National Autonomous University of Mexico, Circuito Mario de la Cueva,
Ciudad de la Investigación en Humanidades, Ciudad Universitaria,
C.P.04510, México, D.F.
Proceedings
Una Comparación De Los Modelos Poisson Y Binomial Negativa Con Stata: Un Ejercicio Didactico
Noé Becerra Rodríguez
UAM-Xochimilco
Fortino Vela Peón
UAM-Xochimilco
El objetivo de esta presentación es mostrar el uso de modelos
econométricos cuando se tienen variables de tipo contable, es decir,
en términos de simples conteos. La aplicación se hace a
considerando la información proveniente de una muestra de
académicos levantada durante 2008-09 en la Universidad
Autónoma Metropolitana. Se considera como variable de respuesta al
número de artículos publicados que fueron reportados en la
encuesta mientr as que cómo variables predictoras se usan
características de tip personal y de redes de investigación de
los académicos, tales como edad, tipo de actividad profesional
(básica, aplicada o desarrollo tecnológico), afiliación
al Sistema Nacional de Investigadores, país de obtención del
doctorado, entre otras. El trabajo se inicia mostrando por qué
razón resulta inapropiado el usar mínimos cuadrados ordinarios
para variable s contables. Se continúa realizando diversas
estimaciones para explicar algunos temas relacionados con este tipo de
modelos. A lo largo de la exposición se señalan las siguientes
técnicas de modelación econométri ca utilizando los
comandos de Stata: Poisson simple, Poisson exacta, Poisson con efectos
mezclados, Poisson truncada al cero, Poisson inflada por el cero, binomial
negativa simple, binomial negativa generalizada, binomial negativa truncada
al cero, binom ial negativa inflada por el cero y pruebas de bondad de
ajuste para estos modelos. La ponencia tiene una motivación
didáctica al considerar importante el que los alumnos tanto de las
licenciaturas en economía y dministración así como de
los posgrados en Ciencias Sociales que se imparten hoy en día
instituciones como la UAM Xochimilco, identifiquen las posibilidades que
ofrece este tipo de herramienta así como sus posibilidades de
fácil implementación en Stata.
Nonparametric classification of fossil gymnosperm’s foliar area
Erika Lourdes Ortíz-Martínez
Laboratorio de Paleontología
Isaías H. Salgado-Ugarte
Laboratorio de Biometría y Biología Pesquera, Facultad de Estudios Superiores Zaragoza, UNAM
María Patricia Velasco de León
Laboratorio de Paleontología
In Paleobotany, the features of leaf laminae linked to the climate have been
used to infer past climatic conditions. However, in spite of the increasing
number of paleoecological studies, studies worldwide have been focused on the
angiosperms, leaving aside other important groups throughout geological time.
In Mexico, for example, the most abundant and widely distributed fossil
flora are found in Early and Middle Jurassic deposits and are constituted
mainly of gymnosperms. So far, the contributions of investigations of the climate
where this vegetal group dominated have only been carried out at high-latitude localities, counting genera not present in Mexico besides the use
of an informal leaf-size proposal. It has been demonstrated that this
character has a direct relationship with climate. Therefore, this research
work was aimed at proposing for gymnosperms leaf-lamina size
classification using foliar area of each of the 737 plant fossils collected
from Jurassic outcrops at Mexico’s Oaxaca State Mixtec region in the
Rosario, Zorrillo, Taberna-Simón, and Tecomazúchl formations and
the Tecocoyunca group. To classify the area by its extension we used kernel
density estimators (smoothed histograms) with the (empirical suggestion)
half of optimal bandwidth (Härdle, 1991, Smoothing Techniques: With Implications in S [Springer]). The resulting multimodal
distribution was the base to set up categories defined with a new algorithm
denominated “antimodes”.
All the estimations were performed with Mosqueda-Romo and Salgado-Ugarte’s
programs amodes.ado (a new one), bandw.ado,
warpdenm.ado, bhataplt.ado, bhatgauc.ado, and their
updated versions for Stata 11.
The results allow the demarcation of seven categories, the
most abundant being foliar area number three (Microphile I, Zamites,
Pterophyllum, and Ptilophyllum genera) with a 58.06% representation, while
the group with the largest surface exposed to sunlight radiation (over 57.74%)
but the least percentage representation at the study zone (0.53%) is the
Pelourdea genus. The gathered data permit the inference that during the Jurassic
age in the Mixtec terrain, xeric conditions prevailed, which conditioned the
small size of the leaves.
