*! version 1.1.0 Sep 2021
help quaidsce
also see: quaidsce postestimation

Title

quaidsce -- Estimate censored almost-ideal demand systems

Syntax

quaidsce varlist_expshares [if] [in], anot(#)
{prices(varlist_prices)|lnprices(varlist_lnprices)}
{expenditure(varlist_exp)|lnexpenditure(varlist_lnexp)}
[options]

where varlist_expshares is the list of expenditure share
variables. You must specify all the expenditure share variables. Do
not omit one of the shares to avoid a singular covariance matrix;
quaidsce does that automatically.

# anot(#) value to use for alpha_0 parameter

* prices(varlist_prices) list of price variables

* lnprices(varlist_lnprices) list of variables containing natural logarithms of prices

+ expenditure(varlist_exp) variable representing total expenditure

+ lnexpenditure(varlist_lnexp) variable representing the natural logarithm of total expenditure

demographics(varlist_demo) demographic variables to include

noquadratic do not include quadratic expenditure term

nocensor do not include censoring correction

nolog suppress the iteration log

vce(vcetype) vcetype may be gnr, robust, cluster clustvar, bootstrap, or jackknife

level(#) set confidence level; default is level(95)

method(method_name) NLSUR estimator; default is

# anot(#) is required.

* You must specify prices() or lnprices() but not both.

+ You must specify expenditure() or lnexpenditure() but not both.

Description

quaidsce estimates the parameters of Deaton and Muellbauer's
(1980) almost-ideal demand system (AIDS) and Banks, Blundell, and
Lewbel's (1997) quadratic AIDS model. Demographic variables can be
included in the model based on Ray's (1983) expenditure function scaling
technique. Censoring is accounted for based on the method proposed by
Shonkwiler and Yen (1999). This command extends over the quaids
command, as described in Poi (2012).

Options

anot(#) specifies the value of the alpha_0 parameter that
appears in the price index. anot() is required.

prices(varlist_prices) specifies the list of price
variables. You must specify the price variables in the same order that
you specify the expenditure share variables varlist_expshares. You
must specify prices() or lnprices() but not both.

lnprices(varlist_lnprices) specifies the list of variables
containing the natural logarithms of the price variables. You must
specify this list in the same order that you specify the expenditure
share variables varlist_expshares. You must specify prices()
or lnprices() but not both.

expenditure(varlist_exp) specifies the variable
representing total expenditure on all the goods in the demand system.
You must specify expenditure() or lnexpenditure() but not
both.

lnexpenditure(varlist_lnexp) specifies the variable
representing the natural logarithm of the total expenditure on all
the goods in the demand system. You must specify expenditure() or
lnexpenditure() but not both.

demographics(varlist_demo) requests that the variables
varlist_demo be included in the demand system based on the scaling
technique introduced by Ray (1983).

noquadratic requests that the quadratic income term not be
included in the expenditure share equations. Specifying this option
requests Deaton and Muellbauer's (1980) AIDS model. The default is to
include the quadratic income term, yielding Banks, Blundell, and
Lewbel's (1997) quadratic AIDS model.

nocensor requests to avoid the censoring correction two-step approach.
The default is to conduct the two-step as proposed by Shonkwiler and Yen (1999).

nolog requests that the iteration log be suppressed.

vce(vcetype) specifies the type of standard error reported,
which includes types that are derived from asymptotic theory, that are
robust to some kinds of misspecification, that allow for intragroup
correlation, and that use bootstrap or jackknife methods. This option
works just as it does for nlsur; see [R] nlsur.

vce(gnr), the default, uses the conventionally derived
variance estimator for nonlinear models fit by using Gauss-Newton
regression. For final output, use vce(bootstrap)

method(method_name); nlsur estimator method; see [R] nlsur. Default is ifgnls.
Other methods can be used to conduct faster model selection (at your own risk).

level(#); see [R] estimation options.

Saved results

quaidsce saves the following in e():

Scalars

e(N) number of observations

e(ll) log likelihood

e(N_clust) number of clusters

e(ndemos) number of demographic variables

e(anot) value of alpha_0

e(ngoods) number of goods

Macros

e(cmd) quaidsce

e(clustvar) name of cluster variable

e(vce) vcetype specified in vce()

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

e(properties) b V

e(estat_cmd) program used to implement estat

e(predict) program used to implement predict

e(demographics) demographic variables included

e(lhs) expenditure share variables

e(expenditure) expenditure variable

e(lnexpenditure) log-expenditure variable

e(prices) price variables

e(lnprices) log-price variables

e(quadratic) noquadratic

e(censor) nocensor

e(method) specified in method()

e(properties) b V

Matrices

e(b) coefficient vector

e(V) variance-covariance matrix of the estimators

e(best) coefficient vector of estimated parameters

e(Vest) variance-covariance matrix of estimated parameters

e(alpha) alpha vector

e(beta) beta vector

e(gamma) gamma matrix

e(lambda) lambda vector

e(eta) eta matrix

e(rho) rho vector

e(delta) delta vector

Functions

e(sample) marks estimation sample

References

Banks, J., R. Blundell, and A. Lewbel. 1997. Quadratic Engle curves
and consumer demand. Review of Economics and Statistics 79:
527-539.

Deaton, A. S., and J. Muellbauer. 1980. An almost ideal demand system.
American Economic Review 70: 312-326.

Poi, B. P. 2012. Easy demand system estimation with quaids.
Stata Journal 12: 433-446.

Shonkwiler, J and Yen, Steven T., (1999), Two-Step estimation of a censored system of equations,
American Journal of Agricultural Economics, 81, issue 4, p. 972-982,

Ray, R. 1983. Measuring the costs of children: An alternative approach.
Journal of Public Economics 22: 89-102.

Authors

Juan C. Caro

University of Luxembourg

juan.caro@uni.lu

Juan Carlos Salgado

INSP Mexico

Grace Melo

Texas A&M University

Jose Alberto Molina

Universidad de Zaragoza

Also see

Help: quaidsce postestimation