RDSmoothnessBound function

Lower bound on smoothness constant M in sharp RD designs

Estimate a lower bound on the smoothness constant M and provide a lower confidence interval for it, using method described in supplement to Kolesár and Rothe (2018).

RDSmoothnessBound(
  object,
  s,
  separate = FALSE,
  multiple = TRUE,
  alpha = 0.05,
  sclass = "H"
)

Arguments

• object: An object of class "RDResults", typically a result of a call to RDHonest.

• s: Number of support points that curvature estimates should average over.

• separate: If TRUE, report estimates separately for data above and below cutoff. If FALSE, report pooled estimates.

• multiple: If TRUE, use multiple curvature estimates. If FALSE, only use a single curvature estimate using observations closest to the cutoff.

• alpha: determines confidence level 1-alpha.

• sclass: Smoothness class, either "T" for Taylor or "H"

  for Hölder class.

Returns

Returns a data frame wit the following columns:

• estimate: Point estimate for lower bounds for M.
• conf.low: Lower endpoint for a one-sided confidence interval for M

The data frame has a single row if separate==FALSE; otherwise it has two rows, corresponding to smoothness bound estimates and confidence intervals below and above the cutoff, respectively.

Examples

## Subset data to increase speed
r <- RDHonest(log(earnings)~yearat14, data=cghs,
              subset=abs(yearat14-1947)<10,
              cutoff=1947, M=0.04, h=3)
RDSmoothnessBound(r, s=2)

References

Michal Kolesár and Christoph Rothe. Inference in regressiondiscontinuity designs with a discrete running variable. AmericanEconomic Review, 108(8):2277—-2304, August 2018.\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1257/aer.20160945")}