pwc-archive/papers-with-abstracts huggingface dataset:
Neural circuits for dynamics-based segmentation of time series
Authors: Tiberiu Tesileanu, Siavash Golkar, Samaneh Nasiri, Anirvan M. Sengupta, Dmitri B. Chklovskii
Date: 2021-04-24 00:00:00
Tasks: Segmentation, Time Series, Time Series Analysis

The brain must extract behaviorally relevant latent variables from the signals streamed by the sensory organs. Such latent variables are often encoded in the dynamics that generated the signal rather than in the specific realization of the waveform. Therefore, one problem faced by the brain is to segment time series based on underlying dynamics. We present two algorithms for performing this segmentation task that are biologically plausible, which we define as acting in a streaming setting and all learning rules being local. One algorithm is model-based and can be derived from an optimization problem involving a mixture of autoregressive processes. This algorithm relies on feedback in the form of a prediction error, and can also be used for forecasting future samples. In some brain regions, such as the retina, the feedback connections necessary to use the prediction error for learning are absent. For this case, we propose a second, model-free algorithm that uses a running estimate of the autocorrelation structure of the signal to perform the segmentation. We show that both algorithms do well when tasked with segmenting signals drawn from autoregressive models with piecewise-constant parameters. In particular, the segmentation accuracy is similar to that obtained from oracle-like methods in which the ground-truth parameters of the autoregressive models are known. We also test our methods on datasets generated by alternating snippets of voice recordings. We provide implementations of our algorithms at https://github.com/ttesileanu/bio-time-series.
Multiple radio frequency measurement with an improved frequency resolution based on stimulated Brillouin scattering with a reduced gain bandwidth
Authors: Taixia Shi, Yang Chen
Date: 2021-04-23 00:00:00

A photonic-assisted multiple radio frequency (RF) measurement approach based on stimulated Brillouin scattering (SBS) and frequency-to-time mapping with high accuracy and high-frequency resolution is reported. A two-tone signal is single-sideband (SSB) modulated on an optical carrier via a dual-parallel Mach-Zehnder modulator to construct one SBS gain and two SBS losses for SBS gain bandwidth reduction. The unknown RF signal is also SSB modulated on a carrier that has been modulated by a sweep signal, thus the unknown RF signal is converted to a sweep optical signal along with the sweep optical carrier. The bandwidth-reduced SBS gain spectrum is detected by the sweep optical signals at different specific time, mapping the RF frequencies to the time domain. An experiment is performed. RF frequencies from 0.3 to 7.6 GHz are simultaneously measured with a root mean square error of less than 1 MHz. In addition, the frequency resolution of the measurement can be much lower than 10 MHz, which is now the best result in the RF frequency measurement methods employing the SBS effect.
Weak species in rock-paper-scissors models
Authors: P. P. Avelino, B. F. de Oliveira, R. S. Trintin
Date: 2021-04-29 00:00:00

In this letter, we investigate the population dynamics in a May-Leonard formulation of the rock-paper-scissors game in which one or two species, which we shall refer to as "weak", have a reduced predation or reproduction probability. We show that in a nonspatial model the stationary solution where all three species coexist is always unstable, while in a spatial stochastic model coexistence is possible for a wide parameter space. We find, that a reduced predation probability results in a significantly higher abundance of "weak" species, in models with either one or two "weak" species, as long as the simulation lattices are sufficiently large for coexistence to prevail. On the other hand, we show that a reduced reproduction probability has a smaller impact on the abundance of "weak" species, generally leading to a slight decrease of its population size -- the increase of the population size of one of the "weak" species being more than compensated by the reduction of the other, in the two species case. We further show that the species abundances in models where both predation and reproduction probabilities are simultaneously reduced may be accurately estimated from the results obtained considering only a reduction of either the predation or the reproduction probability.
Statistical Modeling of Airborne Virus Transmission Through Imperfectly Fitted Face Masks
Authors: Sebastian Lotter, Lukas Brand, Maximilian Schäfer, Robert Schober
Date: 2021-04-26 00:00:00

