pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic LATEs with a Static Instrument
Authors: Bruno Ferman, Otávio Tecchio
Date: 2023-05-29 00:00:00

In many situations, researchers are interested in identifying dynamic effects of an irreversible treatment with a time-invariant binary instrumental variable (IV). For example, in evaluations of dynamic effects of training programs with a single lottery determining eligibility. A common approach in these situations is to report per-period IV estimates. Under a dynamic extension of standard IV assumptions, we show that such IV estimands identify a weighted sum of treatment effects for different latent groups and treatment exposures. However, there is possibility of negative weights. We discuss point and partial identification of dynamic treatment effects in this setting under different sets of assumptions.
Time-Varying Vector Error-Correction Models: Estimation and Inference
Authors: Jiti Gao, Bin Peng, Yayi Yan
Date: 2023-05-28 00:00:00
Tasks: Time Series

This paper considers a time-varying vector error-correction model that allows for different time series behaviours (e.g., unit-root and locally stationary processes) to interact with each other to co-exist. From practical perspectives, this framework can be used to estimate shifts in the predictability of non-stationary variables, test whether economic theories hold periodically, etc. We first develop a time-varying Granger Representation Theorem, which facilitates the establishment of asymptotic properties for the model, and then propose estimation and inferential methods and theory for both short-run and long-run coefficients. We also propose an information criterion to estimate the lag length, a singular-value ratio test to determine the cointegration rank, and a hypothesis test to examine the parameter stability. To validate the theoretical findings, we conduct extensive simulations. Finally, we demonstrate the empirical relevance by applying the framework to investigate the rational expectations hypothesis of the U.S. term structure.
Estimating overidentified linear models with heteroskedasticity and outliers
Authors: Lei Bill Wang
Date: 2023-05-28 00:00:00

A large degree of overidentification causes severe bias in TSLS. A conventional heuristic rule used to motivate new estimators in this context is approximate bias. This paper formalizes the definition of approximate bias and expands the applicability of approximate bias to various classes of estimators that bridge OLS, TSLS, and Jackknife IV estimators (JIVEs). By evaluating their approximate biases, I propose new approximately unbiased estimators, including UOJIVE1 and UOJIVE2. UOJIVE1 can be interpreted as a generalization of an existing estimator UIJIVE1. Both UOJIVEs are proven to be consistent and asymptotically normal under a fixed number of instruments and controls. The asymptotic proofs for UOJIVE1 in this paper require the absence of high leverage points, whereas proofs for UOJIVE2 do not. In addition, UOJIVE2 is consistent under many-instrument asymptotic. The simulation results align with the theorems in this paper: (i) Both UOJIVEs perform well under many instrument scenarios with or without heteroskedasticity, (ii) When a high leverage point coincides with a high variance of the error term, an outlier is generated and the performance of UOJIVE1 is much poorer than that of UOJIVE2.
Using Limited Trial Evidence to Credibly Choose Treatment Dosage when Efficacy and Adverse Effects Weakly Increase with Dose
Authors: Charles F. Manski
Date: 2023-05-26 00:00:00
Tasks: Decision Making

In medical treatment and elsewhere, it has become standard to base treatment intensity (dosage) on evidence in randomized trials. Yet it has been rare to study how outcomes vary with dosage. In trials to obtain drug approval, the norm has been to specify some dose of a new drug and compare it with an established therapy or placebo. Design-based trial analysis views each trial arm as qualitatively different, but it may be highly credible to assume that efficacy and adverse effects (AEs) weakly increase with dosage. Optimization of patient care requires joint attention to both, as well as to treatment cost. This paper develops methodology to credibly use limited trial evidence to choose dosage when efficacy and AEs weakly increase with dose. I suppose that dosage is an integer choice t in (0, 1, . . . , T), T being a specified maximum dose. I study dosage choice when trial evidence on outcomes is available for only K dose levels, where K < T + 1. Then the population distribution of dose response is partially rather than point identified. The identification region is a convex polygon determined by linear equalities and inequalities. I characterize clinical and public-health decision making using the minimax-regret criterion. A simple analytical solution exists when T = 2 and computation is tractable when T is larger.
Monotonicity Anomalies in Scottish Local Government Elections
Authors: David McCune, Adam Graham-Squire
Date: 2023-05-28 00:00:00
Tasks: Fairness

