pwc-archive/papers-with-abstracts huggingface dataset:
Macroeconomic Forecasting using Dynamic Factor Models: The Case of Morocco
Authors: Daoui Marouane
Date: 2023-02-27 00:00:00

This article discusses the use of dynamic factor models in macroeconomic forecasting, with a focus on the Factor-Augmented Error Correction Model (FECM). The FECM combines the advantages of cointegration and dynamic factor models, providing a flexible and reliable approach to macroeconomic forecasting, especially for non-stationary variables. We evaluate the forecasting performance of the FECM model on a large dataset of 117 Moroccan economic series with quarterly frequency. Our study shows that FECM outperforms traditional econometric models in terms of forecasting accuracy and robustness. The inclusion of long-term information and common factors in FECM enhances its ability to capture economic dynamics and leads to better forecasting performance than other competing models. Our results suggest that FECM can be a valuable tool for macroeconomic forecasting in Morocco and other similar economies.
Evaluación del efecto del PAMI en la cobertura en salud de los adultos mayores en Argentina
Authors: Juan Marcelo Virdis, Fernando Delbianco, María Eugenia Elorza
Date: 2023-02-28 00:00:00
Tasks: regression

We conducted regression discontinuity design models in order to evaluate changes in access to healthcare services and financial protection, using as a natural experiment the age required to retire in Argentina, the moment in which people are able to enroll in the free social health insurance called PAMI. The dependent variables were indicators of the population with health insurance, out-of-pocket health expenditure, and use of health services. The results show that PAMI causes a high increase in the population with health insurance and marginal reductions in health expenditure. No effects on healthcare use were found.
Intergenerational Mobility Trends and the Changing Role of Female Labor
Authors: Ulrika Ahrsjö, René Karadakic, Joachim Kahr Rasmussen
Date: 2023-02-28 00:00:00

Using harmonized administrative data from Scandinavia, we find that intergenerational rank associations in income have increased uniformly across Sweden, Denmark, and Norway for cohorts born between 1951 and 1979. By gender, father-son mobility remains stable, while correlations for mothers and daughters rise. Similar patterns appear in US survey data, albeit with different timing. We show that the decline in income mobility reflects female income more accurately capturing underlying productivity, rather than stronger intergenerational transmission of human capital or changes in assortative mating. Finally, we show parent-child correlations increase mainly when women gain access to jobs that match their productivity.
The association between Marital Locus of Control and break-up intentions
Authors: David Boto-García, Federico Perali
Date: 2023-02-27 00:00:00

Understanding couple instability is a topic of social and economic relevance. This paper investigates how the risk of dissolution relates to efforts to solve disagreements. We study whether the prevalence of relationship instability in the past among couples is associated with marital locus of control. This is a noncognitive trait that captures individuals perception of control over problems within the couple. We implement a list experiment using the count-item technique to a sample of current real-life couples to elicit truthful answers about couple break-up intentions in the past at the individual level. We find that around 44 per cent of our sample has considered to end their relationship with their partner in the past. The intention to break-up is more prevalent among those who score low in marital locus of control, males, low-income earners, individuals with university studies and couples without children.
Econometric assessment of the monetary policy shocks in Morocco: Evidence from a Bayesian Factor-Augmented VAR
Authors: Marouane Daoui
Date: 2023-02-27 00:00:00

The analysis of the effects of monetary policy shocks using the common econometric models (such as VAR or SVAR) poses several empirical anomalies. However, it is known that in these econometric models the use of a large amount of information is accompanied by dimensionality problems. In this context, the approach in terms of FAVAR (Factor Augmented VAR) models tries to solve this problem. Moreover, the information contained in the factors is important for the correct identification of monetary policy shocks and it helps to correct the empirical anomalies usually encountered in empirical work. Following Bernanke, Boivin and Eliasz (2005) procedure, we will use the FAVAR model to analyze the impact of monetary policy shocks on the Moroccan economy. The model used allows us to obtain impulse response functions for all indicators in the macroeconomic dataset used (117 quarterly frequency series from 1985: Q1 to 2018: Q4) to have a more realistic and complete representation of the impact of monetary policy shocks in Morocco.
Off-Balance Sheet Activities and Scope Economies in U.S. Banking
Authors: Jingfang Zhang, Emir Malikov
Date: 2023-02-26 00:00:00
Tasks: quantile regression