The rapid emergence and the disastrous impact of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic on public health, societies, and economies around the world has created an urgent need for understanding the pathways critical for virus transmission. Airborne virus transmission by asymptomatic SARS-CoV-2-infected individuals is considered to be a major contributor to the spread of SARS-CoV-2 and social distancing and wearing of face masks in public have been implemented as countermeasures in many countries. However, a comprehensive risk assessment framework for the airborne transmission of SARS-CoV-2 incorporating realistic assumptions on the filtration of infectious aerosols (IAs) by face masks is not available yet. In particular, in most end-to-end models for airborne virus transmission, it is neglected that the stochastic spread of IAs through imperfectly fitted face masks depends on the dynamics of the breathing of the wearer. In this paper, we consider airborne virus transmission from an infected but asymptomatic person to a healthy person, both wearing imperfectly fitted face masks, in an indoor environment. By framing the end-to-end virus transmission as a Molecular Communications (MC) system, we obtain a statistical description of the number of IAs inhaled by the healthy person subject to the respective configurations of the face masks of both persons. We demonstrate that the exhalation and inhalation air flow dynamics have a significant impact on the stochastic filtering of IAs by the face masks. Furthermore, we show that the fit of the face mask of the infected person can highly impact the infection probability. We conclude that the proposed MC model may contribute a valuable assessment tool to fight the spread of SARS-CoV-2 as it encompasses the randomness of the transmission process and enables comprehensive risk analysis beyond statistical averages.
Nonparametric Difference-in-Differences in Repeated Cross-Sections with Continuous Treatments
Authors: Xavier D'Haultfoeuille, Stefan Hoderlein, Yuya Sasaki
Date: 2021-04-29 00:00:00

This paper studies the identification of causal effects of a continuous treatment using a new difference-in-difference strategy. Our approach allows for endogeneity of the treatment, and employs repeated cross-sections. It requires an exogenous change over time which affects the treatment in a heterogeneous way, stationarity of the distribution of unobservables and a rank invariance condition on the time trend. On the other hand, we do not impose any functional form restrictions or an additive time trend, and we are invariant to the scaling of the dependent variable. Under our conditions, the time trend can be identified using a control group, as in the binary difference-in-differences literature. In our scenario, however, this control group is defined by the data. We then identify average and quantile treatment effect parameters. We develop corresponding nonparametric estimators and study their asymptotic properties. Finally, we apply our results to the effect of disposable income on consumption.
Sequential Search Models: A Pairwise Maximum Rank Approach
Authors: Jiarui Liu
Date: 2021-04-28 00:00:00

This paper studies sequential search models that (1) incorporate unobserved product quality, which can be correlated with endogenous observable characteristics (such as price) and endogenous search cost variables (such as product rankings in online search intermediaries); and (2) do not require researchers to know the true distribution of the match value between consumers and products. A likelihood approach to estimate such models gives biased results. Therefore, I propose a new estimator -- pairwise maximum rank (PMR) estimator -- for both preference and search cost parameters. I show that the PMR estimator is consistent using only data on consumers' search order among one pair of products rather than data on consumers' full consideration set or final purchase. Additionally, we can use the PMR estimator to test for the true match value distribution in the data. In the empirical application, I apply the PMR estimator to quantify the effect of rankings in Expedia hotel search using two samples of the data set, to which consumers are randomly assigned. I find the position effect to be \$0.11-\$0.36, and the effect estimated using the sample with randomly generated rankings is close to the effect estimated using the sample with endogenous rankings. Moreover, I find that the true match value distribution in the data is unlikely to be N(0,1). Likelihood estimation ignoring endogeneity gives an upward bias of at least \$1.17; misspecification of match value distribution as N(0,1) gives an upward bias of at least \$2.99.
A model of multiple hypothesis testing
Authors: Davide Viviano, Kaspar Wuthrich, Paul Niehaus
Date: 2021-04-27 00:00:00
Tasks: model

Multiple hypothesis testing practices vary widely, without consensus on which are appropriate when. This paper provides an economic foundation for these practices designed to capture leading examples, such as regulatory approval on the basis of clinical trials. In studies of multiple treatments or sub-populations, adjustments may be appropriate depending on scale economies in the research production function, with control of classical notions of compound errors emerging in some but not all cases. In studies with multiple outcomes, indexing is appropriate and adjustments to test levels may be appropriate if the intended audience is heterogeneous. Data on actual costs in the drug approval process suggest both that some adjustment is warranted in that setting and that standard procedures may be overly conservative.
Uniform inference for value functions
Authors: Sergio Firpo, Antonio F. Galvao, Thomas Parker
Date: 2019-11-22 00:00:00

We propose a method to conduct uniform inference for the (optimal) value function, that is, the function that results from optimizing an objective function marginally over one of its arguments. Marginal optimization is not Hadamard differentiable (that is, compactly differentiable) as a map between the spaces of objective and value functions, which is problematic because standard inference methods for nonlinear maps usually rely on Hadamard differentiability. However, we show that the map from objective function to an $L_p$ functional of a value function, for $1 \leq p \leq \infty$, are Hadamard directionally differentiable. As a result, we establish consistency and weak convergence of nonparametric plug-in estimates of Cram\'er-von Mises and Kolmogorov-Smirnov test statistics applied to value functions. For practical inference, we develop detailed resampling techniques that combine a bootstrap procedure with estimates of the directional derivatives. In addition, we establish local size control of tests which use the resampling procedure. Monte Carlo simulations assess the finite-sample properties of the proposed methods and show accurate empirical size and nontrivial power of the procedures. Finally, we apply our methods to the evaluation of a job training program using bounds for the distribution function of treatment effects.
Weak Instrumental Variables: Limitations of Traditional 2SLS and Exploring Alternative Instrumental Variable Estimators
Authors: Aiwei Huang, Madhurima Chandra, Laura Malkhasyan
Date: 2021-04-26 00:00:00
Tasks: Causal Inference

