pwc-archive/papers-with-abstracts huggingface dataset:
Identifying causal effects with subjective ordinal outcomes
Authors: Leonard Goff
Date: 2022-12-30 00:00:00
Tasks: Causal Identification, Causal Inference, regression, Unity

Survey questions often ask respondents to select from ordered scales where the meanings of the categories are subjective, leaving each individual free to apply their own definitions in answering. This paper studies the use of these responses as an outcome variable in causal inference, accounting for variation in interpretation of the categories across individuals. I find that when a continuous treatment variable is statistically independent of both i) potential outcomes; and ii) heterogeneity in reporting styles, a nonparametric regression of response category number on that treatment variable recovers a quantity proportional to an average causal effect among individuals who are on the margin between successive response categories. The magnitude of a given regression coefficient is not meaningful on its own, but the ratio of local regression derivatives with respect to two such treatment variables identifies the relative magnitudes of convex averages of their effects. These results can be seen as limiting cases of analogous results for binary treatment variables, though comparisons of magnitude involving discrete treatments are not as readily interpretable outside of the limit. I obtain a partial identification result for comparisons involving discrete treatments under further assumptions. An empirical application illustrates the results by revisiting the effects of income comparisons on subjective well-being, without assuming cardinality or interpersonal comparability of responses.
Empirical Bayes When Estimation Precision Predicts Parameters
Authors: Jiafeng Chen
Date: 2022-12-29 00:00:00

Gaussian empirical Bayes methods usually maintain a precision independence assumption: The unknown parameters of interest are independent from the known standard errors of the estimates. This assumption is often theoretically questionable and empirically rejected. This paper proposes to model the conditional distribution of the parameter given the standard errors as a flexibly parametrized location-scale family of distributions, leading to a family of methods that we call CLOSE. The CLOSE framework unifies and generalizes several proposals under precision dependence. We argue that the most flexible member of the CLOSE family is a minimalist and computationally efficient default for accounting for precision dependence. We analyze this method and show that it is competitive in terms of the regret of subsequent decisions rules. Empirically, using CLOSE leads to sizable gains for selecting high-mobility Census tracts.
What Estimators Are Unbiased For Linear Models?
Authors: Lihua Lei, Jeffrey Wooldridge
Date: 2022-12-29 00:00:00

The recent thought-provoking paper by Hansen [2022, Econometrica] proved that the Gauss-Markov theorem continues to hold without the requirement that competing estimators are linear in the vector of outcomes. Despite the elegant proof, it was shown by the authors and other researchers that the main result in the earlier version of Hansen's paper does not extend the classic Gauss-Markov theorem because no nonlinear unbiased estimator exists under his conditions. To address the issue, Hansen [2022] added statements in the latest version with new conditions under which nonlinear unbiased estimators exist. Motivated by the lively discussion, we study a fundamental problem: what estimators are unbiased for a given class of linear models? We first review a line of highly relevant work dating back to the 1960s, which, unfortunately, have not drawn enough attention. Then, we introduce notation that allows us to restate and unify results from earlier work and Hansen [2022]. The new framework also allows us to highlight differences among previous conclusions. Lastly, we establish new representation theorems for unbiased estimators under different restrictions on the linear model, allowing the coefficients and covariance matrix to take only a finite number of values, the higher moments of the estimator and the dependent variable to exist, and the error distribution to be discrete, absolutely continuous, or dominated by another probability measure. Our results substantially generalize the claims of parallel commentaries on Hansen [2022] and a remarkable result by Koopmann [1982].
Supercompliers
Authors: Matthew L. Comey, Amanda R. Eng, Pauline Leung, Zhuan Pei
Date: 2022-12-28 00:00:00
Tasks: regression

