Economic Performance, Voting, and Political Support: A Unified Approach

A presidential vote function and a presidential approval ratings function are jointly estimated for U.S. post-war observations. The estimation technique treats the two equations as seemingly unrelated regressions with unequal numbers of observations. Cross-equation restrictions implying that voters and poll respondents use identical standards in judging the economic performance of incumbents are imposed and tested. Estimates show that both votes and approval ratings are influenced by GNP growth and inflation. The results suggest that poll respondents are more inflation averse than voters; however, tests of this hypothesis are not conclusive

Campaign Contributions and Congressional Voting: A Simultaneous Probit-Tobit Model

Full-information maximum likelihood (FIML) estimates of the simultaneous probit-Tobit (SPT) model suggest that effects of campaign contributions on voting are smaller than single equation probit estimates would indicate. The author has generally unable to conclude that contributions have a significant impact on voting decisions, apparently votes are most often decided on the basis of personal ideology or preferences of constituents. These findings differ markedly from earlier results of economists Gary C. Durden and Jonathan J. Silberman, whose single equation models showed a substantial impact of contributions on voting decisions. Despite the lack of significance according to model SPT, it would not, however, be appropriate to unambiguously conclude that contributions have no effects on voting. For six of eight coefficients the anticipated positive sign resulted and one coefficient remained marginally significant. The article also shows that the lack of significance is attributable not only to smaller coefficient size, but also to larger standard errors. The FIML estimates of the contribution coefficients are not very precise

Presidential Popularity and Macroeconomic Performance: Are Voters Really So Naive?

The article focuses on the relationships between the macroeconomic performance of political administration and their popularity or vote getting ability. All of the studies that has been performed to analyze the relationships agree that votes and popularity can be explained well by models which suppose that voters judge policy makers on the basis of retrospective evaluation of past macroeconomic outcomes. While conventional popularity functions assume that voters simply punish inflation and reward output or low unemployment, voters who understand the long and short run relationships noted above would evaluate policymakers differently. Inflation in a given period is largely determined by past expectations of inflation, which cannot easily be controlled by current policy choices. The results of a study done by the author, show that data on presidential popularity are consistent with the hypothesis that voters are concerned with the future consequences of current economic policy choices and are aware of the nature of constraints imposed by economic reality

Monetary Policy Preferences of Individual FOMC Members: A Content Analysis of the Memoranda of Discussion

The Memoranda of Discussion provide detailed records of Federal Open Market Committee (FOMC) meeting deliberations. Procedures are developed for coding the textual data in the Memoranda and assessing the reliability of those codings. The codings are then used in the estimation of parameters of individual FOMC members\u27 reaction functions. Data from the 1970 to 1976 period are employed in the estimation. In the future, similar methods could be used to analyze newly released transcripts of FOMC meetings held after 1976

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

The Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation.Comment: 39 pages, 19 figure

Scintillation light detection in the 6-m drift-length ProtoDUNE Dual Phase liquid argon TPC

DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6  ×  6  ×  6 m 3 liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019-2020 as a prototype of the DUNE Far Detector. Charged particles propagating through the LArTPC produce ionization and scintillation light. The scintillation light signal in these detectors can provide the trigger for non-beam events. In addition, it adds precise timing capabilities and improves the calorimetry measurements. In ProtoDUNE-DP, scintillation and electroluminescence light produced by cosmic muons in the LArTPC is collected by photomultiplier tubes placed up to 7 m away from the ionizing track. In this paper, the ProtoDUNE-DP photon detection system performance is evaluated with a particular focus on the different wavelength shifters, such as PEN and TPB, and the use of Xe-doped LAr, considering its future use in giant LArTPCs. The scintillation light production and propagation processes are analyzed and a comparison of simulation to data is performed, improving understanding of the liquid argon properties

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation.Comment: 31 pages, 15 figure

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation

Design, construction and operation of the ProtoDUNE-SP Liquid Argon TPC

The ProtoDUNE-SP detector is a single-phase liquid argon time projection chamber (LArTPC) that was constructed and operated in the CERN North Area at the end of the H4 beamline. This detector is a prototype for the first far detector module of the Deep Underground Neutrino Experiment (DUNE), which will be constructed at the Sandford Underground Research Facility (SURF) in Lead, South Dakota, U.S.A. The ProtoDUNE-SP detector incorporates full-size components as designed for DUNE and has an active volume of 7 × 6 × 7.2 m3. The H4 beam delivers incident particles with well-measured momenta and high-purity particle identification. ProtoDUNE-SP's successful operation between 2018 and 2020 demonstrates the effectiveness of the single-phase far detector design. This paper describes the design, construction, assembly and operation of the detector components