Instrumental variables estimation has gained considerable traction in recent decades as a tool for causal inference, particularly amongst empirical researchers. This paper makes three contributions. First, we provide a detailed theoretical discussion on the properties of the standard two-stage least squares estimator in the presence of weak instruments and introduce and derive two alternative estimators. Second, we conduct Monte-Carlo simulations to compare the finite-sample behavior of the different estimators, particularly in the weak-instruments case. Third, we apply the estimators to a real-world context; we employ the different estimators to calculate returns to schooling.
Performance of Empirical Risk Minimization for Linear Regression with Dependent Data
Authors: Christian Brownlees, Guðmundur Stefán Guðmundsson
Date: 2021-04-25 00:00:00
Tasks: regression

This paper establishes bounds on the performance of empirical risk minimization for large-dimensional linear regression. We generalize existing results by allowing the data to be dependent and heavy-tailed. The analysis covers both the cases of identically and heterogeneously distributed observations. Our analysis is nonparametric in the sense that the relationship between the regressand and the regressors is not specified. The main results of this paper show that the empirical risk minimizer achieves the optimal performance (up to a logarithmic factor) in a dependent data setting.
Hermite Polynomial-based Valuation of American Options with General Jump-Diffusion Processes
Authors: Li Chen, Guang Zhang
Date: 2021-04-24 00:00:00

We present a new approximation scheme for the price and exercise policy of American options. The scheme is based on Hermite polynomial expansions of the transition density of the underlying asset dynamics and the early exercise premium representation of the American option price. The advantages of the proposed approach are threefold. First, our approach does not require the transition density and characteristic functions of the underlying asset dynamics to be attainable in closed form. Second, our approach is fast and accurate, while the prices and exercise policy can be jointly produced. Third, our approach has a wide range of applications. We show that the proposed approximations of the price and optimal exercise boundary converge to the true ones. We also provide a numerical method based on a step function to implement our proposed approach. Applications to nonlinear mean-reverting models, double mean-reverting models, Merton's and Kou's jump-diffusion models are presented and discussed.
Causal Spillover Effects Using Instrumental Variables
Authors: Gonzalo Vazquez-Bare
Date: 2020-03-12 00:00:00

I set up a potential outcomes framework to analyze spillover effects using instrumental variables. I characterize the population compliance types in a setting in which spillovers can occur on both treatment take-up and outcomes, and provide conditions for identification of the marginal distribution of compliance types. I show that intention-to-treat (ITT) parameters aggregate multiple direct and spillover effects for different compliance types, and hence do not have a clear link to causally interpretable parameters. Moreover, rescaling ITT parameters by first-stage estimands generally recovers a weighted combination of average effects where the sum of weights is larger than one. I then analyze identification of causal direct and spillover effects under one-sided noncompliance, and show that causal effects can be estimated by 2SLS in this case. I illustrate the proposed methods using data from an experiment on social interactions and voting behavior. I also introduce an alternative assumption, independence of peers' types, that identifies parameters of interest under two-sided noncompliance by restricting the amount of heterogeneity in average potential outcomes.
Robust decision-making under risk and ambiguity
Authors: Maximilian Blesch, Philipp Eisenhauer
Date: 2021-04-23 00:00:00
Tasks: Decision Making

Economists often estimate economic models on data and use the point estimates as a stand-in for the truth when studying the model's implications for optimal decision-making. This practice ignores model ambiguity, exposes the decision problem to misspecification, and ultimately leads to post-decision disappointment. Using statistical decision theory, we develop a framework to explore, evaluate, and optimize robust decision rules that explicitly account for estimation uncertainty. We show how to operationalize our analysis by studying robust decisions in a stochastic dynamic investment model in which a decision-maker directly accounts for uncertainty in the model's transition dynamics.
The Effect of Marketing Investment on Firm Value and Systematic Risk
Authors: Musaab Mousa, Saeed Nosratabadi, Judit Sagi, Amir Mosavi
Date: 2021-04-29 00:00:00
Tasks: Marketing

