Pass-Through And The Prediction Of Merger Price Effects

We use Monte Carlo experiments to study how pass-through can improve merger price predictions, focusing on the first order approximation (FOA) proposed in Jaffe and Weyl [2013]. FOA addresses the functional form misspecification that can exist in standard merger simulations. We find that the predictions of FOA are tightly distributed around the true price effects if pass-through is precise, but that measurement error in pass-through diminishes accuracy. As a comparison to FOA, we also study a methodology that uses pass-through to select among functional forms for use in simulation. This alternative also increases accuracy relative to standard merger simulation and proves more robust to measurement error

Upward Pricing Pressure As A Predictor Of Merger Price Effects

We use Monte Carlo experiments to evaluate whether “upward pricing pressure” (UPP) accurately predicts the price effects of mergers, motivated by the observation that UPP is a restricted form of the first order approximation derived in Jaffe and Weyl (2013). Results indicate that UPP is quite accurate with standard log-concave demand systems, but understates price effects if demand exhibits greater convexity. Prediction error does not systematically exceed that of misspecified simulation models, nor is it much greater than that of correctly-specified models simulated with imprecise demand elasticities. The results also support that UPP provides accurate screens for anticompetitive mergers

The Mobile Phone and You: Human Interaction and Integration with Mobile Technology

What is the impact of mobile phones on those who use them? Does the use of mobile phones affect sociocultural systems when they are introduced? With the prevalence of the mobile phone in society today it is imperative that mobile phone use is studied. Through the use of ethnography, I have studied students at Georgia State University in order to understand the mobile phones influence on this particular culture. I have found that my participants have been changed through use of this device as it has become a part of themselves, adding functionality to users while splitting their focus between the digital world mediated by the phone and physical reality. I found that participant’s sentiments of their phones were conflicting. They praised their device while remaining wary about its effects on them and others

Reducing Reparameterization Gradient Variance

Optimization with noisy gradients has become ubiquitous in statistics and machine learning. Reparameterization gradients, or gradient estimates computed via the "reparameterization trick," represent a class of noisy gradients often used in Monte Carlo variational inference (MCVI). However, when these gradient estimators are too noisy, the optimization procedure can be slow or fail to converge. One way to reduce noise is to use more samples for the gradient estimate, but this can be computationally expensive. Instead, we view the noisy gradient as a random variable, and form an inexpensive approximation of the generating procedure for the gradient sample. This approximation has high correlation with the noisy gradient by construction, making it a useful control variate for variance reduction. We demonstrate our approach on non-conjugate multi-level hierarchical models and a Bayesian neural net where we observed gradient variance reductions of multiple orders of magnitude (20-2,000x)

Approximate Inference for Constructing Astronomical Catalogs from Images

We present a new, fully generative model for constructing astronomical catalogs from optical telescope image sets. Each pixel intensity is treated as a random variable with parameters that depend on the latent properties of stars and galaxies. These latent properties are themselves modeled as random. We compare two procedures for posterior inference. One procedure is based on Markov chain Monte Carlo (MCMC) while the other is based on variational inference (VI). The MCMC procedure excels at quantifying uncertainty, while the VI procedure is 1000 times faster. On a supercomputer, the VI procedure efficiently uses 665,000 CPU cores to construct an astronomical catalog from 50 terabytes of images in 14.6 minutes, demonstrating the scaling characteristics necessary to construct catalogs for upcoming astronomical surveys.Comment: accepted to the Annals of Applied Statistic

Summer habitat use and movements of invasive wild pigs (Sus scrofa) in Canadian agro-ecosystems

Resource selection informs understanding of a species’ ecology and is especially pertinent for invasive species. Since introduced to Canada, wild pigs (Sus scrofa Linnaeus, 1978) remain understudied despite recognized negative impacts on native and agricultural systems globally. Elsewhere in North America, pigs typically use forests and forage in agricultural crops. We hypothesized Canadian wild pigs would behave similarly, and using GPS locations from 15 individuals, we examined diel and seasonal resource selection and movement in the Canadian prairie region. Forests were predominately selected during the day, while corn (Zea mays L.), oilseeds, and wheat (Triticum aestivum L.) were predominately selected at night. Forests and corn were consistently selected throughout the growing season.Wetlands and forests showed greater use rates than other habitats, with evident trade-offs as crop use increased with the timing of maturation. Activity was consistent with foraging in growing crops. Results indicate diel patterns were likely a function of short-term needs to avoid daytime anthropogenic risk, while seasonal patterns demonstrate how habitats that fill multiple functional roles——food, cover, and thermoregulation——can be optimized. Understanding selection by invasive species is an important step in understanding their potential environmental impacts in novel environments and informs their management