Description Usage Arguments Value Author(s) References See Also Examples
Estimate the parameters of a Student's t vector autoregressive model
y_t = φ_0 + ∑_{i=1}^p Φ_i * y_{ti} + ε_t
to fit the given time series with missing values. If the time series does not contain missing values, the maximum likelihood (ML) estimation is done via the iterative EM algorithm until converge is achieved. With missing values, the stochastic EM algorithm is employed for the estimation (currently the maximum number of iterations will be executed without attempting to check early converge).
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Y 
Time series object coercible to either a numeric matrix (e.g., 
p 
Positive integer indicating the order of the VAR model. 
omit_missing 
Logical value indicating whether to use the omitvariable method, i.e.,
excluding the variables with missing data from the analysis (default is 
parallel_max_cores 
Positive integer indicating the maximum numer of cores used in the parallel computing,
only valid when 
verbose 
Logical value indicating whether to report in console the information of each iteration. 
return_iterates 
Logical value indicating whether to return the parameter estimates at each iteration (default is 
initial 
List with the initial values of the parameters of the VAR model, which may contain some or all of the following elements:

L 
Positive integer with the number of Markov chains (default is 10). 
maxiter 
Positive integer with the number of maximum iterations (default is 100). 
ptol 
Nonnegative number with the tolerance to determine the convergence of the (stochastic) EM method. 
partition_groups 
Logical value indicating whether to partition 
K 
Positive integer indicating the values of the step sizes in the stochastic EM method. 
A list with the following elements:

The estimate for ν. 

The estimate for φ_0. 

The estimate for Φ_i. 

The estimate for scatter matrix, i.e., Σ. 

A logical value indicating whether the method has converged. 

A number indicating how many iteration has been used. 

A numerical vector indicating how much is comsumed in each iteration. 

A number indicating how much time is comsumed overall. 

A number indicating how much time is comsumed for each iteration in average. 

A list as the records of parameter estimates of each iteration, only returned when 
Rui Zhou and Daniel P. Palomar
R. Zhou, J. Liu, S. Kumar, and D. P. Palomar, "Student’s t VAR Modeling with Missing Data via Stochastic EM and Gibbs Sampling," IEEE Trans. on Signal Processing, vol. 68, pp. 61986211, Oct. 2020.
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