Single Transferable Vote (STV) is a voting method used to elect multiple candidates in ranked-choice elections. One weakness of STV is that it fails multiple fairness criteria related to monotonicity and no show paradoxes. We analyze 1,079 local government STV elections in Scotland to estimate the frequency of such monotonicity anomalies in real-world elections, and compare our results with prior empirical and theoretical research about the rates at which such anomalies occur. In 62 of the 1079 elections we found some kind of monotonicity anomaly. We generally find that the rates of anomalies are similar to prior empirical research and much lower than what most theoretical research has found. The STV anomalies we find are the first of their kind to be documented in real-world multiwinner elections.
Macroeconomic Effects of Inflation Targeting: A Survey of the Empirical Literature
Authors: Goran Petrevski
Date: 2023-05-27 00:00:00

This paper surveys the empirical literature of inflation targeting. The main findings from our review are the following: there is robust empirical evidence that larger and more developed countries are more likely to adopt the IT regime; the introduction of this regime is conditional on previous disinflation, greater exchange rate flexibility, central bank independence, and higher level of financial development; the empirical evidence has failed to provide convincing evidence that IT itself may serve as an effective tool for stabilizing inflation expectations and for reducing inflation persistence; the empirical research focused on advanced economies has failed to provide convincing evidence on the beneficial effects of IT on inflation performance, while there is some evidence that the gains from the IT regime may have been more prevalent in the emerging market economies; there is not convincing evidence that IT is associated with either higher output growth or lower output variability; the empirical research suggests that IT may have differential effects on exchange-rate volatility in advanced economies versus EMEs; although the empirical evidence on the impact of IT on fiscal policy is quite limited, it supports the idea that IT indeed improves fiscal discipline; the empirical support to the proposition that IT is associated with lower disinflation costs seems to be rather weak. Therefore, the accumulated empirical literature implies that IT does not produce superior macroeconomic benefits in comparison with the alternative monetary strategies or, at most, they are quite modest.
Duration Dependence and Heterogeneity: Learning from Early Notice of Layoff
Authors: Div Bhagia
Date: 2023-05-27 00:00:00

This paper presents a novel approach to distinguish the impact of duration-dependent forces and adverse selection on the exit rate from unemployment by leveraging variation in the length of layoff notices. I formulate a Mixed Hazard model in discrete time and specify the conditions under which variation in notice length enables the identification of structural duration dependence while allowing for arbitrary heterogeneity across workers. Utilizing data from the Displaced Worker Supplement (DWS), I employ the Generalized Method of Moments (GMM) to estimate the model. According to the estimates, the decline in the exit rate over the first 48 weeks of unemployment is largely due to the worsening composition of surviving jobseekers. Furthermore, I find that an individual's likelihood of exiting unemployment decreases initially, then increases until unemployment benefits run out, and remains steady thereafter. These findings are consistent with a standard search model where returns to search decline early in the spell.
Coarse Information Design
Authors: Qianjun Lyu, Wing Suen, Yimeng Zhang
Date: 2023-05-29 00:00:00

We study an information design problem with continuous state and discrete signal space. Under convex and S-shaped value functions, the optimal information structure is interval-partitional and exhibits a dual expectations property: each induced signal is the conditional mean (taken under the prior density) of each interval; and each interval cutoff is the barycenter (taken under the value function curvature) of the interval formed by neighboring signals. This property enables an examination into which part of the state space is more finely partitioned. The analysis can be extended to general value functions and adapted to study coarse mechanism design.
Firm-quasi-stability and re-equilibration in matching markets with contracts
Authors: Yi-You Yang
Date: 2023-05-29 00:00:00

We study firm-quasi-stability in the framework of many-to-many matching with contracts under substitutable preferences. We establish various links between firm-quasi-stability and stability, and give new insights into the existence and lattice property of stable allocations. In addition, we show that firm-quasi-stable allocations appear naturally when the stability of the market is disrupted by the entry of new firms or the retirement of some workers, and introduce a generalized deferred acceptance algorithm to show that the market can regain stability from firm-quasi-stable allocations by a decentralized process of offers and acceptances. Moreover, it is shown that the entry of new firms or the retirement of workers cannot be bad for any of the incumbent workers and cannot be good for any of the original firms, while each new firm gets its optimal outcome under stable allocations whenever the law of aggregate demand holds.
Creating Personalized Synthetic Voices from Post-Glossectomy Speech with Guided Diffusion Models
Authors: Yusheng Tian, Guangyan Zhang, Tan Lee
Date: 2023-05-27 00:00:00
Tasks: Speech Synthesis, Voice Conversion

This paper is about developing personalized speech synthesis systems with recordings of mildly impaired speech. In particular, we consider consonant and vowel alterations resulted from partial glossectomy, the surgical removal of part of the tongue. The aim is to restore articulation in the synthesized speech and maximally preserve the target speaker's individuality. We propose to tackle the problem with guided diffusion models. Specifically, a diffusion-based speech synthesis model is trained on original recordings, to capture and preserve the target speaker's original articulation style. When using the model for inference, a separately trained phone classifier will guide the synthesis process towards proper articulation. Objective and subjective evaluation results show that the proposed method substantially improves articulation in the synthesized speech over original recordings, and preserves more of the target speaker's individuality than a voice conversion baseline.
LLC type galvanic isolated resonant converter
Authors: Antonio Brajdić
Date: 2023-05-29 00:00:00

