PanelEstimate: PanelEstimate

Description Usage Arguments Value Author(s) References Examples

View source: R/PanelEstimate.R

Description

PanelEstimate estimates a causal quantity of interest, including the average treatment effect for treated or control units (att and atc, respectively), the average effect of treatment reversal on reversed units, 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.

Usage

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

Arguments

sets

A PanelMatch object attained via the PanelMatch function.

data

The same time series cross sectional data set provided to the PanelMatch function used to produce the matched sets.

number.iterations

If using bootstrapping for calculating standard errors, this is the number of bootstrap iterations. Provide as integer. If se.method is not equal to "bootstrap", this argument has no effect.

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. This parameter is only available for the bootstrap method of standard error calculation.

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.

se.method

Method used for calculating standard errors, provided as a character string. Users must choose between "bootstrap", "conditional", and "unconditional" methods. Default is "bootstrap". "bootstrap" uses a block bootstrapping procedure to calculate standard errors. The conditional method calculates the variance of the estimator, assuming independence across units but not across time. The unconditional method also calculates the variance of the estimator analytically, but makes no such assumptions about independence across units. When the quantity of interest is "att", "atc", or "art", all methods are available. Only "bootstrap" is available for the ate. See Section 3.4 of Imai, Kim, and Wang (2021) for more details.

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

se.method

The method used to calculate standard errors. This is the same as the argument provided to the function.

bootstrapped.estimates

the bootstrapped point estimate values, when applicable

bootstrap.iterations

the number of iterations used in bootstrapping, when applicable

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 <insong@mit.edu>, Erik Wang <haixiao@Princeton.edu>, Adam Rauh <amrauh@umich.edu>, and Kosuke Imai <imai@harvard.edu>

References

Imai, Kosuke, In Song Kim, and Erik Wang (2021)

Examples

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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)

PanelMatch documentation built on Sept. 5, 2021, 5:48 p.m.