eventstudyr (version 1.1.4)

EventStudy: Estimates Equation (2) in Freyaldenhoven et al. (2021)

Description

EventStudy uses regression methods to estimate the effect of a policy on a given outcome.

Usage

EventStudy(
  estimator,
  data,
  outcomevar,
  policyvar,
  idvar,
  timevar,
  controls = NULL,
  proxy = NULL,
  proxyIV = NULL,
  FE = TRUE,
  TFE = TRUE,
  post,
  overidpost = 1,
  pre,
  overidpre = post + pre,
  normalize = -1 * (pre + 1),
  cluster = TRUE,
  anticipation_effects_normalization = TRUE
)

Value

A list that contains, under "output", the estimation output as an lm_robust object, and under "arguments", the arguments passed to the function.

Arguments

estimator

Accepts one of "OLS" or "FHS". If "OLS" is specified, implements Ordinary Least Squares. If "FHS" is specified, implements Instrumental Variables (IV) estimator proposed in Freyaldenhoven Hansen Shapiro (FHS, 2019).

data

Data frame containing the variables of interest.

outcomevar

Character indicating column of outcome variable y.

policyvar

Character indicating column of policy variable z.

idvar

Character indicating column of units.

timevar

Character indicating column of time periods.

controls

Optional character vector indicating a set of control variables q.

proxy

Character indicating column of variable that is thought to be affected by the confound but not by the policy. Should be specified if and only if estimator is specified as "FHS".

proxyIV

Character of column to be used as an instrument. Should be specified if and only if estimator is specified as "FHS". If NULL, defaults to the strongest lead of the policy variable based on the first stage.

FE

Logical indicating whether unit fixed-effects should be included. Defaults to TRUE.

TFE

Logical indicating whether time fixed-effects should be included. Defaults to TRUE.

post

Whole number indicating the number of periods in the past before which the past values of the policy are not supposed to affect the value of the outcome. Corresponds to M in equation (2) of Freyaldenhoven et al. (2021).

overidpost

Optional whole number indicating the number of event times after "post" to be included in estimation. Defaults to 1. Corresponds to L_M in equation (2) of Freyaldenhoven et al. (2021).

pre

Whole number indicating the number of periods in the future after which the future values of the policy are not supposed to affect the value of the outcome today. Corresponds to G in equation (2) of Freyaldenhoven et al. (2021).

overidpre

Optional whole number indicating the number of event times earlier than -"pre" to be included in estimation. Defaults to "post" + "pre". Corresponds to L_G in equation (2) of Freyaldenhoven et al. (2021).

normalize

Specifies the event-time coefficient to be normalized. Defaults to - pre - 1.

cluster

Logical indicating whether to use clustered errors by units. If FALSE, will use unclustered heteroskedasticity-robust standard errors. Defaults to TRUE. Must be TRUE if FE is TRUE.

anticipation_effects_normalization

If set to TRUE, runs the default process and switches coefficient to be normalized to 0 when there are anticipation effects. If set to FALSE, does not make the switch. Defaults to TRUE.

Examples

# A minimal example
eventstudy_model <-
  EventStudy(
    estimator = "OLS",
    data = example_data,
    outcomevar = "y_base",
    policyvar = "z",
    idvar = "id",
    timevar = "t",
    pre = 0, post = 3,
    normalize = -1
  )

### Access estimated model
eventstudy_model$output

summary(eventstudy_model$output)

### data.frame of estimates
estimatr::tidy(eventstudy_model$output)

### Access arguments
eventstudy_model$arguments

# A dynamic OLS model with anticipation effects and controls
eventstudy_model_dyn <-
  EventStudy(
    estimator = "OLS",
    data = example_data,
    outcomevar = "y_base",
    policyvar = "z",
    idvar = "id",
    timevar = "t",
    controls = "x_r",
    FE = TRUE, TFE = TRUE,
    post = 3, overidpost = 5,
    pre  = 2, overidpre  = 4,
    normalize = - 3,
    cluster = TRUE,
    anticipation_effects_normalization = TRUE
  )

summary(eventstudy_model_dyn$output)

# A static model
eventstudy_model_static <-
  EventStudy(
    estimator = "OLS",
    data = example_data,
    outcomevar = "y_jump_m",
    policyvar = "z",
    idvar = "id",
    timevar = "t",
    FE = TRUE, TFE = TRUE,
    post = 0, overidpost = 0,
    pre  = 0, overidpre  = 0,
    cluster = TRUE
  )

summary(eventstudy_model_static$output)

# A dynamic model with an unbalanced panel
data_unbal <- example_data[1:(nrow(example_data)-1),]  # drop last row to make unbalanced

eventstudy_model_unbal <-
 EventStudy(
    estimator = "OLS",
    data = data_unbal,
    outcomevar = "y_base",
    policyvar = "z",
    idvar = "id",
    timevar = "t",
    pre = 0, post = 3,
    normalize = -1
  )

summary(eventstudy_model_unbal$output)

# A dynamic model estimated using IV
eventstudy_model_iv <-
  EventStudy(
    estimator = "FHS",
    data = example_data,
    outcomevar = "y_base",
    policyvar = "z",
    idvar = "id",
    timevar = "t",
    proxy = "x_r",
    FE = TRUE, TFE = TRUE,
    post = 2, overidpost = 1,
    pre  = 0, overidpre  = 3,
    normalize = -1,
    cluster = TRUE
  )

summary(eventstudy_model_iv$output)