In a binary-treatment instrumental variable framework, we define supercompliers as the subpopulation whose treatment take-up positively responds to eligibility and whose outcome positively responds to take-up. Supercompliers are the only subpopulation to benefit from treatment eligibility and, hence, are important for policy. We provide tools to characterize supercompliers under a set of jointly testable assumptions. Specifically, we require standard assumptions from the local average treatment effect literature plus an outcome monotonicity assumption. Estimation and inference can be conducted with instrumental variable regression. In two job-training experiments, we demonstrate our machinery's utility, particularly in incorporating social welfare weights into marginal-value-of-public-funds analysis.
Forward Orthogonal Deviations GMM and the Absence of Large Sample Bias
Authors: Robert F. Phillips
Date: 2022-12-28 00:00:00

It is well known that generalized method of moments (GMM) estimators of dynamic panel data regressions can have significant bias when the number of time periods ($T$) is not small compared to the number of cross-sectional units ($n$). The bias is attributed to the use of many instrumental variables. This paper shows that if the maximum number of instrumental variables used in a period increases with $T$ at a rate slower than $T^{1/2}$, then GMM estimators that exploit the forward orthogonal deviations (FOD) transformation do not have asymptotic bias, regardless of how fast $T$ increases relative to $n$. This conclusion is specific to using the FOD transformation. A similar conclusion does not necessarily apply when other transformations are used to remove fixed effects. Monte Carlo evidence illustrating the analytical results is provided.
Innovation in times of Covid-19
Authors: Torsten Heinrich, Jangho Yang
Date: 2022-12-29 00:00:00
Tasks: Management

Did the Covid-19 pandemic have an impact on innovation? Past economic disruptions, anecdotal evidence, and the previous literature suggest a decline with substantial differences between industries. We leverage USPTO patent application data to investigate and quantify the disturbance. We assess differences by field of technology (at the CPC subclass level) as well as the impact of direct and indirect relevance for the management of the pandemic. Direct Covid-19 relevance is identified from a keyword search of the patent application fulltexts; indirect Covid-19 relevance is derived from past CPC subclass to subclass citation patterns. We find that direct Covid-19 relevance is associated with a strong boost to the growth of the number of patent applications in the first year of the pandemic at the same order of magnitude (in percentage points) as the percentage of patents referencing Covid-19. We find no effect for indirect Covid-19 relevance, indicating a focus on applied research at the expense of more basic research. Fields of technology (CPC mainsections) have an additional significant impact, with, e.g., mainsections A (human necessities) and C (chemistry, metallurgy) having a strong performance.
Innovation through intra and inter-regional interaction in economic geography
Authors: José M. Gaspar, Minoru Osawa
Date: 2022-12-29 00:00:00

We develop a two-region economic geography model with vertical innovations that improve the quality of manufactured varieties produced in each region. The chance of innovation depends on the \emph{related variety}, i.e. the importance of interaction between researchers within the same region rather than across different regions. As economic integration increases from a low level, a higher related variety is associated with more agglomerated spatial configurations. However, if the interaction with foreign scientists is relatively more important for innovation, economic activities may (completely) re-disperse after an initial phase of agglomeration due to the increase in the relative importance of a higher chance of innovation in the less industrialized region. This non-monotonic relationship between economic integration and spatial imbalances may exhibit very diverse qualitative properties, not yet described in the literature.
Optimal Robust Mechanism in Bilateral Trading
Authors: Komal Malik
Date: 2022-12-29 00:00:00

We consider a model of bilateral trade with private values. The value of the buyer and the cost of the seller are jointly distributed. The true joint distribution is unknown to the designer, however, the marginal distributions of the value and the cost are known to the designer. The designer wants to find a trading mechanism that is robustly Bayesian incentive compatible, robustly individually rational, budget-balanced and maximizes the expected gains from trade over all such mechanisms. We refer to such a mechanism as an optimal robust mechanism. We establish equivalence between Bayesian incentive compatible mechanisms (BIC) and dominant strategy mechanisms (DSIC). We characterise the worst distribution for a given mechanism and use this characterisation to find an optimal robust mechanism. We show that there is an optimal robust mechanism that is deterministic (posted-price), dominant strategy incentive compatible, and ex-post individually rational. We also derive an explicit expression of the posted-price of such an optimal robust mechanism. We also show the equivalence between the efficiency gains from the optimal robust mechanism (max-min problem) and guaranteed efficiency gains if the designer could choose the mechanism after observing the true joint distribution (min-max problem).
Detection of Malfunctioning Modules in Photovoltaic Power Plants using Unsupervised Feature Clustering Segmentation Algorithm
Authors: Divyanshi Dwivedi, Pradeep Kumar Yemula, Mayukha Pal
Date: 2022-12-30 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

