combine_submodels: Generate a GVAR Model

In franzmohr/bgvars: Bayesian Inference of Global Vector Autoregressive and Gloal Vector Error Correction Models

Generate a GVAR Model

Description

Combines the country model results to a global VAR model and solves it.

Usage

combine_submodels(object, period = NULL, thin = 1)

Arguments

object	a list containing the results of the country estimates, usually, the result of a call to draw_posterior.
period	an integer of the time index for which the GVAR should be solved. Only used when time varying weights or parameters are used.
thin	an integer specifying the thinning factor for the MCMC output. Defaults to 1 to obtain the full MCMC sequence.

Value

An object of class "bgvar".

Examples

# Load data
data("gvar2019")

# Create regions
temp <- create_regions(country_data = gvar2019$country_data,
             weight_data = gvar2019$weight_data,
             region_weights = gvar2019$region_weights,
             regions = list(EA =  c("AT", "BE", "DE", "ES", "FI", "FR", "IT", "NL")),
             period = 3)

country_data <- temp$country_data
weight_data <- temp$weight_data
global_data = gvar2019$global_data

# Difference series to make them stationary
country_data <- diff_variables(country_data, variables = c("y", "Dp", "r"), multi = 100)
global_data <- diff_variables(global_data, multi = 100)

# Create time varying weights
weight_data <- create_weights(weight_data, period = 3, country_data = country_data)

# Generate specifications
model_specs <- create_specifications(
                 country_data = country_data,
                 global_data = global_data,
                 countries = c("US", "JP", "CA", "NO", "GB", "EA"), 
                 domestic = list(variables = c("y", "Dp", "r"), lags = 1),
                 foreign = list(variables = c("y", "Dp", "r"), lags = 1),
                 global = list(variables = c("poil"), lags = 1),
                 deterministic = list(const = TRUE, trend = FALSE, seasonal = FALSE),
                 iterations = 10,
                 burnin = 10)
# Note that the number of iterations and burnin draws should be much higher!
                                     
# Overwrite country-specific specifications
model_specs[["US"]][["domestic"]][["variables"]] <- c("y", "Dp", "r")
model_specs[["US"]][["foreign"]][["variables"]] <- c("y", "Dp")

# Create estimation objects
country_models <- create_models(country_data = country_data,
                                weight_data = weight_data,
                                global_data = global_data,
                                model_specs = model_specs)

# Add priors
models_with_priors <- add_priors(country_models,
                                 coef = list(v_i = 1 / 9, v_i_det = 1 / 10),
                                 sigma = list(df = 3, scale = .0001))

# Obtain posterior draws
object <- draw_posterior(models_with_priors)

# Solve GVAR
gvar <- combine_submodels(object)

franzmohr/bgvars documentation built on Sept. 2, 2023, 12:45 p.m.