Empirical Models of Auctions

Many important economic questions arising in auctions can be answered only with knowledge of the underlying primitive distributions governing bidder demand and information. An active literature has developed aiming to estimate these primitives by exploiting restrictions from economic theory as part of the econometric model used to interpret auction data. We review some highlights of this recent literature, focusing on identification and empirical applications. We describe three insights that underlie much of the recent methodological progress in this area and discuss some of the ways these insights have been extended to richer models allowing more convincing empirical applications. We discuss several recent empirical studies using these methods to address a range of important economic questions.

Empirical Models of Auctions

Many important economic questions arising in auctions can be answered only with knowledge of the underlying primitive distributions governing bidder demand and information. An active literature has developed aiming to estimate these primitives by exploiting restrictions from economic theory as part of the econometric model used to interpret auction data. We review some highlights of this recent literature, focusing on identification and empirical applications. We describe three insights that underlie much of the recent methodological progress in this area and discuss some of the ways these insights have been extended to richer models allowing more convincing empirical applications. We discuss several recent empirical studies using these methods to address a range of important economic questions.Auctions, Identification, Estimation, Testing

Empirical Models of Auctions

Many important economic questions arising in auctions can be answered only with knowledge of the underlying primitive distributions governing bidder demand and information. An active literature has developed aiming to estimate these primitives by exploiting restrictions from economic theory as part of the econometric model used to interpret auction data. We review some highlights of this recent literature, focusing on identification and empirical applications. We describe three insights that underlie much of the recent methodological progress in this area and discuss some of the ways these insights have been extended to richer models allowing more convincing empirical applications. We discuss several recent empirical studies using these methods to address a range of important economic questions

lassopack: Model selection and prediction with regularized regression in Stata

This article introduces lassopack, a suite of programs for regularized regression in Stata. lassopack implements lasso, square-root lasso, elastic net, ridge regression, adaptive lasso and post-estimation OLS. The methods are suitable for the high-dimensional setting where the number of predictors $p$ may be large and possibly greater than the number of observations, $n$. We offer three different approaches for selecting the penalization (`tuning') parameters: information criteria (implemented in lasso2), $K$-fold cross-validation and $h$-step ahead rolling cross-validation for cross-section, panel and time-series data (cvlasso), and theory-driven (`rigorous') penalization for the lasso and square-root lasso for cross-section and panel data (rlasso). We discuss the theoretical framework and practical considerations for each approach. We also present Monte Carlo results to compare the performance of the penalization approaches.Comment: 52 pages, 6 figures, 6 tables; submitted to Stata Journal; for more information see https://statalasso.github.io

Elemental Abundances in the X-Ray Gas of Early-Type Galaxies with XMM and Chandra Observations

The source of hot gas in elliptical galaxies is thought to be due to stellar mass loss, with contributions from supernova events and possibly from infall from a surrounding environment. This picture predicts supersolar values for the metallicity of the gas toward the inner part of the galaxy, which can be tested by measuring the gas phase abundances. We use high-quality data for 10 nearby early-type galaxy from XMM-Newton, featuring both the EPIC and the Reflection Grating Spectrometer, where the strongest emission lines are detected with little blending; some Chandra data are also used. We find excellent consistency in the elemental abundances between the different XMM instruments and good consistency with Chandra. Differences in abundances with aperture size and model complexity are examined, but large differences rarely occur. For a two-temperature thermal model plus a point source contribution, the median Fe and O abundances are 0.86 and 0.44 of the Solar value, while Si and Mg abundances are similar to that for Fe. This is similar to stellar abundances for these galaxies but supernovae were expected to enhance the gas phase abundances considerably, which is not observed.Comment: 35 pages, 10 figures, accepted for publication in Astrophysical Journa

Recruiting and Retaining Individuals with Serious Mental Illness and Diabetes in Clinical Research: Lessons Learned from a Randomized, Controlled Trial.

Abstract: Recruitment and retention of individuals with serious mental illness (SMI) and comorbid diabetes mellitus (DM) in research studies can be challenging with major impediments being difficulties reaching participants via telephone contact, logistic difficulties due to lack of transportation, ongoing psychiatric symptoms, and significant medical complications. Research staff directly involved in recruitment and retention processes of this study reviewed their experiences. The largest barriers at the macro, mediator, and micro levels identified in this study were inclement weather, transportation difficulties, and intermittent and inaccessible telephone contact. Barrier work-around practices included using the health system’s EHR to obtain current phone numbers, providing transportation assistance (bus passes or parking reimbursement), and flexible scheduling of appointments. Suggestions are intended to assist in planning for recruitment and retention strategies