The energy transition towards photovoltaic solar energy has evolved to be a viable and sustainable source for the generation of electricity. It has effectively emerged as an alternative to the conventional mode of electricity generation for developing countries to meet their energy requirement. Thus, many solar power plants have been set up across the globe. However, in these large-scale or remote solar power plants, monitoring and maintenance persist as challenging tasks, mainly identifying faulty or malfunctioning cells in photovoltaic (PV) panels. In this paper, we use an unsupervised deep-learning image segmentation model for the detection of internal faults such as hot spots and snail trails in PV panels. Generally, training or ground truth labels are not available for large solar power plants, thus the proposed model is highly recommended as it does not require any prior learning or training. It extracts the features from the input image and segments out the faults in the image. Here we use infrared thermal images of the PV panel as input, passed to a convolutional neural network which assigns cluster labels to the pixels. Further, optimize the pixel labels, features and model parameters using backpropagation based on iterative stochastic gradient descent. Then, we compute similarity loss and spatial continuity loss to assign the same label to the pixel with similar features and spatial continuity to reduce noises in the image segmentation process. The effectiveness of the proposed approach was examined on an online available dataset for the recognition of snail trails and hot spot failures in monocrystalline solar panels.
Selecting Classifiers by Pooling over Cross-Validation Results in More Consistency in Small-Sample Classification of Atrial Flutter Localization
Authors: Muhammad Haziq Bin Kamarul Azman, Olivier Meste, Kushsairy Kadir
Date: 2022-12-30 00:00:00

Selecting learning machines such as classifiers is an important task when using AI in the clinic. K-fold crossvalidation is a practical technique that allows simple inference of such machines. However, the recipe generates many models and does not provide a means to determine the best one. In this paper, a modified recipe is presented, that generates more consistent machines with similar on-average performance, but less extra-sample loss variance and less feature bias. A use case is provided by applying the recipe onto the atrial flutter localization problem.
Capacity Analysis of Holographic MIMO Channels with Practical Constraints
Authors: Yuan Zhang, Jianhua Zhang, Yuxiang Zhang, Yuan YAO, Guangyi Liu
Date: 2022-12-29 00:00:00

Holographic Multiple-Input and Multiple-Output (MIMO) is envisioned as a promising technology to realize unprecedented spectral efficiency by integrating a large number of antennas into a compact space. Most research on holographic MIMO is based on isotropic scattering environments, and the antenna gain is assumed to be unlimited by deployment space. However, the channel might not satisfy isotropic scattering because of generalized angle distributions, and the antenna gain is limited by the array aperture in reality. In this letter, we aim to analyze the holographic MIMO channel capacity under practical angle distribution and array aperture constraints. First, we calculate the spectral density for generalized angle distributions by introducing a wavenumber domain-based method. And then, the capacity under generalized angle distributions is analyzed and two different aperture schemes are considered. Finally, numerical results show that the capacity is obviously affected by angle distribution at high signal-to-noise ratio (SNR) but hardly affected at low SNR, and the capacity will not increase infinitely with antenna density due to the array aperture constraint.
Compressed domain vibration detection and classification for distributed acoustic sensing
Authors: Xingliang Shen, Huan Wu, Kun Zhu, Yujia Li, Hua Zheng, Jialong Li, Liyang Shao, Perry Ping Shum, Chao Lu
Date: 2022-12-27 00:00:00
Tasks: Classification, Compressive Sensing

