View source: R/rdsensitivity.R

rdsensitivity | R Documentation |

`rdsensitivity`

analyze the sensitivity of randomization p-values
and confidence intervals to different window lengths.

rdsensitivity( Y, R, cutoff = 0, wlist, wlist_left, tlist, statistic = "diffmeans", p = 0, evalat = "cutoff", kernel = "uniform", fuzzy = NULL, ci = NULL, ci_alpha = 0.05, reps = 1000, seed = 666, nodraw = FALSE, quietly = FALSE )

`Y` |
a vector containing the values of the outcome variable. |

`R` |
a vector containing the values of the running variable. |

`cutoff` |
the RD cutoff (default is 0). |

`wlist` |
the list of windows to the right of the cutoff. By default the program constructs 10 windows around the cutoffwith 5 observations each. |

`wlist_left` |
the list of windows to the left of the cutoff. If not specified, the windows are constructed symmetrically around the cutoff based on the values in wlist. |

`tlist` |
the list of values of the treatment effect under the null to be evaluated. By default the program employs ten evenly spaced points within the asymptotic confidence interval for a constant treatment effect in the smallest window to be used. |

`statistic` |
the statistic to be used in the balance tests. Allowed options are |

`p` |
the order of the polynomial for outcome adjustment model. Default is 0. |

`evalat` |
specifies the point at which the adjusted variable is evaluated. Allowed options are |

`kernel` |
specifies the type of kernel to use as weighting scheme. Allowed kernel types are |

`fuzzy` |
indicates that the RD design is fuzzy. |

`ci` |
returns the confidence interval corresponding to the indicated window length. |

`ci_alpha` |
Specifies value of alpha for the confidence interval. Default alpha is .05 (95% level CI). |

`reps` |
number of replications. Default is 1000. |

`seed` |
the seed to be used for the randomization tests. |

`nodraw` |
suppresses contour plot. |

`quietly` |
suppresses the output table. |

`tlist` |
treatment effects grid |

`wlist` |
window grid |

`results` |
table with corresponding p-values for each window and treatment effect pair. |

`ci` |
confidence interval (if |

Matias Cattaneo, Princeton University. cattaneo@princeton.edu

Rocio Titiunik, Princeton University. titiunik@princeton.edu

Gonzalo Vazquez-Bare, UC Santa Barbara. gvazquez@econ.ucsb.edu

Cattaneo, M.D., R. Titiunik and G. Vazquez-Bare. (2016). Inference in Regression Discontinuity Designs under Local Randomization. *Stata Journal* 16(2): 331-367.

# Toy dataset R <- runif(100,-1,1) Y <- 1 + R -.5*R^2 + .3*R^3 + (R>=0) + rnorm(100) # Sensitivity analysis # Note: low number of replications to speed up process. # The user should increase the number of replications. tmp <- rdsensitivity(Y,R,wlist=seq(.75,2,by=.25),tlist=seq(0,5,by=1),reps=500)

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