dyadclust:
Estimates cluster-robust standard errors for dyadic data using multiway
decomposition as described in Aronow
et al. (2015). The package also supports parallel computing using parallel.To install the package and update it the following command can be used in Stata:
net install dyadclust, from (https://raw.githubusercontent.com/cfbalcazar/dyadclust/main/dyadclust/) replace force allpsevent:
Allows the user to estimate a Pseudo-Event study using parametric or
non-parametric matching as described in Nopo (2008) and
Nopo (2008).
This methodology is particularly useful to study the causal impact of
life-cycle events, combining both the power of pseudo-panels with event
studies in a similar spirit as synthetic control matching.To install the package and update it the following command can be used in Stata:
net install nopomatch, from (https://raw.githubusercontent.com/cfbalcazar/nopomatch/main/nopomatch/) replace force allnopomatch:
Estimates the decomposition of a binary treatment using non-parametric
matching as described in Nopo (2008). I
worked with this methodology many years ago and made an extension of the
do-file that allows the user decomposing the difference in means for
binary outcomes.To install the package and update it the following command can be used in Stata:
net install nopomatch, from (https://raw.githubusercontent.com/cfbalcazar/nopomatch/main/nopomatch/) replace force allrepresent:
Computes and provides diagnostics over the effective regression weights
as defined in Aronow
and Samii (2016). Causal effects estimated via multiple regression
differentially weight each unit’s contribution. The “effective sample”
that regression uses to generate the estimate may bear little
resemblance to the population of interest, and the results may be
non-representative in a manner similar to what quasi-experimental
methods or experiments with convenience samples produce. This command
allows the user to explore this problem.To install the package and update it the following command can be used in Stata:
net install represent, from (https://raw.githubusercontent.com/cfbalcazar/represent/main/represent/) replace force allfwlgraph:
Allows one to visualize the relationship between a dependent variable
and one independent variable of interest. The approach is motivated by
the Frisch-Waugh-Lovell theorem - one of my favorite theorems!. It is
quite useful to visualize what the regression with control variables is
actually representing, which can help one determine whether a linear or
other specification might make sense, as well as visually identifying
outliers.To install the package and update it the following command can be used in Stata:
net install fwlgraph, from (https://raw.githubusercontent.com/cfbalcazar/fwlgraph/main/fwlgraph/) replace force all