pick_regime {gmvarkit} | R Documentation |
Pick regime parameters \upsilon_{m}
= (\phi_{m,0},
\phi_{m}
,\sigma_{m},\nu_{m})
Description
pick_regime
picks the regime-parameters from the given parameter vector.
Usage
pick_regime(
p,
M,
d,
params,
m,
model = c("GMVAR", "StMVAR", "G-StMVAR"),
constraints = NULL,
same_means = NULL,
weight_constraints = NULL,
structural_pars = NULL,
with_df = TRUE
)
Arguments
p |
a positive integer specifying the autoregressive order of the model. |
M |
|
d |
number of time series in the system, i.e. the dimension. |
params |
a real valued vector specifying the parameter values.
Above, In the GMVAR model, The notation is similar to the cited literature. |
m |
which component? |
model |
is "GMVAR", "StMVAR", or "G-StMVAR" model considered? In the G-StMVAR model, the first |
constraints |
a size |
same_means |
Restrict the mean parameters of some regimes to be the same? Provide a list of numeric vectors
such that each numeric vector contains the regimes that should share the common mean parameters. For instance, if
|
weight_constraints |
a numeric vector of length |
structural_pars |
If
See Virolainen (forthcoming) for the conditions required to identify the shocks and for the B-matrix as well (it is |
with_df |
should the degrees of freedom parameter (if any) be included? |
Details
Note that in some cases, a numeric vector of length zero is returned (see section Return)
Value
- For reduced form models:
-
- For GMVAR model:
a length
pd^2+d+d(d+1)/2
vector containing\upsilon_{m}
= (\phi_{m,0},
\phi_{m}
,\sigma_{m})
, where\phi_{m}
= (vec(A_{m,1}),...,vec(A_{m,1})
and\sigma_{m} = vech(\Omega_{m})
.- For StMVAR model:
a length
pd^2+d+d(d+1)/2 + 1
vector containing (\upsilon_{m}
,\nu_{m}
), where\nu_{m}
is dropped ifwith_df == FALSE
.- For G-StMVAR model:
Same as GMVAR for GMVAR type regimes and same as StMVAR for StMVAR type regimes.
- For structural models:
-
- For SGMVAR model:
a length
pd^2 + d
vector(\phi_{m,0},
\phi_{m}
)
.- For StMVAR model:
a length
pd^2 + d + 1
vector(\phi_{m,0},
\phi_{m}
,\nu_{m})
, where\nu_{m}
is dropped ifwith_df == FALSE
.- For G-StMVAR model:
Same as GMVAR for GMVAR type regimes and same as StMVAR for StMVAR type regimes.
- When AR constraints are employed:
As above, but without
\phi_{m}
.- When mean constaints are employed:
As above, but without
\phi_{m,0}
(which are\mu_m
in this case).- When lambda constraints are employed:
As above. Note that lambda parameters are not returned in any specification.
- When weight constraints are employed:
As above. Note that alpha parameters are not returned in any specification.
Note that if both, AR and mean constraints are employed, a lenght zero numeric vector is returned for
structural GMVAR type regimes (or structural StMVAR type regimes if with_df=FALSE
).
Warning
No argument checks!
References
Kalliovirta L., Meitz M. and Saikkonen P. 2016. Gaussian mixture vector autoregression. Journal of Econometrics, 192, 485-498.
Virolainen S. (forthcoming). A statistically identified structural vector autoregression with endogenously switching volatility regime. Journal of Business & Economic Statistics.
Virolainen S. 2022. Gaussian and Student's t mixture vector autoregressive model with application to the asymmetric effects of monetary policy shocks in the Euro area. Unpublished working paper, available as arXiv:2109.13648.
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