/dports/finance/R-cran-urca/urca/man/ |
H A D | cajorls.Rd | 4 \title{OLS regression of VECM} 6 This function returns the OLS regressions of a restricted VECM, 8 containing the restricted VECM and a matrix object with the normalised 10 which equation in the VECM should be estimated and reported, or if 11 \code{"reg.number = NULL"} each equation in the VECM will be estimated 21 \item{reg.number}{The number of the equation in the VECM that should 23 within the VECM are estimated.} 31 restricted VECM and a matrix object with the normalised cointegrating 58 \concept{VECM OLS Johansen Juselius Cointegration Co-integration}
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H A D | cajools.Rd | 4 \title{OLS regression of VECM} 6 This function returns the OLS regressions of an unrestricted VECM, 8 certain number of which equation in the VECM should be estimated and 9 reported, or if \code{"reg.number=NULL"} each equation in the VECM 17 \item{reg.number}{The number of the equation in the VECM that should 19 within the VECM are estimated.} 57 \concept{VECM OLS Johansen Juselius Cointegration Co-integration}
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H A D | alphaols.Rd | 4 \title{OLS regression of VECM weighting matrix} 6 This functions estimates the \eqn{\bold{\alpha}} matrix of a VECM. 7 The following OLS regression of the R-form of the VECM is hereby 57 \concept{VECM OLS Loading Johansen Juselius Cointegration Co-integration}
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H A D | ca.jo.Rd | 22 \item{spec}{Determines the specification of the VECM, see details below.} 38 the following two specifications of a VECM exist: 54 impacts, hence if \code{spec="longrun"} is choosen, the above VECM is 57 The other VECM specification is of the form: 75 \code{spec="transitory"} the second VECM form is estimated. Please note 84 in the VECM. Please note, that the number of rows of the matrix
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H A D | plotres.Rd | 4 \title{Graphical inspection of VECM residuals} 36 \concept{VECM Residuals Johansen Juselius}
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H A D | cajolst.Rd | 26 effects. The VECM is then estimated and tested for cointegration rank 28 slot \code{"bp"}. Please note, that the \emph{transitory} VECM
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H A D | lttest.Rd | 56 \concept{VECM Test Linear Trend Johansen Juselius}
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/dports/finance/R-cran-vars/vars/man/ |
H A D | logLik.Rd | 18 Returns the log-Likelihood of a VAR, level-VECM, SVAR or SVEC object. 36 The log-likelihood of a VAR or level-VECM model is defined as: 75 \concept{VECM}
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H A D | vec2var.Rd | 7 \title{Transform a VECM to VAR in levels} 28 model (VECM) into a level-VAR form. The rank of the matrix 84 \concept{VECM}
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H A D | Phi.Rd | 19 converted VECM to VAR. 68 level version of a VECM. However, a convergence to zero of 110 \concept{VECM}
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H A D | normality.Rd | 14 VAR(p) or of a VECM in levels. 82 \concept{VECM}
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H A D | residuals.Rd | 16 Returns the residuals of a VAR(p)-model or for a VECM in levels. For
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H A D | irf.Rd | 21 VECM to VAR(p)) or a SVAR for \code{n.ahead} steps. 147 \concept{VECM}
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/dports/math/gretl/gretl-2021d/addons/SVAR/ |
H A D | SVAR_Cfuncs.inp | 17 function matrix C1mat( const matrix A, bool VECM[0], 21 are alpha and beta, which are used only if VECM is nonzero, that 30 if VECM == 0 34 errorif( !exists(jalpha) || !exists(jbeta), "Need cointegration params for C1 in VECM!")