Distributed acoustic sensing (DAS) is a novel enabling technology that can turn existing fibre optic networks to distributed acoustic sensors. However, it faces the challenges of transmitting, storing, and processing massive streams of data which are orders of magnitude larger than that collected from point sensors. The gap between intensive data generated by DAS and modern computing system with limited reading/writing speed and storage capacity imposes restrictions on many applications. Compressive sensing (CS) is a revolutionary signal acquisition method that allows a signal to be acquired and reconstructed with significantly fewer samples than that required by Nyquist-Shannon theorem. Though the data size is greatly reduced in the sampling stage, the reconstruction of the compressed data is however time and computation consuming. To address this challenge, we propose to map the feature extractor from Nyquist-domain to compressed-domain and therefore vibration detection and classification can be directly implemented in compressed-domain. The measured results show that our framework can be used to reduce the transmitted data size by 70% while achieves 99.4% true positive rate (TPR) and 0.04% false positive rate (TPR) along 5 km sensing fibre and 95.05% classification accuracy on a 5-class classification task.
A Stackelberg reinsurance-investment game under $α$-maxmin mean-variance criterion and stochastic volatility
Authors: Guohui Guan, Zongxia Liang, Yilun Song
Date: 2022-12-29 00:00:00

This paper investigates a Stackelberg game between an insurer and a reinsurer under the $\alpha$-maxmin mean-variance criterion. The insurer can purchase per-loss reinsurance from the reinsurer. With the insurer's feedback reinsurance strategy, the reinsurer optimizes the reinsurance premium in the Stackelberg game. The financial market consists of cash and stock with Heston's stochastic volatility. Both the insurer and reinsurer maximize their respective $\alpha$-maxmin mean-variance preferences in the market. The criterion is time-inconsistent and we derive the equilibrium strategies by the extended Hamilton-Jacobi-Bellman equations. Similar to the non-robust case in Li and Young (2022), excess-of-loss reinsurance is the optimal form of reinsurance strategy for the insurer. The equilibrium investment strategy is determined by a system of Riccati differential equations. Besides, the equations determining the equilibrium reinsurance strategy and reinsurance premium rate are given semi-explicitly, which is simplified to an algebraic equation in a specific example. Numerical examples illustrate that the game between the insurer and reinsurer makes the insurance more radical when the agents become more ambiguity aversion or risk aversion. Furthermore, the level of ambiguity, ambiguity attitude, and risk attitude of the insurer (reinsurer) have similar effects on the equilibrium reinsurance strategy, reinsurance premium, and investment strategy.
Constrained monotone mean-variance problem with random coefficients
Authors: Ying Hu, Xiaomin Shi, Zuo Quan Xu
Date: 2022-12-29 00:00:00
Tasks: Math

This paper studies the monotone mean-variance (MMV) problem and the classical mean-variance (MV) problem with convex cone trading constraints in a market with random coefficients. We provide semiclosed optimal strategies and optimal values for both problems via certain backward stochastic differential equations (BSDEs). After noting the links between these BSDEs, we find that the two problems share the same optimal portfolio and optimal value. This generalizes the result of Shen and Zou $[$ SIAM J. Financial Math., 13 (2022), pp. SC99-SC112$]$ from deterministic coefficients to random ones.
Asymptotic behavior of mean fixation times in the Moran process with frequency-independent fitnesses
Authors: Rosângela A. Pires, Armando G. M. Neves
Date: 2022-12-30 00:00:00
Tasks: Math

We derive asymptotic formulae in the limit when population size N tends to infinity for mean fixation times (conditional and unconditional) in a population with two types of individuals, A and B, governed by the Moran process. We consider only the case in which the fitness of the two types do not depend on the population frequencies. Our results start with the important cases in which the initial condition is a single individual of any type, but we also consider the initial condition of a fraction x, 0<x<1, of A individuals, where x is kept fixed and the total population size tends to infinity. In the cases covered by Antal and Scheuring (Bull Math Biol 68(8):1923-1944, 2006), i.e. conditional fixation times for a single individual of any type, it will turn out that our formulae are much more accurate than the ones they found. As quoted, our results include other situations not treated by them.
COGEDAP: A COmprehensive GEnomic Data Analysis Platform
Authors: Bayram Cevdet Akdeniz, Oleksandr Frei, Espen Hagen, Tahir Tekin Filiz, Sandeep Karthikeyan, Joelle Pasman, Andreas Jangmo, Jacob Bergsted, John R. Shorter, Richard Zetterberg, Joeri Meijsen, Ida Elken Sonderby, Alfonso Buil, Martin Tesli, Yi Lu, Patrick Sullivan, Ole Andreassen, Eivind Hovig
Date: 2022-12-28 00:00:00

