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Last updated: 14 October 2010

2010 Portuguese Stata Users Group meeting

17 September 2010


School of Economics and Management
University of Minho

Gualtar University Campus
4710-057 Braga


Estimation of high-dimensional models

Paulo Guimarães
Moore School of Business, University of South Carolina
In this presentation, I provide a detailed discussion of an alternative iterative approach for the estimation of linear regression models with two high-dimensional fixed-effects, such as large employer–employee datasets. I also show how to extend the approach to three high-dimensional fixed effects. Finally, I show how the algorithm may be used for estimation of several high-dimensional nonlinear models.

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The price of unobservables and the employer size–wage premium

João C. Cerejeira da Silva
University of Minho and NIPE
In this presentation, I consider the estimation of the employer-size wage effect. I use an estimator that extends the standard panel-data techniques to the case in which the return to permanent component of the error term is differently rewarded across firm sizes. This is a more general model with interactions between time-varying explanatory variables and some unobservable, time-constant variables. I show that a model of this type can be estimated using a nonlinear GMM technique. The results show that some of the observed skills—namely, education, age, and tenure—have high returns in large firms, while the opposite is true for occupations requiring high skill and for the gender gap. On the other hand, the price of nonobserved skills is reduced as firm size increases. This finding is consistent with explanations based on the premise that large employers have more difficulty monitoring workers, which therefore leads them to monitor less closely.

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The determinants of private sector and multilateral development agencies’ participation in infrastructure projects

Maria Basílio
IP Beja
Much more investment will be needed in developing countries to achieve the Millennium Development Goals, specifically, the goal of reducing poverty. In this respect, private-sector investment is critical, bringing more funds, expertise, and efficiency to the development of projects in several essential areas, like energy, transport, water, and telecommunications. Complementarily, the involvement of Multilateral Development Agencies (MDA) plays an important “enabling” function, acting like a mechanism of risk reduction and enhancing credit.

To address these unexplored topics, I perform an empirical analysis of the cross-country determinants of private sector and MDA participation in infrastructure Public Private Partnerships; for this analysis, I use data from developing countries, which was acquired from the World Bank’s Private Participation in Infrastructure database.

The results suggest the following: the participation of MDA is higher for less populous and poorer countries. Yet neither level of political risk of a country nor respect for human rights seems to play any role in explaining multilateral participation in projects. Concerning private-sector participation, proxies for the country’s economic risk are more relevant. The private sector seems to prefer investing in projects located in richer and less populous countries. Also statistically relevant is the country’s legal origin and whether the project has MDA participation.

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Is there evidence for the existence of a financial accelerator mechanism in the Portuguese manufacturing sector?

Jorge Cunha
University of Minho
In recent times, due to developments in the field of information economics, there was a rationalization of the link between financial factors and fluctuations in economic activity (Bernanke et al. 1996; Gertler 1988). An issue that has been highlighted is the possibility that fluctuations in economic activity can be induced (or amplified) by fragilities in a firm’s financial position—the so-called financial accelerator mechanism.

Based on this reasoning, I aim to contribute to the empirical literature on this issue by testing the following three hypotheses: (a) the financial position of a firm is a major determinant of its capital investment decisions; (b) the financial position of a firm is more important for firms that face higher information problems in financial markets; and (c) the financial position of a firm is even more important for firms that face higher information problems in financial markets at times of economic recession.

Aggregate data for 16 industrial sectors, covering a period of time from 1990 to 2005, was used in the empirical study. These data were obtained from the Central Balance-Sheet Database of the Portuguese Central Bank. In this database, economic and financial information on Portuguese nonfinancial firms is included.

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Producing output tables from multiple regressions for Latex using Stata

Miguel Portela
University of Minho
Abstract not available.

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Intelligent data analysis of clinical trials with Stata

Antonio Gouveia de Oliveira
University Nova de Lisboa
Clinical trial statistical analysis and reporting is a formidable task. A final-study report requires the creation of hundreds of tables and data listings, and the calculation of over one thousand statistical significance levels, difference estimates, and confidence limits. Typically, several database programmers, statistical programmers, and biostatisticians are needed to perform this task over a period of time that is measured in months.

I describe the design approaches and the evaluation of an intelligent data analysis system (DART) that automates the creation of clinical trial statistical reports, which is one component of an integrative Clinical Trials Information System. This application was developed in Stata programming language and has about 9,000 lines of code. This unsupervised knowledge-based system is able to select, according to the characteristics of the study design, the study statistical analysis plan and the type of baseline and efficacy variables used (which are all encoded and stored in the database), the statistical methods adequate for each analysis, and the results that need to be reported. The entire process of data analysis and reporting can be performed automatically, or the user may specify some parameters of the analysis (e.g., scale transformations, adjustment for confounding). The application can handle commonly used statistical methods applied to clinical trials analyses for nominal, multi-valued, ordinal, interval, and event/count data in one-, two-, and multiple-arm trials, crossover studies, and factorial designs, with or without stratification. It handles imputation of missing data, scale transformations, and regrouping of study centers. It can automatically select baseline variables for inclusion as covariates, and conduct poststratification analyses and subgroup analyses.

So far, DART has been successfully used for the automated statistical reporting of 35 pharmaceutical clinical trials. In a validation study, the statistical methods used in a random sample of 51 clinical trials were published in The New England Journal of Medicine and in The Lancet, reporting 97 different analyses. The analytical methods were identical or equivalent to those selected by DART in 84.5% of the analyses, different from DART in 6.2%, and not supported by DART in 9.3%.

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Giving graphs a good look: Schemes and the Graph Editor

Bill Rising
Users often need a consistent look for their Stata graphs for publications or internal documents. To change one graph, the user can include options. To change many graphs at once, it is better to create a scheme or graph recording, which automates the changes and simplifies the graph commands. I will show how to make simple schemes and graph recordings so that you can get a consistent look for all your graphs.

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Scientific organizers

Antonio Gouveia Oliveira, Universidade Nova de Lisboa

João Cerejeira, Universidade do Minho

Jorge Caiado, CEMAPRE, ISEG, Universidade Técnica de Lisboa

Miguel Portela, Universidade do Minho

Paulo Guimarães, University of South Carolina

Logistics organizers

Timberlake Consultores, the official distributor of Stata in Portugal.