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/dports/finance/R-cran-urca/urca/man/
H A Dcajorls.Rd4 \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}
H A Dcajools.Rd4 \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}
H A Dalphaols.Rd4 \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}
H A Dca.jo.Rd22 \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
H A Dplotres.Rd4 \title{Graphical inspection of VECM residuals}
36 \concept{VECM Residuals Johansen Juselius}
H A Dcajolst.Rd26 effects. The VECM is then estimated and tested for cointegration rank
28 slot \code{"bp"}. Please note, that the \emph{transitory} VECM
H A Dlttest.Rd56 \concept{VECM Test Linear Trend Johansen Juselius}
/dports/finance/R-cran-vars/vars/man/
H A DlogLik.Rd18 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}
H A Dvec2var.Rd7 \title{Transform a VECM to VAR in levels}
28 model (VECM) into a level-VAR form. The rank of the matrix
84 \concept{VECM}
H A DPhi.Rd19 converted VECM to VAR.
68 level version of a VECM. However, a convergence to zero of
110 \concept{VECM}
H A Dnormality.Rd14 VAR(p) or of a VECM in levels.
82 \concept{VECM}
H A Dresiduals.Rd16 Returns the residuals of a VAR(p)-model or for a VECM in levels. For
H A Dirf.Rd21 VECM to VAR(p)) or a SVAR for \code{n.ahead} steps.
147 \concept{VECM}
/dports/math/gretl/gretl-2021d/addons/SVAR/
H A DSVAR_Cfuncs.inp17 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!")
/dports/math/gretl/gretl-2021d/lib/src/
H A Doptions.c79 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 },
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H A Djohansen.h66 #define effective_order(v) (v->order+(v->ci==VECM))
H A Dvar.c311 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 …]
/dports/math/py-statsmodels/statsmodels-0.13.1/docs/source/
H A Dvector_ar.rst16 :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
/dports/math/gretl/gretl-2021d/doc/tex_it/
H A Dvecm.tex5 \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
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/dports/math/gretl/gretl-2021d/doc/tex/
H A Dvecm.tex5 \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
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/vector_ar/tests/
H A Dtest_vecm.py21 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(
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/
H A Dapi.py108 from .vector_ar.vecm import VECM
/dports/math/gretl/gretl-2021d/share/scripts/misc/
H A Dhamilton.inp23 # Estimate VECM: lag order 12, cointegration rank 1
/dports/math/py-statsmodels/statsmodels-0.13.1/docs/source/release/
H A Dversion0.9.rst38 - 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
/dports/math/gretl/gretl-2021d/doc/tex_ru/
H A Dhp-estimate.tex124 (матрицу коинтеграции, следующую за оценкой VECM), \dollar{h}

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