jointness: Calculation of of the jointness measures

In bdsm: Bayesian Dynamic Systems Modeling

Calculation of of the jointness measures

Description

This function calculates four types of the jointness measures based on the posterior model probabilities calculated using binomial and binomial-beta model prior. The four measures are:

1. HCGHM - for Hofmarcher et al. (2018) measure;
   

2. LS - for Ley & Steel (2007) measure;
   

3. DW - for Doppelhofer & Weeks (2009) measure;
   

4. PPI - for posterior probability of including both variables.
   The measures under binomial model prior will appear in a table above the diagonal, and the measure calculated under binomial-beta model prior below the diagonal.
   
   REFERENCES
   Doppelhofer G, Weeks M (2009) Jointness of growth determinants. Journal of Applied Econometrics., 24(2), 209-244. doi: 10.1002/jae.1046
   Hofmarcher P, Crespo Cuaresma J, Grün B, Humer S, Moser M (2018) Bivariate jointness measures in Bayesian Model Averaging: Solving the conundrum. Journal of Macroeconomics, 57, 150-165. doi: 10.1016/j.jmacro.2018.05.005
   Ley E, Steel M (2007) Jointness in Bayesian variable selection with applications to growth regression. Journal of Macroeconomics, 29(3), 476-493. doi: 10.1016/j.jmacro.2006.12.002

Usage

jointness(bma_list, measure = "HCGHM", rho = 0.5, app = 3)

Arguments

bma_list	bma object (the result of the bma function)
measure	Parameter for choosing the measure of jointness: HCGHM - for Hofmarcher et al. (2018) measure; LS - for Ley & Steel (2007) measure; DW - for Doppelhofer & Weeks (2009) measure; PPI - for posterior probability of including both variables.
rho	The parameter "rho" (\rho) to be used in HCGHM jointness measure (default rho = 0.5). Works only if HCGHM measure is chosen (Hofmarcher et al. 2018).
app	Parameter indicating the decimal place to which the jointness measures should be rounded (default app = 3).

Value

A table with jointness measures for all the pairs of regressors used in the analysis. Parameter "above" indicates what model prior is used for the values ABOVE the diagonal, and parameter "below" indicates what model prior is used for the values BELOW the diagonal.

Examples

library(magrittr)

data_prepared <- economic_growth[,1:7] %>%
   feature_standardization(timestamp_col = year, entity_col = country) %>%
   feature_standardization(timestamp_col = year, entity_col = country,
                           time_effects = TRUE, scale = FALSE)

model_space <- optimal_model_space(df = data_prepared, dep_var_col = gdp,
                                   timestamp_col = year, entity_col = country,
                                   init_value = 0.5)

bma_results <- bma(df = data_prepared, dep_var_col = gdp, timestamp_col = year,
entity_col = country, model_space = model_space, run_parallel = FALSE, dilution = 0)

jointness_table <- jointness(bma_results, measure = "HCGHM", rho = 0.5, app = 3)

bdsm documentation built on April 4, 2025, 1:06 a.m.