Title

mmeiv -- Multiple Marginal Effects IV Estimation

Syntax

mmeiv depvar [varlist_W2]
(varlist_X =
varname_T { varlist_W1 }) [if] [in]
[weight]
[, options]

Description

mmeiv implements a multiple marginal effects estimation using instrumental variables, as proposed in Caetano and Escanciano.

Following the notation in the paper, mmeiv estimates multiple marginal effects of varlist_X on depvar using the instrument varname_T and covariates varlist_W1. varlist_X and varlist_W1 are endogenous variables. varlist_W1 is the same as W as discussed in Caetano and Escanciano. Note that varlist_W1 must have at least as many elements as varlist_X minus one. varlist_W2 are additional exogenous controls.

Options

xknots(# [# #...]) specifies the # of knots that are used in creating linear splines for each variable in varlist_X. Knots are placed at percentiles of the data. Specifying multiple knots for an element of varlist_X is akin to assuming that this element may have different marginal effects for different values of the variable. By default each element in varlist_X has only one marginal effect (linear).

wknots(# [# #...]) specifies the # of knots that are used in creating linear splines for each variable in varlist_W1. Knots are placed at percentiles of the data. Specifying multiple knots for an element of varlist_W1 is akin to transforming that element into multiple controls, each of which controls for pieces of the support of that variable separately. By default each element in varlist_W1 is used only as itself.

vce(vcetype) vcetype may be unadjusted,
robust, cluster clustvar, bootstrap,
jackknife, or hac kernel. Default is unadjusted.

predict stores marginal effect estimate and standard errors of the marginal effect of each variable in varlist_X.

plot plots the estimated marginal effect of each variable in varlist_X on depvar.

graphoptions(string) passes through twoway options to the plotted graphs.

Stored results

mmeiv stores the following in e():

Scalars

e(N) number of observations

e(mss) model sum of squares

e(df_m) model degrees of freedom

e(rss) residual sum of squares

e(df_r) residual degrees of freedom

e(r2) R-squared

e(r2_a) adjusted R-squared

e(F) F statistic

e(rmse) root mean squared error

e(N_clust) number of clusters

e(chi2) chi-squared

e(wlagopt) lags used in HAC weight matrix (if Newey-West
algorithm used)

e(vcelagopt) lags used in HAC VCE matrix (if Newey-West
algorithm used)

e(rank) rank of e(V)

Macros

e(cmd) mmeiv

e(cmdline) command as typed

e(depvar) name of dependent variable

e(instd) instrumented variable

e(insts) instruments

e(wtype) weight type

e(wexp) weight expression

e(title) title in estimation output

e(clustvar) name of cluster variable

e(hac_kernel) HAC kernel

e(hac_lag) HAC lag

e(vce) vcetype specified in vce()

e(vcetype) title used to label Std. Err.

e(exogr) exogenous regressors

e(properties) b V

Matrices

e(b) coefficient vector

e(V) variance-covariance matrix of the estimators

Functions

e(sample) marks estimation sample

References

Caetano, C. and J. C. Escanciano (2019): "Identifying Multiple Marginal Effects with a Single Instrument," working paper.

Authors

Carolina Caetano

University of Georgia

Athens, GA

{ browse "mailto:carol.caetano@uga.edu" : carol.caetano@uga.edu }

Juan Carlos Escanciano

University of Indiana

Bloomington, IN

ailto:jescanci@indiana.edu

Alon Bergman

The Wharton School

Philadelphia, PA

alonberg@wharton.upenn.edu