*! version 16.0 18march2021

[VAR-NR] hd_plot() -- plot impulse response(s) with confidence bands

Syntax

void
hd_plot(transmorphic scalar hd, struct var_struct scalar VAR, struct opt_struct scalar opts)

Description

hd_plot() plots the historical decomposition of the structural shocks
estimated using hd_funct() identified as specified in hd
for the SVAR stored in VAR.

If the SVAR was identified using short-run or long-run zero restrictions,
hd should be filled with the output from hd_funct().

If the SVAR was identified using [narrative] sign restrictions, hd should
be filled with the object indexed by the key string "median" in the associative
array output by sr_analysis_funct() with the "hd"
option specified. In this case, the median draw is plotted.

Options for historical decomposition plotting are set to defaults when
var_nr is run and are adjusted using
var_nr_options in Stata or using standard
structure.element structure syntax in Mata. Options affecting HD
plotting are outlined below. Note that the options that determine the
hd_funct() calculations also affect hd_plot(), so these
should be kept constant for the purpose of plotting.

Options

save_fmt specifies the file type that output plots should be saved as.
Outputs are automatically saved to the current working directory. If "none" is
specified, then the plots are displayed, but not saved. "none" is the default.

shck_plt allows the user to select just one shock for which historical
decomposition should be plotted. If "all" is specified, hd_plot() will
plot historical decomposition for all endogenous variables. If a variable name
or, in the [narrative] sign restrictions case, a shock name is specified, the
function will only plot the impulse response for the specified shock. "all" is
the default.

Remarks

hd_plot() can be called in Stata using function
var_nr_hd_plot.

For an overview of functions in the toolbox, see

[VAR-NR] var_nr Toolbox -- Stata functions and Mata functions.

Sources

Code follows Kilian and Lutkepohl's notation in
Structural Vector Autoregressive Analysis (2016).