help rscore

Title

rscore- Estimation of responsiveness scores

Syntax

rscore
outcome
[varlist]
[if] [in]
[weight],
model(modeltype)
rs_name(stub)
[factors(varlist_f)
xlist(varlist_c)
graph(#)
radar(numlist)
id_string(varname)
vce(vcetype)
save_graph1(filename)
save_graph2(filename)]

fweights, pweights, iweights are allowed;
see weight.

Description

rscore computes unit-specific responsiveness scores using an iterated Random-Coefficient-Regression (RCR).
The basic econometrics of this model can be found in Wooldridge (2002, pp. 638-642).
The model estimated by rscore considers a regression of a response variable y, i.e. (outcome),
on a series of factors (or regressors) x, i.e. varlist, by assuming a different reaction (or "responsiveness")
of each unit to each factor contained in x. rscore allows for: (i) ranking units according to the
level of the responsiveness score obtained; (ii) detecting factors that are more influential in driving unit performance;
(iii) studying, more in general, the distribution (heterogeneity) of factor responsiveness scores across units.

Options

model(modeltype) specifies the model
to be estimated, where modeltype must be one of the following
models: "ols", "fe", "re". It is always required to specify one model.

rs_name(stub) specifes the name the user wants to give to the responsiveness scores
variables generate by rscore. RS variables are thus named as: stub1, stub2, stub3,
... and so forth.

factors(varlist_f) specifies that factor variables have to be included
among the regressors. It is optional for both models.

xlist(varlist_c) specifies that control variables (which are not factors)
have to be included among the regressors. It is optional for both models.

graph(#) provides a combined graph of the densities of the responsiveness scores. The
number # defines the width of the graph's x-axis. The user can set a proper # for providing a good rendering of the graph.

radar(numlist) provides a radar plot of the responsiveness scores for the units specifed
in numlist. Notice that, in order to run this option, the user must specify the id_string(varname)
option.

id_string(varname) requests to specify a string variable as identifer of each observation.
This is compulsory if the user wishes to provide a radar plot of the responsiveness scores.

vce(vcetype): allows to choose vcetype as either robust, or cluster clustvar.

save_graph1(filename) allows to save the graph generate by the option graph(#)
in the user specifed filename.

save_graph2(filename) allows to save the graph generate by the option radar(numlist)
in the user specifed filename.

modeltype_options

Model
ols regression estimated by ordinary least squares (OLS)

fe panel data fixed-effect regression (FE)

re panel data random-effect regression (RE)

rscore returns goodness-of-fit statistics - i.e. R-squared - for each estimated factor regression, by storing them
in the following scalars: e(R1), ..., e(RQ). Further, rscore provides the average R-squared - i.e the overall goodness-of-fit of the model -
stored in the scalar e(R).

rscore creates a number of variables:

stubj is the responsiveness scores variable related to the j-th variable of varlist.
They are as many as the variables considered in varlist.

Remarks

Please, before running this program, remember to have the most recent up-to-date version installed.

Examples

. rscore y x1 x2 x3 , rs_name(RS) model(ols) factor(f1 f2)
. rscore y x1 x2 x3 , rs_name(RS) model(fe) factor(f1 f2) xlist(x4 x5)

Reference

Wooldridge, J. M. 2002. Econometric Analysis of Cross Section and Panel Data.
The MIT Press, Cambridge.

Author

Giovanni Cerulli

IRCrES-CNR

Research Institute for Sustainable Economic Growth, National Research Council of Italy

E-mail: giovanni.cerulli@ircres.cnr.it

Also see

Online: ivregress