Setup for DiD with a Continuous Treatment

A function that creates a pte_params object, adding several different variables that are needed when there is a continuous treatment.

Usage

setup_pte_cont(
  yname,
  gname,
  tname,
  idname,
  data,
  xformula = ~1,
  target_parameter,
  aggregation,
  treatment_type,
  required_pre_periods = 1,
  anticipation = 0,
  base_period = "varying",
  cband = TRUE,
  alp = 0.05,
  boot_type = "multiplier",
  weightsname = NULL,
  gt_type = "att",
  biters = 100,
  cl = 1,
  dname,
  dvals = NULL,
  degree = 1,
  num_knots = 0,
  ...
)

Arguments

yname

Name of outcome in data

gname

Name of group in data

tname

Name of time period in data

idname

Name of id in data

data

balanced panel data

xformula

A formula for additional covariates. This is not currently supported.

target_parameter

Two options are "level" and "slope". In the first case, the function will report level effects, i.e., ATT's. In the second case, the function will report slope effects, i.e., ACRT's

aggregation

"dose" averages across timing-groups and time periods and provides results as a function of the dose. "eventstudy" averages across timing-groups and doses and reports results as a function of the length of exposure to the treatment.

"none" is a stub for reporting fully disaggregated results that can be processed as desired by the user. This is not currently supported though.

The combination of the arguments target_parameter and aggregation strongly affects the behavior of the function (and target of the analysis). For example, setting target_parameter="level" and aggregation="eventstudy" is effectively the same thing as binarizing the treatment (i.e., where units are considered treated if they experience any positive amount of the treatment) and reporting an event study.

treatment_type

"continuous" or "discrete" depending on the nature of the treatment. Default is "continuous". "discrete" is not yet supported.

required_pre_periods

The number of required pre-treatment periods to implement the estimation strategy. Default is 1.

anticipation

how many periods before the treatment actually takes place that it can have an effect on outcomes

base_period

The type of base period to use. This only affects the numeric value of results in pre-treatment periods. Results in post-treatment periods are not affected by this choice. The default is "varying", where the base period will "back up" to the immediately preceding period in pre-treatment periods. The other option is "universal" where the base period is fixed in pre-treatment periods to be the period right before the treatment starts. "Universal" is commonly used in difference-in-differences applications, but can be unnatural for other identification strategies.

cband

whether or not to report a uniform (instead of pointwise) confidence band (default is TRUE)

alp

significance level; default is 0.05

boot_type

which type of bootstrap to use

weightsname

The name of the column that contains sampling weights. The default is NULL, in which case no sampling weights are used.

gt_type

which type of group-time effects are computed. The default is "att". Different estimation strategies can implement their own choices for gt_type

biters

number of bootstrap iterations; default is 100

cl

number of clusters to be used when bootstrapping; default is 1

dname

The name of the treatment variable in the data. The functionality of cont_did is different from the did package in that the treatment variable is the "amount" of the treatment in a particular period, rather than gname which gives the time period when a unit becomes treated. The dname variable should, for a particular unit, be constant across time periods—even in pre-treatment periods. For units that never participate in the treatment, the amount of the treatment may not be defined in some applications—it is ignored in this function.

dvals

an optional argument specifying which values of the treatment to evaluate ATT(d) and/or ACRT(d). If no values are supplied, then the default behavior is to set dvals to be the 1st to 99th percentiles of the dose among units that experience any positive dose.

degree

The degree of the B-Spline used in estimation. The default is 3, which in combination with the default choice for the num-knots, leads to fitting models for the group of treated units that only that is a cubic polynomial in the dose. Setting degree=1 will lead to a linear model, while setting degree=2 will lead to a quadratic model.

num_knots

The number of knots to include for the B-Spline. The default is 0 so that the spline is global (i.e., this will amount to fitting a global polynomial). There is a bias-variance tradeoff for including more or less knots.

...

additional arguments

Value

pte_params object