Plot method for emfx objects — plot.emfx • etwfe

Visualize the results of an emfx call.

Usage

# S3 method for class 'emfx'
plot(
  x,
  type = c("pointrange", "errorbar", "ribbon"),
  pch = 16,
  zero = TRUE,
  grid = TRUE,
  ref = -1,
  ...
)

Arguments

x: An emfx object.
type: Character. The type of plot display. One of "pointrange" (default), "errorbar", or "ribbon".
pch: Integer or character. Which plotting character or symbol to use (see points). Defaults to 16 (i.e., small solid circle). Ignored if type = "ribbon".
zero: Logical. Should 0-zero line be emphasized? Default is TRUE.
grid: Logical. Should a background grid be displayed? Default is TRUE.
ref: Integer. Reference line marker for event-study plot. Default is -1 (i.e., the period immediately preceding treatment). To remove completely, set to NA, NULL, or FALSE. Only used if the underlying object was computed using emfx(..., type = "event").
...: Additional arguments passed to tinyplot::tinyplot.

Value

No return value, called for side effect of producing a plot.

Examples

# \dontrun{
# We’ll use the mpdta dataset from the did package (which you’ll need to 
# install separately).

# install.packages("did")
data("mpdta", package = "did")

#
# Basic example
#

# The basic ETWFE workflow involves two consecutive function calls:
# 1) `etwfe` and 2) `emfx`

# 1) `etwfe`: Estimate a regression model with saturated interaction terms.
mod = etwfe(
  fml  = lemp ~ lpop, # outcome ~ controls (use 0 or 1 if none)
  tvar = year,        # time variable
  gvar = first.treat, # group variable
  data = mpdta,       # dataset
  vcov = ~countyreal  # vcov adjustment (here: clustered by county)
  )

# mod ## A fixest model object with fully saturated interaction effects.

# 2) `emfx`: Recover the treatment effects of interest.

(mod_es = emfx(mod, type = "event")) # dynamic ATE a la an event study
#> 
#>     Term event Estimate Std. Error     z Pr(>|z|)    S   2.5 %   97.5 %
#>  .Dtreat     0  -0.0332     0.0134 -2.48    0.013  6.3 -0.0594 -0.00701
#>  .Dtreat     1  -0.0573     0.0172 -3.34   <0.001 10.2 -0.0910 -0.02373
#>  .Dtreat     2  -0.1379     0.0308 -4.48   <0.001 17.0 -0.1982 -0.07751
#>  .Dtreat     3  -0.1095     0.0323 -3.39   <0.001 10.5 -0.1729 -0.04619
#> 
#> Type:  response 
#> Comparison: TRUE - FALSE
#> Columns: term, contrast, event, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high 
#> 

# Etc. Other aggregation type options are "simple" (the default), "group"
# and "calendar"

# To visualize results, use the native plot method (see `?plot.emfx`)
plot(mod_es)


# Notice that we don't get any pre-treatment effects with the default
# "notyet" treated control group. Switch to the "never" treated control
# group if you want this.
etwfe(
  lemp ~ lpop, tvar = year, gvar = first.treat, data = mpdta,
  vcov = ~countyreal,
  cgroup = "never"    ## <= use never treated group as control
  ) |>
  emfx("event") |>
  plot()


#
# Heterogeneous treatment effects
#

# Example where we estimate heterogeneous treatment effects for counties 
# within the 8 US Great Lake states (versus all other counties). 

gls = c("IL" = 17, "IN" = 18, "MI" = 26, "MN" = 27,
        "NY" = 36, "OH" = 39, "PA" = 42, "WI" = 55)

mpdta$gls = substr(mpdta$countyreal, 1, 2) %in% gls

hmod = etwfe(
  lemp ~ lpop, tvar = year, gvar = first.treat, data = mpdta, 
  vcov = ~countyreal,
  xvar = gls           ## <= het. TEs by gls
  )

# Heterogeneous ATEs (could also specify "event", etc.) 

emfx(hmod)
#> 
#>     Term .Dtreat   gls Estimate Std. Error     z Pr(>|z|)   S  2.5 %  97.5 %
#>  .Dtreat    TRUE FALSE  -0.0637     0.0376 -1.69   0.0906 3.5 -0.137 0.01007
#>  .Dtreat    TRUE  TRUE  -0.0472     0.0271 -1.74   0.0817 3.6 -0.100 0.00594
#> 
#> Type:  response 
#> Comparison: TRUE - FALSE
#> Columns: term, contrast, .Dtreat, gls, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high 
#> 

# To test whether the ATEs across these two groups (non-GLS vs GLS) are 
# statistically different, simply pass an appropriate "hypothesis" argument.

emfx(hmod, hypothesis = "b1 = b2")
#> 
#>   Term Estimate Std. Error      z Pr(>|z|)   S  2.5 % 97.5 %
#>  b1=b2  -0.0164     0.0559 -0.294    0.769 0.4 -0.126  0.093
#> 
#> Type:  response 
#> Columns: term, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high 
#> 

plot(emfx(hmod))


#
# Nonlinear model (distribution / link) families
#

# Poisson example

mpdta$emp = exp(mpdta$lemp)

etwfe(
  emp ~ lpop, tvar = year, gvar = first.treat, data = mpdta, 
  vcov = ~countyreal,
  family = "poisson"   ## <= family arg for nonlinear options
  ) |>
  emfx("event")
#> The variables '.Dtreat:first.treat::2006:year::2004', '.Dtreat:first.treat::2006:year::2005', '.Dtreat:first.treat::2007:year::2004', '.Dtreat:first.treat::2007:year::2005', '.Dtreat:first.treat::2007:year::2006', '.Dtreat:first.treat::2006:year::2004:lpop_dm' and 4 others have been removed because of collinearity (see $collin.var).
#> 
#>     Term event Estimate Std. Error       z Pr(>|z|)    S  2.5 % 97.5 %
#>  .Dtreat     0   -25.35       15.9 -1.5942    0.111  3.2  -56.5   5.82
#>  .Dtreat     1     1.09       41.8  0.0261    0.979  0.0  -80.9  83.09
#>  .Dtreat     2   -75.12       22.3 -3.3696   <0.001 10.4 -118.8 -31.43
#>  .Dtreat     3  -101.82       28.1 -3.6234   <0.001 11.7 -156.9 -46.75
#> 
#> Type:  response 
#> Comparison: TRUE - FALSE
#> Columns: term, contrast, event, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high 
#> 
# }