Propelled by the recent financial product innovations involving derivatives, securitization and mortgages, commercial banks are becoming more complex, branching out into many "nontraditional" banking operations beyond issuance of loans. This broadening of operational scope in a pursuit of revenue diversification may be beneficial if banks exhibit scope economies. The existing (two-decade-old) empirical evidence lends no support for such product-scope-driven cost economies in banking, but it is greatly outdated and, surprisingly, there has been little (if any) research on this subject despite the drastic transformations that the U.S. banking industry has undergone over the past two decades in the wake of technological advancements and regulatory changes. Commercial banks have significantly shifted towards nontraditional operations, making the portfolio of products offered by present-day banks very different from that two decades ago. In this paper, we provide new and more robust evidence about scope economies in U.S. commercial banking. We improve upon the prior literature not only by analyzing the most recent data and accounting for bank's nontraditional off-balance sheet operations, but also in multiple methodological ways. To test for scope economies, we estimate a flexible time-varying-coefficient panel-data quantile regression model which accommodates three-way heterogeneity across banks. Our results provide strong evidence in support of significantly positive scope economies across banks of virtually all sizes. Contrary to earlier studies, we find no empirical corroboration for scope diseconomies.
On the Estimation of Cross-Firm Productivity Spillovers with an Application to FDI
Authors: Emir Malikov, Shunan Zhao
Date: 2023-02-26 00:00:00

We develop a novel methodology for the proxy variable identification of firm productivity in the presence of productivity-modifying learning and spillovers which facilitates a unified "internally consistent" analysis of the spillover effects between firms. Contrary to the popular two-step empirical approach, ours does not postulate contradictory assumptions about firm productivity across the estimation steps. Instead, we explicitly accommodate cross-sectional dependence in productivity induced by spillovers which facilitates identification of both the productivity and spillover effects therein simultaneously. We apply our model to study cross-firm spillovers in China's electric machinery manufacturing, with a particular focus on productivity effects of inbound FDI.
SSL-QALAS: Self-Supervised Learning for Rapid Multiparameter Estimation in Quantitative MRI Using 3D-QALAS
Authors: Yohan Jun, Jaejin Cho, Xiaoqing Wang, Michael Gee, P. Ellen Grant, Berkin Bilgic, Borjan Gagoski
Date: 2023-02-28 00:00:00
Tasks: Quantitative MRI, Self-Supervised Learning, Transfer Learning

Purpose: To develop and evaluate a method for rapid estimation of multiparametric T1, T2, proton density (PD), and inversion efficiency (IE) maps from 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) measurements using self-supervised learning (SSL) without the need for an external dictionary. Methods: A SSL-based QALAS mapping method (SSL-QALAS) was developed for rapid and dictionary-free estimation of multiparametric maps from 3D-QALAS measurements. The accuracy of the reconstructed quantitative maps using dictionary matching and SSL-QALAS was evaluated by comparing the estimated T1 and T2 values with those obtained from the reference methods on an ISMRM/NIST phantom. The SSL-QALAS and the dictionary matching methods were also compared in vivo, and generalizability was evaluated by comparing the scan-specific, pre-trained, and transfer learning models. Results: Phantom experiments showed that both the dictionary matching and SSL-QALAS methods produced T1 and T2 estimates that had a strong linear agreement with the reference values in the ISMRM/NIST phantom. Further, SSL-QALAS showed similar performance with dictionary matching in reconstructing the T1, T2, PD, and IE maps on in vivo data. Rapid reconstruction of multiparametric maps was enabled by inferring the data using a pre-trained SSL-QALAS model within 10 s. Fast scan-specific tuning was also demonstrated by fine-tuning the pre-trained model with the target subject's data within 15 min. Conclusion: The proposed SSL-QALAS method enabled rapid reconstruction of multiparametric maps from 3D-QALAS measurements without an external dictionary or labeled ground-truth training data.
Spiking Neural Network Nonlinear Demapping on Neuromorphic Hardware for IM/DD Optical Communication
Authors: Elias Arnold, Georg Böcherer, Florian Strasser, Eric Müller, Philipp Spilger, Sebastian Billaudelle, Johannes Weis, Johannes Schemmel, Stefano Calabrò, Maxim Kuschnerov
Date: 2023-02-28 00:00:00