On a daily basis, we at Stata receive a broad variety of technical questions from
users working in different areas with a large number of Stata commands. I
selected a few common, interesting questions to help provide a brief view of
the tools that are available in Stata for time-series analysis. I will start
with a quick, simple introduction to time series in Stata, and I will then
illustrate the use of a few commands to perform common tasks that are
normally involved in the kind of empirical analysis developed by some of the
Stata users who regularly contact us for technical assistance.
Una de las principales necesidades que involucran el manejo de
información proveniente de encuestas periódicas es la
generación sistemática de estadísticos y tabulados para
el análisis. En esta presentaci&oacut e;n, realizamos un ejercicio
comparativo mediante comandos Stata y Mata para la generación de
indicadores estratégicos, estadísticos y tabulados en
ámbitos determinados, a partir de los microdatos originales con
criterios prec odificados de la Encuesta Nacional de Ocupación y
Empleo (ENOE) que trimestralmente levanta el Instituto Nacional de
Estadística y Geografía (INEGI). La sintaxis de usuario, las
matrices de resultados y plantillas que presentamos en este trabajo muestran
la versatilidad de Stata como herramienta ideal en el proceso de
enseñanza-aprendizaje enfocado particularmente a los temas de
economía laboral que requieren aplicación empírica.
Endogenous treatment effects for count data models with endogenous participation or sample selection
Massimiliano Bratti
University of Milan
Alfonso Miranda
Institute of Education, University of London
We propose an estimator for models in which an endogenous dichotomous
treatment affects a count outcome in the presence of either sample selection
or endogenous participation using maximum simulated likelihood. We allow for
the treatment to have an effect on both the participation or the sample
selection rule and on the main outcome. Applications of this model are
frequent in—but not limited to—health economics. We show an
application of the model using data from Kenkel (2001, Kenkel and Terza, Journal of Applied Econometrics 16: 165–184), who investigated the
effect of physician advice on the amount of alcohol consumption. Our
estimates suggest that in these data a) neglecting treatment endogeneity
leads to a wrongly signed effect of physician advice on drinking intensity,
b) accounting for treatment endogeneity but neglecting endogenous
participation leads to an upward biased estimate of the treatment effect,
and c) advice only affects the drinking-intensive margin but not drinking
prevalence.
Pitfalls in the analysis of complex surveys using Stata
Carlos Guerrero de Lizardi
Tecnológico de Monterrey
The purpose of this presentation is to show the common mistakes in the
analysis of complex surveys. In Mexico, we have a significant number of
complex surveys available, which cover (among other issues) household
income and expenditures, the labor market, consumer confidence, public security perception, and family life. The
heart of the matter is the following: if you ignore the sampling design of a
complex survey (basically, the probability weights, the clustering, and the
stratification), inevitably you will get an erroneous estimation of whatever
you are dealing with.
Stata is a fully survey-capable software that takes into account the
sampling design. I explore Stata’s survey methods capabilities and, as far
as I know, illustrate the best practices in the analysis of complex
surveys for the following topics: descriptive statistics, variance
estimation methods, hypothesis testing, and econometric models.
This talk considers robust inference for regression models where data are
clustered, with correlation of observations in the same cluster (such as
state) and independence across clusters. The talk will range from the
simplest case of heteroskedastic-ro bust (one individual per cluster)
through to complications such as a small number of clusters and
two-clustering. The relevant Stata commands and Stata add-ons, where
available, will be discussed.
Departamento de Economía, Centro de Investigación en Alimentación y Desarrollo A. C.
Esta presentación muestra los beneficios que ofrece al usuario el
paquete de Análisis Distributivo en Stata (DASP) para la
evaluación del bienestar y su distribución, la medición
de la pobreza y la desigualdad, y la m anera en que los datos requeridos por
DASP se relacionan con temas importantes en la economía del bienestar
entre otros tópicos, tales como los efectos impositivos y el impacto
de las transferencias. El objetivo es que de forma general, se comprenda la
estructura básica del funcionamiento de los módulos de DASP y
su fuerte potencial en conjunto con las bondades del programa Stata. Se
presentan casos empíricos para mostrar sus aplicaciones.
