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MCMC Diagnostics for Estimated SEM

library(koma)

Overview

This vignette shows how to inspect MCMC diagnostics for a koma_estimate object:

Build a Small Model

equations <- "consumption ~ gdp + consumption.L(1) + interest_rate,
investment ~ gdp + investment.L(1) + interest_rate,
gdp == (consumption/gdp)*consumption + (investment/gdp)*investment"

exogenous_variables <- c("interest_rate")

sys_eq <- system_of_equations(
    equations = equations,
    exogenous_variables = exogenous_variables
)

dates <- list(
    estimation = list(start = c(1996, 1), end = c(2019, 4)),
    forecast = list(start = c(2023, 1), end = c(2023, 4))
)

Prepare Data and Estimate

data("small_open_economy")

series <- unique(c(sys_eq$endogenous_variables, sys_eq$exogenous_variables))
ts_data <- small_open_economy[series]

ts_data <- lapply(ts_data, function(x) {
    as_ets(x, series_type = "level", method = "diff_log")
})
ts_data$interest_rate <- as_ets(
    ts_data$interest_rate,
    series_type = "rate",
    method = "none"
)

set.seed(123)
estimates <- estimate(
  ts_data = ts_data,
  sys_eq = sys_eq,
  dates = dates,
  options = list(gibbs = list(ndraws = 200))
)

Trace Plots

Trace plots help detect non-stationary behavior and abrupt jumps in the chain.

if (requireNamespace("ggplot2", quietly = TRUE)) {
    trace_plot(
        estimates,
        variables = c("consumption", "investment"),
        params = c("beta", "gamma"),
        thin = 2,
        max_draws = 100
    )
}

Running Means

Running means make long-run stabilization of posterior draws easier to assess.

if (requireNamespace("ggplot2", quietly = TRUE)) {
    running_mean_plot(
        estimates,
        variables = c("consumption", "investment"),
        params = c("beta", "gamma"),
        grace_draws = 25
    )
}

Autocorrelation Function (ACF) Plots

ACF plots highlight serial dependence in posterior draws by lag.

if (requireNamespace("ggplot2", quietly = TRUE)) {
    acf_plot(
        estimates,
        variables = c("consumption", "investment"),
        params = c("beta", "gamma"),
        max_lag = 20
    )
}

Interactive Diagnostics

If plotly is installed, all diagnostic plots can be returned as interactive objects by setting interactive = TRUE.

if (requireNamespace("ggplot2", quietly = TRUE) &&
    requireNamespace("plotly", quietly = TRUE)) {
    acf_plot(
        estimates,
        variables = "consumption",
        params = "beta",
        interactive = TRUE
    )
}

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.