Analyzing the financial benefit of marketing is still a critical topic for both practitioners and researchers. Companies consider marketing costs as a type of investment and expect this investment to be returned to the company in the form of profit. On the other hand, companies adopt different innovative strategies to increase their value. Therefore, this study aims to test the impact of marketing investment on firm value and systematic risk. To do so, data related to four Arabic emerging markets during the period 2010-2019 are considered, and firm share price and beta share are considered to measure firm value and systematic risk, respectively. Since a firm's ownership concentration is a determinant factor in firm value and systematic risk, this variable is considered a moderated variable in the relationship between marketing investment and firm value and systematic risk. The findings of the study, using panel data regression, indicate that increasing investment in marketing has a positive effect on the firm value valuation model. It is also found that the ownership concentration variable has a reinforcing role in the relationship between marketing investment and firm value. It is also disclosed that it moderates the systematic risk aligned with the monitoring impact of controlling shareholders. This study provides a logical combination of governance-marketing dimensions to interpret performance indicators in the capital market.
Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS
Authors: Saeed Nosratabadi, Sina Ardabili, Zoltan Lakner, Csaba Mako, Amir Mosavi
Date: 2021-04-29 00:00:00
Tasks: BIG-bench Machine Learning, Time Series Analysis

Advancing models for accurate estimation of food production is essential for policymaking and managing national plans of action for food security. This research proposes two machine learning models for the prediction of food production. The adaptive network-based fuzzy inference system (ANFIS) and multilayer perceptron (MLP) methods are used to advance the prediction models. In the present study, two variables of livestock production and agricultural production were considered as the source of food production. Three variables were used to evaluate livestock production, namely livestock yield, live animals, and animal slaughtered, and two variables were used to assess agricultural production, namely agricultural production yields and losses. Iran was selected as the case study of the current study. Therefore, time-series data related to livestock and agricultural productions in Iran from 1961 to 2017 have been collected from the FAOSTAT database. First, 70% of this data was used to train ANFIS and MLP, and the remaining 30% of the data was used to test the models. The results disclosed that the ANFIS model with Generalized bell-shaped (Gbell) built-in membership functions has the lowest error level in predicting food production. The findings of this study provide a suitable tool for policymakers who can use this model and predict the future of food production to provide a proper plan for the future of food security and food supply for the next generations.
Stakeholder dynamics in residential solar energy adoption: findings from focus group discussions in Germany
Authors: Fabian Scheller, Isabel Doser, Emily Schulte, Simon Johanning, Russell McKenna, Thomas Bruckner
Date: 2021-04-29 00:00:00
Tasks: Decision Making

Although there is a clear indication that stages of residential decision making are characterized by their own stakeholders, activities, and outcomes, many studies on residential low-carbon technology adoption only implicitly address stage-specific dynamics. This paper explores stakeholder influences on residential photovoltaic adoption from a procedural perspective, so-called stakeholder dynamics. The major objective is the understanding of underlying mechanisms to better exploit the potential for residential photovoltaic uptake. Four focus groups have been conducted in close collaboration with the independent institute for social science research SINUS Markt- und Sozialforschung in East Germany. By applying a qualitative content analysis, major influence dynamics within three decision stages are synthesized with the help of egocentric network maps from the perspective of residential decision-makers. Results indicate that actors closest in terms of emotional and spatial proximity such as members of the social network represent the major influence on residential PV decision-making throughout the stages. Furthermore, decision-makers with a higher level of knowledge are more likely to move on to the subsequent stage. A shift from passive exposure to proactive search takes place through the process, but this shift is less pronounced among risk-averse decision-makers who continuously request proactive influences. The discussions revealed largely unexploited potential regarding the stakeholders local utilities and local governments who are perceived as independent, trustworthy and credible stakeholders. Public stakeholders must fulfill their responsibility in achieving climate goals by advising, assisting, and financing services for low-carbon technology adoption at the local level. Supporting community initiatives through political frameworks appears to be another promising step.
The impact of past pandemics on CO$_2$ emissions and transition to renewable energy
Authors: Michal Brzezinski
Date: 2021-04-29 00:00:00

We estimate the short- to medium term impact of six major past pandemic crises on the CO2 emissions and energy transition to renewable electricity. The results show that the previous pandemics led on average to a 3.4-3.7% fall in the CO2 emissions in the short-run (1-2 years since the start of the pandemic). The effect is present only in the rich countries, as well as in countries with the highest pandemic death toll (where it disappears only after 8 years) and in countries that were hit by the pandemic during economic recessions. We found that the past pandemics increased the share of electricity generated from renewable sources within the fiveyear horizon by 1.9-2.3 percentage points in the OECD countries and by 3.2-3.9 percentage points in countries experiencing economic recessions. We discuss the implications of our findings in the context of CO2 emissions and the transition to renewable energy in the post-COVID-19 era.