var_estimate: VAR: Estimation In BGGM: Bayesian Gaussian Graphical Models

Description

Estimate VAR(1) models by efficiently sampling from the posterior distribution. This provides two graphical structures: (1) a network of undirected relations (the GGM, controlling for the lagged predictors) and (2) a network of directed relations (the lagged coefficients). Note that in the graphical modeling literature, this model is also known as a time series chain graphical model \insertCiteabegaz2013sparseBGGM.

Usage

 1 2 3 4 5 6 7 8 9 var_estimate( Y, rho_sd = 0.5, beta_sd = 1, iter = 5000, progress = TRUE, seed = 1, ... )

Arguments

 Y Matrix (or data frame) of dimensions n (observations) by p (variables). rho_sd Numeric. Scale of the prior distribution for the partial correlations, approximately the standard deviation of a beta distribution (defaults to 0.50). beta_sd Numeric. Standard deviation of the prior distribution for the regression coefficients (defaults to 1). The prior is by default centered at zero and follows a normal distribution \insertCite@Equation 9, @sinay2014bayesianBGGM iter Number of iterations (posterior samples; defaults to 5000). progress Logical. Should a progress bar be included (defaults to TRUE) ? seed An integer for the random seed (defaults to 1). ... Currently ignored.

Details

Each time series in Y is standardized (mean = 0; standard deviation = 1).

Value

An object of class var_estimate containing a lot of information that is used for printing and plotting the results. For users of BGGM, the following are the useful objects:

• beta_mu A matrix including the regression coefficients (posterior mean).

• pcor_mu Partial correlation matrix (posterior mean).

• fit A list including the posterior samples.

Note

Regularization:

A Bayesian ridge regression can be fitted by decreasing beta_sd (e.g., beta_sd = 0.25). This could be advantageous for forecasting (out-of-sample prediction) in particular.

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Examples

 1 2 3 4 5 6 7 # data Y <- subset(ifit, id == 1)[,-1] # use alias (var_estimate also works) fit <- var_estimate(Y, progress = FALSE) fit

BGGM documentation built on Aug. 20, 2021, 5:08 p.m.