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/dports/math/gretl/gretl-2021d/lib/src/ |
H A D | options.c | 79 c == VECM || \ 658 { VECM, OPT_A, "crt", 0 }, 659 { VECM, OPT_D, "seasonals", 0 }, 660 { VECM, OPT_F, "variance-decomp", 0 }, 661 { VECM, OPT_I, "impulse-responses", 1 }, 662 { VECM, OPT_N, "nc", 0 }, 663 { VECM, OPT_R, "rc", 0 }, 664 { VECM, OPT_C, "uc", 0 }, 665 { VECM, OPT_T, "ct", 0 }, 666 { VECM, OPT_V, "verbose", 0 }, [all …]
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H A D | johansen.h | 66 #define effective_order(v) (v->order+(v->ci==VECM))
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H A D | var.c | 311 int diff = (v->ci == VECM); in VAR_fill_X() 362 if (v->ci == VECM) { in VAR_fill_X() 500 if (!err && v->ci == VECM) { in VAR_make_lists() 692 if (v->ci == VECM) { in VAR_check_df_etc() 858 if (!err && var->ci == VECM) { in gretl_VAR_new() 1347 if (var->ci == VECM) { in gretl_VAR_get_forecast_matrix() 2513 if (v->ci == VECM) { in gretl_VAR_param_names() 2540 if (v->ci == VECM) { in gretl_VAR_param_names() 2609 int ecm = (var->ci == VECM); in transcribe_VAR_models() 4807 if (var->ci == VECM) { in gretl_VAR_serialize() [all …]
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/dports/math/py-statsmodels/statsmodels-0.13.1/docs/source/ |
H A D | vector_ar.rst | 16 :ref:`Vector Error Correction Models (VECM) <vecm>`. 370 Vector Error Correction Models (VECM) 374 one or more permanent stochastic trends (unit roots). A VECM models the 376 by the assumed number of stochastic trends. :class:`VECM` is used to 379 A VECM(:math:`k_{ar}-1`) has the following form 394 A VECM(:math:`k_{ar} - 1`) with deterministic terms has the form 439 VECM
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/dports/math/gretl/gretl-2021d/doc/tex_it/ |
H A D | vecm.tex | 5 \label{sec:VECM-intro} 76 \label{sec:VECM-rep} 82 \label{eq:VECM-VAR} 90 \label{eq:VECM} 95 Questa � la rappresentazione VECM della (\ref{eq:VECM-VAR}). 113 la (\ref{eq:VECM}) pu� essere scritta come 186 (\ref{eq:VECM}) come 188 \label{eq:VECM-poly} 204 $\alpha \cdot c$, si pu� scrivere la (\ref{eq:VECM-poly}) come 251 o in forma VECM [all …]
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/dports/math/gretl/gretl-2021d/doc/tex/ |
H A D | vecm.tex | 5 \label{sec:VECM-intro} 9 attraction of the Vector Error Correction Model (VECM) is that it 83 \label{sec:VECM-rep} 89 \label{eq:VECM-VAR} 96 \label{eq:VECM} 101 This is the VECM representation of (\ref{eq:VECM-VAR}). 156 where \texttt{p} is the number of lags in (\ref{eq:VECM-VAR}); 199 \label{eq:VECM-poly} 215 write (\ref{eq:VECM-poly}) as 261 or in VECM form [all …]
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/vector_ar/tests/ |
H A D | test_vecm.py | 21 VECM, 114 model = VECM( 159 model = VECM( 204 model = VECM( 1824 assert_raises(ValueError, VECM, univariate_data) 1826 model = VECM(endog, k_ar_diff=1, deterministic="n") 1836 vecm_res = VECM( 1866 vecm_res = VECM( 1943 res0 = VECM( 1951 res2 = VECM( [all …]
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/ |
H A D | api.py | 108 from .vector_ar.vecm import VECM
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/dports/math/gretl/gretl-2021d/share/scripts/misc/ |
H A D | hamilton.inp | 23 # Estimate VECM: lag order 12, cointegration rank 1
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/dports/math/py-statsmodels/statsmodels-0.13.1/docs/source/release/ |
H A D | version0.9.rst | 38 - VECM and enhancements to VAR (including cointegration test) 114 - VECM #3246 (Aleksandar Karakas GSOC, Josef Perktold) 115 - exog support in VAR, incomplete for extra results, part of VECM 174 Vector Error Correction Model (VECM) 177 The VECM framework developed during GSOC 2016 by Aleksandar Karakas adds support
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/dports/math/gretl/gretl-2021d/doc/tex_ru/ |
H A D | hp-estimate.tex | 124 (матрицу коинтеграции, следующую за оценкой VECM), \dollar{h}
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