PanelEstimate: PanelEstimate

In insongkim/PanelMatch: Matching Methods for Causal Inference with Time-Series Cross-Sectional Data

PanelEstimate

Description

PanelEstimate estimates a causal quantity of interest, including the average treatment effect for treated or control units (att and atc, respectively), or average treatment effect (ate), as specified in PanelMatch. This is done by estimating the counterfactual outcomes for each treated unit using matched sets. Users will provide matched sets that were obtained by the PanelMatch function and obtain point estimates via a weighted average computation with weighted bootstrap standard errors. Point estimates and standard errors will be produced for each period in the lead window specified by the lead argument from PanelMatch. Users may run multiple estimations by providing lists of each argument to the function. However, in this format, every argument must be explicitly specified in each configuration and must adhere to the same data types/structures outlined below. See the included code examples for more about how this functionality works.

Usage

PanelEstimate(
  sets,
  number.iterations = 1000,
  df.adjustment = FALSE,
  confidence.level = 0.95,
  moderator = NULL,
  data
)

Arguments

sets	A PanelMatch object attained via the PanelMatch function.
number.iterations	An integer value indicating the number of bootstrap iterations. The default is 1000.
df.adjustment	A logical value indicating whether or not a degree-of-freedom adjustment should be performed for the standard error calculation. The default is FALSE.
confidence.level	A numerical value specifying the confidence level and range of interval estimates for statistical inference. The default is .95.
moderator	The name of a moderating variable, provided as a character string. If a moderating variable is provided the returned object will be a list of PanelEstimate objects. The names of the list will reflect the different values of the moderating variable. More specifically, the moderating variable values will be converted to syntactically proper names using make.names.
data	The same time series cross sectional data set provided to the PanelMatch function used to produce the matched sets

Value

PanelEstimate returns a list of class ‘PanelEstimate’ containing the following components:

estimates	the point estimates of the quantity of interest for the lead periods specified
bootstrapped.estimates	the bootstrapped point estimate values
bootstrap.iterations	the number of iterations used in bootstrapping
method	refinement method used to create the matched sets from which the estimates were calculated
lag	See PanelMatch argument lag for more information.
lead	The lead window sequence for which PanelEstimate is producing point estimates and standard errors.
confidence.level	the confidence level
qoi	the quantity of interest
matched.sets	the refined matched sets used to produce the estimations
standard.error	the standard error(s) of the point estimates

Author(s)

In Song Kim <[e-mail]>, Erik Wang <[e-mail]>, Adam Rauh <[e-mail]>, and Kosuke Imai <[e-mail]>

References

Imai, Kosuke, In Song Kim, and Erik Wang (2018)

Examples

PM.results <- PanelMatch(lag = 4, time.id = "year", unit.id = "wbcode2", 
                         treatment = "dem", refinement.method = "mahalanobis", 
                         data = dem, match.missing = TRUE, 
                         covs.formula = ~ I(lag(tradewb, 1:4)) + I(lag(y, 1:4)), 
                         size.match = 5, qoi = "att",
                         outcome.var = "y", lead = 0:4, forbid.treatment.reversal = TRUE)
PE.results <- PanelEstimate(sets = PM.results, data = dem, number.iterations = 100)

insongkim/PanelMatch documentation built on June 10, 2022, 8 p.m.