| Package: rdrobust | |
| Type: Package | |
| Title: Robust Data-Driven Statistical Inference in | |
| Regression-Discontinuity Designs | |
| Version: 0.80 | |
| Date: 2015-03-18 | |
| Author: Sebastian Calonico <[email protected]>, Matias D. Cattaneo <[email protected]>, Rocio Titiunik <[email protected]> | |
| Maintainer: Sebastian Calonico <[email protected]> | |
| Description: Regression-discontinuity (RD) designs are quasi-experimental research designs popular in social, behavioral and natural sciences. The RD design is usually employed to study the (local) causal effect of a treatment, intervention or policy. This package provides tools for data-driven graphical and analytical statistical inference in RD designs: rdrobust to construct local-polynomial point estimators and robust confidence intervals for average treatment effects at the cutoff in Sharp, Fuzzy and Kink RD settings, rdbwselect to perform bandwidth selection for the different procedures implemented, and rdplot to conduct exploratory data analysis (RD plots). | |
| Depends: |
| using Base.Meta | |
| macro loop_ts(ex) | |
| l, r = ex.args | |
| # Determine which symbol should become the loop index. We've assumed that | |
| # the equation has the form `y[t+1] = ...` or `y[t] = ...`. In the first case | |
| # we need to parse 't+1' to find the symbol (t); in the second case, we can | |
| # just read it off. | |
| idx = |
| ## Some useful and/or interesting Julia macros. I haven't seen many | |
| ## examples online, so I hope this might be useful for people trying | |
| ## to understand macros. Caveat Emptor; I don't understand them that | |
| ## well myself. Additions and improvements are more than welcome. | |
| ## | |
| ## Everything here is available under the MIT license (see bottom of | |
| ## file) | |
| ## | |
| ## - Gray Calhoun (@grayclhn) 10/17/2014 |
| # Copyright (c) 2014 Gray Calhoun. | |
| # | |
| # Permission is hereby granted, free of charge, to any person obtaining | |
| # a copy of this software and associated documentation files (the | |
| # "Software"), to deal in the Software without restriction, including | |
| # without limitation the rights to use, copy, modify, merge, publish, | |
| # distribute, sublicense, and/or sell copies of the Software, and to | |
| # permit persons to whom the Software is furnished to do so, subject to | |
| # the following conditions: | |
| # |
| # Explicitly calculate the inverse by LU decomposition: LAPACK DGESV, | |
| # then calculate the projection matrix X(X'X)^(-1)X' | |
| projection.LU1 <- function(x) | |
| x %*% solve(crossprod(x)) %*% t(x) | |
| # Use DGESV but without explicitly calculating the inverse | |
| projection.LU2 <- function(x) | |
| crossprod(t(x), solve(crossprod(x), t(x))) | |
| # Calculate the projection matrix using the QR decomposition |
| Country|Year|Debt|RGDP|GDP|GDPI|GDP1|GDP2|RGDP1|RGDP2|GDPI1|GDPI2|Infl|Debt1|Debt2|dRGDP|Debtalt|GDP2alt|GDPalt|RGDP2alt|debtgdp|GDP3|GNI | |
| Australia|1800|||||0.608|||||||||||||||| | |
| Australia|1801|||||0.72|||||||||||||||| | |
| Australia|1802|||||0.716|||||||||||||||| | |
| Australia|1803|||||0.768|||||||||||||||| | |
| Australia|1804|||||0.884|||||||||||||||| | |
| Australia|1805|||||0.952|||||||||||||||| | |
| Australia|1806|||||1.044|||||||||||||||| | |
| Australia|1807|||||1.112|||||||||||||||| | |
| Australia|1808|||||1.096|||||||||||||||| |
college_basketball_2013.csv has game outcomes for the 2012-2013 men's college basketball season.
Date: the date the game was played
HomeId: a unique numeric identifier for the home team
AwayId: a unique numeric identifier for the away team
HomeName: the name of the home team
AwayName: the name of the away team
HomeConf: the conference the home team belongs to
AwayConf: the conference the away team belongs to