gen_var {bvartools} | R Documentation |
Vector Autoregressive Model Input
Description
gen_var
produces the input for the estimation of a vector autoregressive (VAR) model.
Usage
gen_var(
data,
p = 2,
exogen = NULL,
s = NULL,
deterministic = "const",
seasonal = FALSE,
structural = FALSE,
tvp = FALSE,
sv = FALSE,
fcst = NULL,
iterations = 50000,
burnin = 5000
)
Arguments
data |
a time-series object of endogenous variables. |
p |
an integer vector of the lag order (default is |
exogen |
an optional time-series object of external regressors. |
s |
an optional integer vector of the lag order of the external regressors (default is |
deterministic |
a character specifying which deterministic terms should
be included. Available values are |
seasonal |
logical. If |
structural |
logical indicating whether data should be prepared for the estimation of a structural VAR model. |
tvp |
logical indicating whether the model parameters are time varying. |
sv |
logical indicating whether time varying error variances should be estimated by employing a stochastic volatility algorithm. |
fcst |
integer. Number of observations saved for forecasting evaluation. |
iterations |
an integer of MCMC draws excluding burn-in draws (defaults to 50000). |
burnin |
an integer of MCMC draws used to initialize the sampler (defaults to 5000). These draws do not enter the computation of posterior moments, forecasts etc. |
Details
The function produces the data matrices for vector autoregressive (VAR) models, which can also include unmodelled, non-deterministic variables:
A_0 y_t = \sum_{i=1}^{p} A_i y_{t - i} +
\sum_{i=0}^{s} B_i x_{t - i} +
C D_t + u_t,
where
y_t
is a K-dimensional vector of endogenous variables,
A_0
is a K \times K
coefficient matrix of contemporaneous endogenous variables,
A_i
is a K \times K
coefficient matrix of endogenous variables,
x_t
is an M-dimensional vector of exogenous regressors and
B_i
its corresponding K \times M
coefficient matrix.
D_t
is an N-dimensional vector of deterministic terms and
C
its corresponding K \times N
coefficient matrix.
p
is the lag order of endogenous variables, s
is the lag
order of exogenous variables, and u_t
is an error term.
If an integer vector is provided as argument p
or s
, the function will
produce a distinct model for all possible combinations of those specifications.
If tvp
is TRUE
, the respective coefficients
of the above model are assumed to be time varying. If sv
is TRUE
,
the error covariance matrix is assumed to be time varying.
Value
An object of class 'bvarmodel'
, which contains the following elements:
data |
A list of data objects, which can be used for posterior simulation. Element
|
model |
A list of model specifications. |
References
Chan, J., Koop, G., Poirier, D. J., & Tobias, J. L. (2019). Bayesian Econometric Methods (2nd ed.). Cambridge: University Press.
Lütkepohl, H. (2006). New introduction to multiple time series analysis (2nd ed.). Berlin: Springer.
Examples
# Load data
data("e1")
e1 <- diff(log(e1))
# Generate model data
data <- gen_var(e1, p = 0:2, deterministic = "const")