Sharp identified sets for discrete variable IV models

Instrumental variable models for discrete outcomes are set, not point, identifying. The paper characterises identified sets of structural functions when endogenous variables are discrete. Identified sets are unions of large numbers of convex sets and may not be convex nor even connected. Each of the component sets is a projection of a convex set that resides in a much higher dimensional space onto the space in which a structural function resides. The paper develops a symbolic expression for this projection and gives a constructive demonstration that it is indeed the identified set. We provide a MathematicaTM notebook which computes the set symbolically. We derive properties of the set, suggest how the set can be used in practical econometric analysis when outcomes and endogenous variables are discrete and propose a method for estimating identified sets under parametric or shape restrictions. We develop an expression for a set of structural functions for the case in which endogenous variables are continuous or mixed discrete-continuous and show that this set contains all structural functions in the identified set in the non-discrete case.

IV models of ordered choice

This paper studies single equation instrumental variable models of ordered choice in which explanatory variables may be endogenous. The models are weakly restrictive, leaving unspecified the mechanism that generates endogenous variables. These incomplete models are set, not point, identifying for parametrically (e.g. ordered probit) or nonparametrically specified structural functions. The paper gives results on the properties of the identified set for the case in which potentially endogenous explanatory variables are discrete. The results are used as the basis for calculations showing the rate of shrinkage of identified sets as the number of classes in which the outcome is categorised increases.

An instrumental variable model of multiple discrete choice

This paper studies identification of latent utility functions in multiple discrete choice models in which there may be endogenous explanatory variables, that is explanatory variables that are not restricted to be distributed independently of the unobserved determinants of latent utilities. The model does not employ large support, special regressor or control function restrictions, indeed it is silent about the process delivering values of endogenous explanatory variables and in this respect it is incomplete. Instead the model employs instrumental variable restrictions requiring the existence of instrumental variables which are excluded from latent utilities and distributed independently of the unobserved components of utilities. We show that the model delivers set identification of the latent utility functions and we characterize sharp bounds on those functions. We develop easy-to-compute outer regions which in parametric models require little more calculation than what is involved in a conventional maximum likelihood analysis. The results are illustrated using a model which is essentially the parametric conditional logit model of McFadden (1974) but with potentially endogenous explanatory variables and instrumental variable restrictions. The method employed has wide applicability and for the first time brings instrumental variable methods to bear on structural models in which there are multiple unobservables in a structural equation.

An instrumental variable model of multiple discrete choice

This paper studies identification of latent utility functions in multiple discrete choice models in which there may be endogenous explanatory variables, that is explanatory variables that are not restricted to be distributed independently of the unobserved determinants of latent utilities. The model does not employ large support, special regressor or control function restrictions, indeed it is silent about the process delivering values of endogenous explanatory variables and in this respect it is incomplete. Instead the model employs instrumental variable restrictions requiring the existence of instrumental variables which are excluded from latent utilities and distributed independently of the unobserved components of utilities. We show that the model delivers set, not point, identification of the latent utility functions and we characterize sharp bounds on those functions. We develop easy-to-compute outer regions which in parametric models require little more calculation than what is involved in a conventional maximum likelihood analysis. The results are illustrated using a model which is essentially the parametric conditional logit model of McFadden (1974) but with potentially endogenous explanatory variables and instrumental variable restrictions. The method employed has wide applicability and for the first time brings instrumental variable methods to bear on structural models in which there are multiple unobservables in a structural equation.

POLICY AND TECHNOLOGY IMPACT ON PRICING OF GRID-SCALE BATTERIES IN NEW ENGLAND INDEPENDENT SYSTEM OPERATOR REGION

Energy storage facilities may enable variable renewable energy penetration for greater carbon-free resource adequacy, displacing carbon-intensive resource adequacy from fossil resources. If successful, this approach could lower carbon emissions from the electricity sector. This capstone project investigates the impact of standalone storage Investment Tax Credits (ITC) and Production Tax Credit (PTC) policy on the market potential for deploying utility-scale battery energy storage systems (BESS) in the Independent System Operator New England (ISONE) market. It includes a model to develop business plans with financial tables, which have historically been difficult to model due to capacity, duration, state of charge and hourly-dependent cost of operation and revenue from various markets. The model determines the internal rate of return of the facility over an assumed lifespan. The impact of policy incentives like investment tax credits and technology sensitivities like round trip efficiency (RTE) and capital costs (CAPEX) were evaluated. The ultimate result of this capstone project was the creation of a tool to help plan grid-scale energy storage projects and evaluate the impacts of tax and other incentive policy proposals on real world project proposals

Characterization of Mg-based Bimetal Treatment of Insensitive Munition 2,4-dinitroanisole

The manufacturing of insensitive munition 2,4-dinitroanisole (DNAN) generates waste streams that require treatment. DNAN has been treated previously with zero-valent iron (ZVI) and Fe-based bimetals. Use of Mg-based bimetals offers certain advantages including potential higher reactivity and relative insensitivity to pH conditions. This work reports preliminary findings of DNAN degradation by three Mg-based bimetals: Mg/Cu, Mg/Ni, and Mg/Zn. Treatment of DNAN by all three bimetals is highly effective in aqueous solutions (\u3e 89% removal) and wastewater (\u3e 91% removal) in comparison with treatment solely with zero-valent magnesium (ZVMg; 35% removal). Investigation of reaction byproducts supports a partial degradation pathway involving reduction of the ortho or para nitro to amino group, leading to 2-amino-4-nitroanisole (2-ANAN) and 4-amino-2-nitroanisole (4-ANAN). Further reduction of the second nitro group leads to 2,4-diaminoanisole (DAAN). These byproducts are detected in small quantities in the aqueous phase. Carbon mass balance analysis suggests near-complete closure (91%) with 12.4 and 78.4% of the total organic carbon (TOC) distributed in the aqueous and mineral bimetal phases, respectively. Post-treatment surface mineral phase analysis indicates Mg(OH)2 as the main oxidized species; oxide formation does not appear to impair treatment

