se_compare: Compare different kinds of standard errors

In speccurvieR: Easy, Fast, and Pretty Specification Curve Analysis

Compare different kinds of standard errors

Description

se_compare() takes in a regression formula (with or without fixed effects), data, and the types of standard errors desired, including clustered, heteroskedasticity-consistent, and bootstrapped. It then returns a data frame with coefficient and standard error estimates for easy comparison and plotting.

Usage

se_compare(
  formula,
  data,
  weights = NULL,
  types = "all",
  cluster = NULL,
  clusteredOnly = FALSE,
  fixedEffectsOnly = FALSE,
  bootSamples = NULL,
  bootSampleSize = NULL
)

Arguments

formula	A string containing a regression formula, with or without fixed effects.
data	A data frame containing the variables provided in 'formula' and any clustering variables passed to 'cluster'.
weights	Optional string with the column name in 'data' that contains weights.
types	A string or vector of strings specifying what types of standard errors are desired. Defaults to "all". The following types are supported for non-fixed effects models: With clustering: "HC0, "HC1", "HC2", "HC3". Without clustering: "iid" (i.e. normal standard errors), "HC0, "HC1", "HC2", "HC3", "HC4", "HC4m", "HC5", "bootstrapped". The following types are supported for fixed effects models: With clustering: "CL_FE" (clustered by fixed effects, i.e. the default standard errors reported by 'feols()' if no clusters are supplied), if clusters are supplied then the conventional clustered standard errors from 'feols()' are estimated for each clustering variable. Two- way clustered standard errors are not supported at this time. Without clustering: "HC0, "HC1", "HC2", "HC3", "HC4", "HC4m", "HC5", "bootstrapped".
cluster	A string or vector of strings specifying variables present in 'data' to be used for clustering standard errors.
clusteredOnly	A boolean indicating whether only standard errors with clustering should be estimated, defaults to 'FALSE'.
fixedEffectsOnly	A boolean indicating whether only standard errors for fixed effects models should be estimated, defaults to 'FALSE'.
bootSamples	An integer or vector of integers indicating how many times the model should be estimated with a random subset of the data. If a vector then every combination of 'bootSamples' and 'bootSampleSize' are estimated.
bootSampleSize	An integer or vector of integers indicating how many observations are in each random subset of the data. If a vector then every combination of 'bootSamples' and 'bootSampleSize' are estimated.

Value

A data frame where row represents an independent variable in the model and each column a type of standard error. Coefficient estimates for each variable are also included (column '"estimate"' for non-fixed effects model and column '"estimate_FE"' for fixed effects models). Columns are automatically named to specify the standard error type.

Some examples:

"iid" = normal standard errors, i.e. assuming homoskedasticity

"CL_FE" = standard errors clustered by fixed effects

"bootstrap_k8n300_FE" = bootstrapped standard errors for a fixed effects model where 'bootSamples = 8' and 'bootSampleSize = 300'

"CL_Depth_ID_FE" = standard errors clustered by the variable "Depth_ID" for a model with fixed effects

"HC0_Sta_ID" = HC0 standard errors clustered by the variable "Sta_ID"

Note: for fixed effects models the "(Intercept)" row will be all 'NA' because the intercept is not reported by 'feols()' when fixed effects are present.

Examples

se_compare(formula = "Salnty ~ T_degC + ChlorA + O2Sat | Sta_ID",
           data = bottles, types = "all", cluster = c("Depth_ID", "Sta_ID"),
           fixedEffectsOnly = FALSE, bootSamples=c(4, 8, 10),
           bootSampleSize=c(300, 500))

se_compare(formula = "Salnty ~ T_degC + ChlorA + O2Sat", data = bottles,
           types = "bootstrapped", bootSamples = c(8, 10),
           bootSampleSize = c(300, 500))

se_compare(formula = "Salnty ~ T_degC + ChlorA", data = bottles,
           types = c("HC0", "HC1", "HC3"))

speccurvieR documentation built on Oct. 10, 2024, 1:08 a.m.