Resonant converters are often being used for high power and high voltage applications to achieve high efficiency, high power density and low EMI. In this paper, we will use a resonant converter for a completely different application. We will take advantage of the Zero Voltage Switching (ZVS) and operating in the vicinity of the series resonance to design a low power and ultra-low noise galvanic isolated half bridge resonant DC-DC converter for powering a medical amplifier. Under lower power we consider up to 6W (500mA @ 12V). The DC-DC converter must meet the safety requirements of the standard IEC 60601.
Towards Robust Data-Driven Underwater Acoustic Localization: A Deep CNN Solution with Performance Guarantees for Model Mismatch
Authors: Amir Weiss, Andrew C. Singer, Gregory W. Wornell
Date: 2023-05-29 00:00:00

Key challenges in developing underwater acoustic localization methods are related to the combined effects of high reverberation in intricate environments. To address such challenges, recent studies have shown that with a properly designed architecture, neural networks can lead to unprecedented localization capabilities and enhanced accuracy. However, the robustness of such methods to environmental mismatch is typically hard to characterize, and is usually assessed only empirically. In this work, we consider the recently proposed data-driven method [19] based on a deep convolutional neural network, and demonstrate that it can learn to localize in complex and mismatched environments. To explain this robustness, we provide an upper bound on the localization mean squared error (MSE) in the ``true" environment, in terms of the MSE in a ``presumed" environment and an additional penalty term related to the environmental discrepancy. Our theoretical results are corroborated via simulation results in a rich, highly reverberant, and mismatch channel.
Secrecy Capacity Analysis of 4-WFRFT Based Physical Layer Security in MIMO System
Authors: T. Xu, X. Wu, J. Zhang
Date: 2023-05-29 00:00:00

The traditional information security based on cryptosystem is seriously threatened due to the exponential growth of computing capacity. In order to improve for the upper cryptosystem security, the secure transmission at the physical layer is introduced into the wireless communication system. However, considering the openness of wireless channel, the performance analysis of security wireless communication systems has become a hot issue in recent years. Due to the existence of the upper layer cryptosystem being cracked and the openness of wireless channel security problems, the Multiple-Input Multiple-Output (MIMO) technology increases the difference between channels, the development of its rich spatial degrees of freedom can greatly improve the channel capacity and achieve the purpose of enhancing the Physical layer security (PLS); On this basis, Weighted Type Fractional Fourier Transform (WFrFT) technology rotates and splits the constellation points of signals, it is equivalent to increasing the artificial noise to enhance the PLS. The Average security capacity is an important index to measure the PLS, Therefore, this paper deduces the Average security capacity in MIMO scenario and gives a specific closed expression when considering the channel correlation. By analyzing the expression, the increase of the number of MIMO antennas will improve the Average security capacity. When the estimated bias is 1 in 4-WFRFT, the Average security capacity will be significantly improved; the Average security capacity will reach the maximum value, thus enhancing the PLS.
Continuous and Noninvasive Measurement of Arterial Pulse Pressure and Pressure Waveform using an Image-free Ultrasound System
Authors: Lirui Xu, Pang Wu, Pan Xia, Fanglin Geng, Peng Wang, Xianxiang Chen, Zhenfeng Li, Lidong Du, Shuping Liu, Li Li, Hongbo Chang, Zhen Fang
Date: 2023-05-29 00:00:00

The local beat-to-beat local pulse pressure (PP) and blood pressure waveform of arteries, especially central arteries, are important indicators of the course of cardiovascular diseases (CVDs). Nevertheless, noninvasive measurement of them remains a challenge in the clinic. This work presents a three-element image-free ultrasound system with a low-computational method for real-time measurement of local pulse wave velocity (PWV) and diameter waveforms, enabling real-time and noninvasive continuous PP and blood pressure waveforms measurement without calibration. The developed system has been well-validated in vitro and in vivo. In in vitro cardiovascular phantom experiments, the results demonstrated high accuracy in the measurement of PP (error < 3 mmHg) and blood pressure waveform (root-mean-square-errors (RMSE) < 2 mmHg, correlation coefficient (r) > textgreater 0.99). In subsequent human carotid experiments, the system was compared with an arterial tonometer, which showed excellent PP accuracy (mean absolute error (MAE) = 3.7 +- 3.4 mmHg) and pressure waveform similarity (RMSE = 3.7 +- 1.6 mmHg, r = 0.98 +- 0.01). Furthermore, comparative experiments with the volume clamp device demonstrated the system's ability to accurately trace blood pressure changes (induced by deep breathing) over a period of one minute, with the MAE of DBP, MAP, and SBP within 5 +- 8 mmHg. The present results demonstrate the accuracy and reliability of the developed system for continuous and noninvasive measurement of arterial PP and blood pressure waveform measurements, with potential applications in the diagnosis and prevention of CVDs.
Wavelet Packet Division Multiplexing (WPDM)-Aided Industrial WSNs
Authors: Indrakshi Dey, Nicola Marchetti
Date: 2023-05-28 00:00:00