Non-sharable sensitive data collection and analysis in large-scale consortia for genomic research is complicated. Time consuming issues in installing software arise due to different operating systems, software dependencies and running the software. Therefore, easier, more standardized, automated protocols and platforms can be a solution to overcome these issues. We have developed one such solution for genomic data analysis using software container technologies. The platform, COGEDAP, consists of different software tools placed into Singularity containers with corresponding pipelines and instructions on how to perform genome-wide association studies (GWAS) and other genomic data analysis via corresponding tools. Using a provided helper script written in Python, users can obtain auto-generated scripts to conduct the desired analysis both on high-performance computing (HPC) systems and on personal computers. The analyses can be done by running these auto-generated scripts with the software containers. The helper script also performs minor re-formatting of the input/output data, so that the end user can work with a unified file format regardless of which genetic software is used for the analysis. COGEDAP is actively being used by users from different countries/projects to conduct their genomic data analyses. Thanks to this platform, users can easily run GWAS and other genomic analyses without spending much effort on software installation, data formats, and other technical requirements.
Integrated information theory (IIT) 4.0: Formulating the properties of phenomenal existence in physical terms
Authors: Larissa Albantakis, Leonardo Barbosa, Graham Findlay, Matteo Grasso, Andrew M Haun, William Marshall, William GP Mayner, Alireza Zaeemzadeh, Melanie Boly, Bjørn E Juel, Shuntaro Sasai, Keiko Fujii, Isaac David, Jeremiah Hendren, Jonathan P Lang, Giulio Tononi
Date: 2022-12-30 00:00:00

This paper presents Integrated Information Theory (IIT) 4.0. IIT aims to account for the properties of experience in physical (operational) terms. It identifies the essential properties of experience (axioms), infers the necessary and sufficient properties that its substrate must satisfy (postulates), and expresses them in mathematical terms. In principle, the postulates can be applied to any system of units in a state to determine whether it is conscious, to what degree, and in what way. IIT offers a parsimonious explanation of empirical evidence, makes testable predictions, and permits inferences and extrapolations. IIT 4.0 incorporates several developments of the past ten years, including a more accurate translation of axioms into postulates and mathematical expressions, the introduction of a unique measure of intrinsic information that is consistent with the postulates, and an explicit assessment of causal relations. By fully unfolding a system's irreducible cause-effect power, the distinctions and relations specified by a substrate can account for the quality of experience.
Seizures and epilepsy after intracerebral hemorrhage: an update
Authors: Laurent Derex, Sylvain Rheims, Laure Peter-Derex
Date: 2022-12-30 00:00:00
Tasks: Prognosis

Seizures are common after intracerebral hemorrhage, occurring in 6 to 15% of the patients, mostly in the first 72 hours. Their incidence reaches 30% when subclinical or non-convulsive seizures are diagnosed by continuous electroencephalogram. Several risk factors for seizures have been described including cortical location of intracerebral hemorrhage, presence of intraventricular hemorrhage, total hemorrhage volume, and history of alcohol abuse. Seizures after intracerebral hemorrhage may theoretically be harmful as they can lead to sudden blood pressure fluctuations, increase intracranial pressure and neuronal injury due to increased metabolic demand. Some recent studies suggest that acute symptomatic seizures (occurring within seven days of stroke) are associated with worse functional outcome and increased risk of death despite accounting for other known prognostic factors such as age and baseline hemorrhage volume. However, the impact of seizures on prognosis is still debated and it remains unclear if treating or preventing seizures might lead to improved clinical outcome. Thus, the currently available scientific evidence does not support the routine use of antiseizure medication as primary prevention among patients with intracerebral hemorrhage. Only prospective adequately powered randomized controlled trials will be able to answer whether seizure prophylaxis in the acute or longer term settings is beneficial or not in patients with intracerebral hemorrhage.