plotResiduals#

Purpose#

Plot residual diagnostics: time series plot, autocorrelation function (ACF), and histogram. Three panels per variable.

Format#

plotResiduals(result)#

Parameters:: result (struct varResult) – Estimation result containing residuals.

Examples#

VAR Residual Diagnostics#

library timeseries;

fname = getGAUSSHome("pkgs/timeseries/examples/data/us_macro_quarterly.csv");
data = loadd(fname, "gdp_growth + cpi_inflation + fed_funds");

ctl = varControlCreate();
ctl.p = 4;
ctl.quiet = 1;

rv = varFit(data, ctl);

// 3 diagnostic panels per variable
plotResiduals(rv);

Remarks#

Three panels per variable:

Time plot — residuals over time with a zero line. Look for patterns, structural breaks, or volatility clustering. If the residuals look random, the model captures the serial dependence.
ACF — autocorrelation at lags 1 through 20. The red dashed lines show the 95% significance bounds (\(\pm 1.96 / \sqrt{T}\)). Spikes beyond these bounds indicate remaining autocorrelation that the model didn’t capture.
Histogram — distribution of residuals. Should look approximately normal and centered at zero. Heavy tails suggest outliers or non-normality.

Multivariate: For models with m > 1 variables, each variable opens a separate plot window with its own 3-panel diagnostic display.

What to look for:

ACF spikes at seasonal lags (12, 24 for monthly data) → add seasonal terms
Volatility clustering in the time plot → consider bvarSvFit()
Skewed histogram → model may be misspecified for extreme observations