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Descriptive and causal variance decompositions

Source: R/ineqx.R
ineqx.Rd

The ineqx package implements Rosche (202X). [...]

Usage

ineqx(
  treat = NULL,
  post = NULL,
  y,
  ystat = "Var",
  group = NULL,
  time = NULL,
  weights = NULL,
  controls = NULL,
  decomp = "post",
  ref = NULL,
  AME_mu = NULL,
  AME_sigma = NULL,
  dat
)

Arguments

treat

Character string. Treatment variable. Values must be 0/1

post

Character string. Before/after variable. Values must be 0/1

y

Character string. Dependent variable. Variable must be continuous.

ystat

Character string. Either "Var" (default) or "CV2". Choose to analyze (the effect of x on) the variance or squared coefficient of variation.

group

Character string. Variable must be categorical. Grouping variable to decompose variance into within- and between-group components.

time

Character string. c.x with specify penalized splines, i.x will specify dummies. Time variable to analyze change over time.

weights

Character string. Weight variable.

controls

Character vector with additional control variables. E.g. c("c.age", "i.sex", ...)

decomp

Character string. Either "post" (default) or "effect".

ref

Number, vector, or list. Counterfactual reference point. See details.

AME_mu

Dataframe with average marginal effects (Mu)

AME_sigma

Dataframe with average marginal effects (Sigma)

dat

Dataframe

Value

List with eight elements: "vars", "dMu", "dSigma", "dW", "dB", "dC", "dP", "dT". See details.

Details

The main function of ineqx. [...]

Counterfactual reference point (ref)

ref can either be a number, c(number, "variable name"), or list(n=c(...), mu=c(...), sigma=c(...), beta=c(...), lambda=c(...)). [...]

Return

...

Author

Benjamin Rosche <benjamin.rosche@gmail.com>

Examples

data(incdat)
# Descriptive variance decomposition
decomp_descr <- ineqx(y="inc", group="group", time="i.year", ref=1, dat=incdat)
#> Performing decomposition ...
#> Done.
plot(decomp_descr, type="dT")


# Causal variance decomposition
decomp_treat <- ineqx(treat="x", post="t", y="inc", group="group", time="i.year", ref=1, dat=incdat)
#> Running variance function regression ...
#> GAMLSS-RS iteration 1: Global Deviance = 8406.392 
#> GAMLSS-RS iteration 2: Global Deviance = 8406.348 
#> GAMLSS-RS iteration 3: Global Deviance = 8406.348 
#> Computing average marginal effects ...
#> Performing decomposition ...
#> Done.
plot(decomp_treat, type="dT")