Closing the Gap: First Year Success in College Mathematics at an HBCU

At our Historically-Black University, about 89% of first-year students place into developmental mathematics, negatively impacting retention and degree completion. In 2012, an NSF-funded learning enrichment project began offering the introductory and developmental mathematics courses on-line over the summer to incoming science, technology, engineering and mathematics (STEM) majors at no cost. Passing rates for the summer on-line classes were around 80%, and students in the on-line classes scored equivalently on the common departmental final exams as students taking the classes in the traditional format. For students who passed the on-line classes, their performance in the following classes (College Algebra and Trigonometry) exceeded that of students who progressed to those courses by taking the traditional series of in-person courses. Three years of data show that students who started college with an on-line mathematics course in a summer bridge program had a higher first year GPA, a better first year retention rate and earned significantly more credits in their first year than the overall population of STEM students. These results suggest that offering introductory mathematics courses on-line as part of a freshman bridge program is an effective, scalable intervention to increase the academic success of students who enter college under-prepared in mathematics. The positive results are particularly exciting since the students in our project were 87% minority

Agricultural intensification, priming for persistence and the emergence of Nipah virus: a lethal bat-borne zoonosis

Emerging zoonoses threaten global health, yet the processes by which they emerge are complex and poorly understood. Nipah virus (NiV) is an important threat owing to its broad host and geographical range, high case fatality, potential for human-to-human transmission and lack of effective prevention or therapies. Here, we investigate the origin of the first identified outbreak of NiV encephalitis in Malaysia and Singapore. We analyse data on livestock production from the index site (a commercial pig farm in Malaysia) prior to and during the outbreak, on Malaysian agricultural production, and from surveys of NiV's wildlife reservoir (flying foxes). Our analyses suggest that repeated introduction of NiV from wildlife changed infection dynamics in pigs. Initial viral introduction produced an explosive epizootic that drove itself to extinction but primed the population for enzootic persistence upon reintroduction of the virus. The resultant within-farm persistence permitted regional spread and increased the number of human infections. This study refutes an earlier hypothesis that anomalous El Niño Southern Oscillation-related climatic conditions drove emergence and suggests that priming for persistence drove the emergence of a novel zoonotic pathogen. Thus, we provide empirical evidence for a causative mechanism previously proposed as a precursor to widespread infection with H5N1 avian influenza and other emerging pathogens

The Eighth Data Release of the Sloan Digital Sky Survey: First Data from SDSS-III

The Sloan Digital Sky Survey (SDSS) started a new phase in August 2008, with new instrumentation and new surveys focused on Galactic structure and chemical evolution, measurements of the baryon oscillation feature in the clustering of galaxies and the quasar Ly alpha forest, and a radial velocity search for planets around ~8000 stars. This paper describes the first data release of SDSS-III (and the eighth counting from the beginning of the SDSS). The release includes five-band imaging of roughly 5200 deg^2 in the Southern Galactic Cap, bringing the total footprint of the SDSS imaging to 14,555 deg^2, or over a third of the Celestial Sphere. All the imaging data have been reprocessed with an improved sky-subtraction algorithm and a final, self-consistent photometric recalibration and flat-field determination. This release also includes all data from the second phase of the Sloan Extension for Galactic Understanding and Evolution (SEGUE-2), consisting of spectroscopy of approximately 118,000 stars at both high and low Galactic latitudes. All the more than half a million stellar spectra obtained with the SDSS spectrograph have been reprocessed through an improved stellar parameters pipeline, which has better determination of metallicity for high metallicity stars.Comment: Astrophysical Journal Supplements, in press (minor updates from submitted version

The Ninth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the SDSS-III Baryon Oscillation Spectroscopic Survey

The Sloan Digital Sky Survey III (SDSS-III) presents the first spectroscopic data from the Baryon Oscillation Spectroscopic Survey (BOSS). This ninth data release (DR9) of the SDSS project includes 535,995 new galaxy spectra (median z=0.52), 102,100 new quasar spectra (median z=2.32), and 90,897 new stellar spectra, along with the data presented in previous data releases. These spectra were obtained with the new BOSS spectrograph and were taken between 2009 December and 2011 July. In addition, the stellar parameters pipeline, which determines radial velocities, surface temperatures, surface gravities, and metallicities of stars, has been updated and refined with improvements in temperature estimates for stars with T_eff<5000 K and in metallicity estimates for stars with [Fe/H]>-0.5. DR9 includes new stellar parameters for all stars presented in DR8, including stars from SDSS-I and II, as well as those observed as part of the SDSS-III Sloan Extension for Galactic Understanding and Exploration-2 (SEGUE-2). The astrometry error introduced in the DR8 imaging catalogs has been corrected in the DR9 data products. The next data release for SDSS-III will be in Summer 2013, which will present the first data from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) along with another year of data from BOSS, followed by the final SDSS-III data release in December 2014.Comment: 9 figures; 2 tables. Submitted to ApJS. DR9 is available at http://www.sdss3.org/dr