Industrial Internet-of-Things (IIoT) involve multiple groups of sensors, each group sending its observations on a particular phenomenon to a central computing platform over a multiple access channel (MAC). The central platform incorporates a decision fusion center (DFC) that arrives at global decisions regarding each set of phenomena by combining the received local sensor decisions. Owing to the diverse nature of the sensors and heterogeneous nature of the information they report, it becomes extremely challenging for the DFC to denoise the signals and arrive at multiple reliable global decisions regarding multiple phenomena. The industrial environment represents a specific indoor scenario devoid of windows and filled with different noisy electrical and measuring units. In that case, the MAC is modelled as a large-scale shadowed and slowly-faded channel corrupted with a combination of Gaussian and impulsive noise. The primary contribution of this paper is to propose a flexible, robust and highly noise-resilient multi-signal transmission framework based on Wavelet packet division multiplexing (WPDM). The local sensor observations from each group of sensors are waveform coded onto wavelet packet basis functions before reporting them over the MAC. We assume a multi-antenna DFC where the waveform-coded sensor observations can be separated by a bank of linear filters or a correlator receiver, owing to the orthogonality of the received waveforms. At the DFC we formulate and compare fusion rules for fusing received multiple sensor decisions, to arrive at reliable conclusions regarding multiple phenomena. Simulation results show that WPDM-aided wireless sensor network (WSN) for IIoT environments offer higher immunity to noise by more than 10 times over performance without WPDM in terms of probability of false detection.
RF SSSL by an Autonomous UAV with Two-Ray Channel Model and Dipole Antenna Patterns
Authors: Hyeokjun Kwon, Sung Joon Maeng, Ismail Guvenc
Date: 2023-05-28 00:00:00

Advancements in unmanned aerial vehicle (UAV) technology have led to their increased utilization in various commercial and military applications. One such application is signal source search and localization (SSSL) using UAVs, which offers significant benefits over traditional ground-based methods due to improved RF signal reception at higher altitudes and inherent autonomous 3D navigation capabilities. Nevertheless, practical considerations such as propagation models and antenna patterns are frequently neglected in simulation-based studies in the literature. In this work, we address these limitations by using a two-ray channel model and a dipole antenna pattern to develop a simulator that more closely represents real-world radio signal strength (RSS) observations at a UAV. We then examine and compare the performance of previously proposed linear least square (LLS) based localization techniques using UAVs for SSSL. Localization of radio frequency (RF) signal sources is assessed based on two main criteria: 1) achieving the highest possible accuracy and 2) localizing the target as quickly as possible with reasonable accuracy. Various mission types, such as those requiring precise localization like identifying hostile troops, and those demanding rapid localization like search and rescue operations during disasters, have been previously investigated. In this paper, the efficacy of the proposed localization approaches is examined based on these two main localization requirements through computer simulations.
IoT Localization and Optimized Topology Extraction Using Eigenvector Synchronization
Authors: Indrakshi Dey, Nicola Marchetti
Date: 2023-05-28 00:00:00

Internet-of-Things (IoT) devices are low size, weight and power (SWaP), low complexity and include sensors, meters, wearables and trackers. Transmitting information with high signal power is exacting on device battery life, therefore an efficient link and network configuration is absolutely crucial to avoid signal power enhancement in interference-rich environment and resorting to battery-life extending strategies. Efficient network configuration can also ensure fulfilment of network performance metrics like throughput, coding rate and spectral efficiency. We formulate a novel approach of first localizing the IoT nodes and then extracting the network topology for information exchange between the nodes (devices, gateway and sinks), such that overall network throughput is maximized. The nodes are localized using noisy measurements of a subset of Euclidean distances between two nodes. Realizable subsets of neighboring devices agree with their own position within the entire network graph through eigenvector synchronization. Using communication global graph-model-based technique, network topology is constructed in terms of transmit power allocation with the aim of maximizing spatial usage and overall network throughput. This topology extraction problem is solved using the concept of linear programming.