mrd_impute | R Documentation |

`mrd_impute`

estimates treatment effects in a multivariate regression discontinuity design (MRDD) with imputed missing values.

```
mrd_impute(
formula,
data,
subset = NULL,
cutpoint = NULL,
bw = NULL,
front.bw = NA,
m = 10,
k = 5,
kernel = "triangular",
se.type = "HC1",
cluster = NULL,
impute = NULL,
verbose = FALSE,
less = FALSE,
est.cov = FALSE,
est.itt = FALSE,
local = 0.15,
ngrid = 250,
margin = 0.03,
boot = NULL,
method = c("center", "univ", "front"),
t.design = NULL,
stop.on.error = TRUE
)
```

`formula` |
The formula of the MRDD; a symbolic description of the model to be fitted. This is supplied in the
format of |

`data` |
An optional data frame containing the variables in the model. If not found in |

`subset` |
An optional vector specifying a subset of observations to be used in the fitting process. |

`cutpoint` |
A numeric vector of length 2 containing the cutpoints at which assignment to the treatment is determined.
The default is |

`bw` |
A vector specifying the bandwidths at which to estimate the RD.
Possible values are |

`front.bw` |
A non-negative numeric vector of length 3 specifying the bandwidths at which to estimate the RD for each
of three effects models (complete model, heterogeneous treatment model, and treatment only model)
detailed in Wong, Steiner, and Cook (2013). If |

`m` |
A non-negative integer specifying the number of uniformly-at-random samples to draw as search candidates for |

`k` |
A non-negative integer specifying the number of folds for cross-validation to determine |

`kernel` |
A string indicating which kernel to use. Options are |

`se.type` |
This specifies the robust standard error calculation method to use,
from the "sandwich" package. Options are,
as in |

`cluster` |
An optional vector of length n specifying clusters within which the errors are assumed
to be correlated. This will result in reporting cluster robust SEs. This option overrides
anything specified in |

`impute` |
An optional vector of length n containing a grouping variable that specifies the imputed variables with missing values. |

`verbose` |
A logical value indicating whether to print additional information to
the terminal. The default is |

`less` |
Logical. If |

`est.cov` |
Logical. If |

`est.itt` |
Logical. If |

`local` |
A non-negative numeric value specifying the range of neighboring points around the cutoff on the standardized scale, for each assignment variable. The default is 0.15. |

`ngrid` |
A non-negative integer specifying the number of non-zero grid points on each assignment variable, which is also the number of zero grid points on each assignment variable. The default is 250. The value used in Wong, Steiner and Cook (2013) is 2500, which may cause long computational time. |

`margin` |
A non-negative numeric value specifying the range of grid points beyond the minimum and maximum of sample points on each assignment variable. The default is 0.03. |

`boot` |
An optional non-negative integer specifying the number of bootstrap samples to obtain standard error of estimates.
This argument is not optional if method is |

`method` |
A string specifying the method to estimate the RD effect. Options are |

`t.design` |
A character vector of length 2 specifying the treatment option according to design.
The first entry is for |

`stop.on.error` |
A logical value indicating whether to remove bootstraps which cause error in the |

`mrd_impute`

returns an object of class "`mrd`

" or `"mrdi"`

for `"front"`

method.
The function `summary`

is used to obtain and print a summary of the
estimated regression discontinuity. The object of class `mrd`

is a list
containing the following components for each estimated treatment effect,
`tau_MRD`

or `tau_R`

and `tau_M`

:

`call` |
The matched call. |

`type` |
A string denoting either |

`cov` |
The names of covariates. |

`bw` |
Numeric vector of each bandwidth used in estimation. |

`obs` |
Vector of the number of observations within the corresponding bandwidth. |

`model` |
For a sharp design, a list of the |

`frame` |
Returns the model frame used in fitting. |

`na.action` |
The observations removed from fitting due to missingness. |

`est` |
Numeric vector of the estimate of the discontinuity in the outcome under a sharp MRDD or the Wald estimator in the fuzzy MRDD, for each corresponding bandwidth. |

`d` |
Numeric vector of the effect size (Cohen's d) for each estimate. |

`se` |
Numeric vector of the standard error for each corresponding bandwidth. |

`z` |
Numeric vector of the z statistic for each corresponding bandwidth. |

`df` |
Numeric vector of the degrees of freedom computed using Barnard and Rubin (1999) adjustment for imputation. |

`p` |
Numeric vector of the p-value for each corresponding bandwidth. |

`ci` |
The matrix of the 95 for each corresponding bandwidth. |

`impute` |
A logical value indicating whether multiple imputation is used or not. |

Wong, V. C., Steiner, P. M., Cook, T. D. (2013). Analyzing regression-discontinuity designs with multiple assignment variables: A comparative study of four estimation methods. Journal of Educational and Behavioral Statistics, 38(2), 107-141. https://journals.sagepub.com/doi/10.3102/1076998611432172.

Lee, D. S., Lemieux, T. (2010). Regression Discontinuity Designs in Economics. Journal of Economic Literature, 48(2), 281-355. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1257/jel.48.2.281")}.

Lee, D. S., Card, D. (2010). Regression discontinuity inference with specification error. Journal of Econometrics, 142(2), 655-674. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.jeconom.2007.05.003")}.

Barnard, J., Rubin, D. (1999). Small-Sample Degrees of Freedom with Multiple Imputation. Biometrika, 86(4), 948-55.

```
set.seed(12345)
x1 <- runif(300, -1, 1)
x2 <- runif(300, -1, 1)
cov <- rnorm(300)
y <- 3 + 2 * (x1 >= 0) + 3 * cov + 10 * (x2 >= 0) + rnorm(300)
imp <- rep(1:3, each = 100)
# all examples below have smaller numbers of m to keep run-time low
# centering
mrd_impute(y ~ x1 + x2 | cov, impute = imp, method = "center", t.design = c("geq", "geq"), m = 3)
# univariate
mrd_impute(y ~ x1 + x2 | cov, impute = imp, method = "univ", t.design = c("geq", "geq"), m = 3)
# frontier - don't run due to computation time
## Not run: mrd_impute(y ~ x1 + x2 | cov, impute = imp, method = "front",
boot = 1000, t.design = c("geq", "geq"), m = 3)
## End(Not run)
```

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