Additional information Huesca.pdf (Inglés | English)
Efectos del programa social oportunidades en zonas urbanas
Armando Sánchez Vargas
Instituto de Investigaciones Econoómicas, UNAM
Este trabajo tiene como objetivo examinar las tendencias de mediano plazo
del comportamiento de los hogares beneficiarios del Programa Oportunidades
en las zonas urbanas. Especícamente, se estudian las tendencias en los
siguientes aspectos sociales: e l consumo alimentario y no alimentario, las
condiciones de vivienda, la inversión en activos del hogar, el empleo
infantil y juvenil y la toma de decisiones en los hogares desde un enfoque
de género. Lo anterior se lleva a cabo mediante el análisis
del panel de datos de Oportunidades, con base en el programa Stata,
procurando explicar las tendencias observadas en dichos indicadores y sus
posibles cambios en el tiempo. Se presenta un análisis
econométrico de los impact os de mediano plazo del Programa sobre los
indicadores de aspectos sociales destacados y, al mismo tiempo, se busca
determinar la existencia de sinergias de Oportunidades con otros programas
sociales. Cabe destacar que los métodos utilizados para la
estimación de los impactos toman en cuenta la naturaleza no
experimental de la información estadística disponible para el
análisis.
Simulating data is a powerful tool for understanding statistical models and
for spotting identification problems. I will use simulation techniques to
explain the building blocks for linear mixed models, and I will also show
how to estimate the parameters using the xtmixed command. Using these
basic blocks, I will explain how more-complex models can be constructed.
Finally, I will explain some nice (but not obvious) applications of
xtmixed.
Laboratorio de Biometría y Biología Pesquera, FES Zaragoza, UNAM
In this talk, we present the updated versions of some simple, improved
programs for nonparametric smoothing. These updated ado programs remain
simple and versatile—mainly with the Stata command style—but now they have
Stata’s version 11 syntax, a change that becomes apparent overall in the
graphical routines.
The programs include the following:
Density traces
boxdetr1 (uniform weight functions by direct calculation)
boxdt21 (uniform weight functions by discretized calculation)
cosdetr1 (cosine weight function)
dentrac1 (uniform and cosine weight functions)
Kernal density estimators
kernsi1 (rectangular weight function)
kernep1 (Epanechnikov weight function)
kerngau1 (Gaussian weight function)
adaker1 (Gaussian weight function with variable bandwidth)
kerneld1 (permits choice of the kernel weight function)
warpdenm1 (based on the averaged shifted histograms; permits choice of the kernel weight function besides counting and estimating the distribution’s modes)
A program for practical rules to smooth parameter (bandwidth) determination in density estimation
bandw1 (permits the kernel specification with automatic adjustment)
Nonparametric assessment of multimodality
with critiband1, a program to find critical bandwidths for a given number of modes
with bootsamb (generates bootstrap samples) and silvtest1 (calculates the significance of a modal number), programs to evaluate the significance of a number of modes by means of a smoothed bootstrap procedure
As with previous versions, this collection of simple programs continue to be
very useful in the analysis of data distribution (biological and of other
kinds) and permits the efficient use of time and effort in the calculations.
It must be stressed, too, that these commands make possible the use of
Stata’s new graphics syntaxes.
We use cookies to ensure that we give you the best experience on our website—to enhance site navigation, to analyze usage, and to assist in our marketing efforts. By continuing to use our site, you consent to the storing of cookies on your device and agree to delivery of content, including web fonts and JavaScript, from third party web services.
Cookie Settings
Privacy policy
Last updated: 16 November 2022
StataCorp LLC (StataCorp) strives to provide our users with exceptional products and services. To do so, we must collect personal information from you. This information is necessary to conduct business with our existing and potential customers. We collect and use this information only where we may legally do so. This policy explains what personal information we collect, how we use it, and what rights you have to that information.
These cookies are essential for our website to function and do not store any personally identifiable information. These cookies cannot be disabled.
Advertising and performance cookies
This website uses cookies to provide you with a better user experience. A cookie is a small piece of data our website stores on a site visitor's hard drive and accesses each time you visit so we can improve your access to our site, better understand how you use our site, and serve you content that may be of interest to you. For instance, we store a cookie when you log in to our shopping cart so that we can maintain your shopping cart should you not complete checkout. These cookies do not directly store your personal information, but they do support the ability to uniquely identify your internet browser and device.
Please note: Clearing your browser cookies at any time will undo preferences saved here. The option selected here will apply only to the device you are currently using.
