. "Error Bands for Impulse Response". Econometrica 67: 1113-1155. r\rarSNZdtypez"Component array must be of length rdrZrowvarrrT)rrr"r rerrWrXZravelr intsizer=rhrir(r eigval_decomprsrtrj)rr!rr5r/r6r_r7rrrr restackrvr[Z stack_covrmrnrorucrrrwrxrprqrrrrAsT    2   $   FJ,    8@zIRAnalysis.err_band_sz3c Cs|j}|j}t||||f}t|D]H}t|D]:}tj|dddd||fdd|||ddddf<q2q&t||||f}t|||df}tj||ftd} t|D]b}t|D]T}t|||ddddf\|||ddddf<|||dddf<| ||f<qq||| fS)z Returns ------- W: array of eigenvectors eigva: list of eigenvalues k: matrix indicating column # of largest eigenvalue for each c_i,j NrSrrzry) r rrrWrXr(r{rr}) rrer rZcov_holdr[rrrmrnrorrrrfs  :  TzIRAnalysis._eigval_decomp_SZcs2jfddfddtdjdDS)Ncsxd}t|D]f}|d|}|jkr2j|}n&tjj|}|d}|j|<t|j|}||}q |S)NrS) rXrRr Z matrix_powerrr rkronr)r[rYmidxZapowZpiece)Krrr_make_gs       zIRAnalysis.G.._make_gcsg|] }|qSrr).0r[)rrr 1sz IRAnalysis.G..rS)r rXrrr)rrrrrYsz IRAnalysis.Gc Cst|j}t|jj|}|j}||jd}t |jdD]t}|dkrRd}n&t ||j |d}||j |j}t t||j ||}||j|j|j} || ||<q@|S)NrSr)reyer rrr HrVrrXrrYrPrrQ) rIkPIkrrZr[apieceZCiZCibarbpiecerrrrU3s zIRAnalysis._orth_covc Cs t|j}t|jj|}d}||jd}t|jdD]}|dkr\||j |d}|r|dkrnd}nt ||}||j |j}t t||j ||j } | |j| j|j} || ||<q>|dkrt|jd|jdf||<q>||j |j||<q>|S)a Compute asymptotic standard errors for cumulative impulse response coefficients Parameters ---------- orth : bool Notes ----- eq. 3.7.7 (non-orth), 3.7.10 (orth) Returns ------- rrSrrT)rrr rrr rVrrXrYrrPrrrQrW) rr!rrFrZr[rZBnZBnbarrrrrr)Js(  zIRAnalysis.cum_effect_covc Cs$|j}|j}|j||||||ddS)zM IRF Monte Carlo integrated error bands of cumulative effect Tr])rrr`)rr!r5r/r6r_rrrrrrIwszIRAnalysis.cum_errband_mccCs|j}tt|j|j|}t|j}|rtt|j jt|j|}tt|||j }||j |j||j |jS||j |jSdS)z) Returns ------- N) rrrZtiler r rr rrrrPrQ)rr!ZlreZFinftyrZBinfZBinfbarrrr lr_effect_covs  zIRAnalysis.lr_effect_covcCstdd|j|dDS)Nc Ss"g|]}ttt|qSrtsaunvecrrkdiagrrrrrrsz%IRAnalysis.stderr..r<)rarrayr(rr!rrrrFs  zIRAnalysis.stderrcCstdd|j|dDS)Nc Ss"g|]}ttt|qSrrrrrrrsz0IRAnalysis.cum_effect_stderr..r<)rrr)rrrrcum_effect_stderrs  zIRAnalysis.cum_effect_stderrcCs"|j|d}ttt|S)Nr<)rrrrrkr)rr!r(rrrlr_effect_stderrs zIRAnalysis.lr_effect_stderrcCs tj||jd|jdftdS)NrTry)rrWr float)rrrrrrVszIRAnalysis._empty_covmcCsd|j}t|}t||}t|}|t||j|t|j||j}t |jt |Sr ) r rZelimination_matrixZcommutation_matrixrrrrr rLinv)rroZLkZKkkrBrrrrs    *z IRAnalysis.HcCstdSr r#rrrr fevd_tableszIRAnalysis.fevd_table)NrNFF)F)FFr,r*Nr\)FFr,r*Nr\N)FFr,r*Nr\N)FFr,r*Nr\N)F)Fr,r*Nr\)F)F)F)F)rKrLrMrNrr(r>r?r@rArfrrYrUr)rIrrFrrrVrrrrrrrOsX     9 @ Q  -     rO)rNZnumpyrZ numpy.linalgZlinalgr Z scipy.linalgrZstatsmodels.tools.decoratorsrZstatsmodels.tsa.tsatoolsrZtsatoolsZ"statsmodels.tsa.vector_ar.plottingZ vector_arrBZstatsmodels.tsa.vector_ar.utilrrmatrrOrrrrs   _