Neuromorphic computing implementing spiking neural networks (SNN) is a promising technology for reducing the footprint of optical transceivers, as required by the fast-paced growth of data center traffic. In this work, an SNN nonlinear demapper is designed and evaluated on a simulated intensity-modulation direct-detection link with chromatic dispersion. The SNN demapper is implemented in software and on the analog neuromorphic hardware system BrainScaleS-2 (BSS-2). For comparison, linear equalization (LE), Volterra nonlinear equalization (VNLE), and nonlinear demapping by an artificial neural network (ANN) implemented in software are considered. At a pre-forward error correction bit error rate of 2e-3, the software SNN outperforms LE by 1.5 dB, VNLE by 0.3 dB and the ANN by 0.5 dB. The hardware penalty of the SNN on BSS-2 is only 0.2 dB, i.e., also on hardware, the SNN performs better than all software implementations of the reference approaches. Hence, this work demonstrates that SNN demappers implemented on electrical analog hardware can realize powerful and accurate signal processing fulfilling the strict requirements of optical communications.
On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds
Authors: Cheng-Xiang Wang, Xiaohu You, Xiqi Gao, Xiuming Zhu, Zixin Li, Chuan Zhang, Haiming Wang, Yongming Huang, Yunfei Chen, Harald Haas, John S. Thompson, Erik G. Larsson, Marco Di Renzo, Wen Tong, Peiying Zhu, Xuemin, Shen, H. Vincent Poor, Lajos Hanzo
Date: 2023-02-28 00:00:00

Fifth generation (5G) mobile communication systems have entered the stage of commercial development, providing users with new services and improved user experiences as well as offering a host of novel opportunities to various industries. However, 5G still faces many challenges. To address these challenges, international industrial, academic, and standards organizations have commenced research on sixth generation (6G) wireless communication systems. A series of white papers and survey papers have been published, which aim to define 6G in terms of requirements, application scenarios, key technologies, etc. Although ITU-R has been working on the 6G vision and it is expected to reach a consensus on what 6G will be by mid-2023, the related global discussions are still wide open and the existing literature has identified numerous open issues. This paper first provides a comprehensive portrayal of the 6G vision, technical requirements, and application scenarios, covering the current common understanding of 6G. Then, a critical appraisal of the 6G network architecture and key technologies is presented. Furthermore, existing testbeds and advanced 6G verification platforms are detailed for the first time. In addition, future research directions and open challenges are identified for stimulating the on-going global debate. Finally, lessons learned to date concerning 6G networks are discussed.
Multi-target Range and Angle detection for MIMO-FMCW radar with limited antennas
Authors: Himali Singh, Arpan Chattopadhyay
Date: 2023-02-28 00:00:00
Tasks: Compressive Sensing

Multiple-input multiple-output (MIMO) radar has several advantages with respect to the traditional radar array systems in terms of performance and flexibility. However, in order to achieve high angular resolution, a MIMO radar requires a large number of transmit and receive antennas, which increases hardware design and computational complexities. Although spatial compressive sensing (CS) has been recently considered for a pulsed-waveform MIMO radar with sparse random arrays, such methods for the frequency-modulated continuous wave (FMCW) radar remain largely unexplored. In this context, we propose a novel multi-target localization algorithm in the range-angle domain for a MIMO FMCW radar with a sparse array of randomly placed transmit and receive elements. In particular, we first obtain the targets' range-delays using a discrete Fourier transform (DFT)-based focusing operation. The target angles are then recovered at each detected range using CS-based techniques exploiting the sparsity of the target scene. Our simulation results demonstrate the effectiveness of the proposed algorithm over the classical methods in detecting multiple targets with a sparse array.
Constant-Modulus Waveform Design for Dual-Function Radar-Communication Systems in the Presence of Clutter
Authors: Wenjun Wu, Bo Tang, Xuyang Wang
Date: 2023-02-28 00:00:00

We investigate the constant-modulus (CM) waveform design for dual-function radar communication systems in the presence of clutter.To minimize the interference power and enhance the target acquisition performance, we use the signal-to-interference-plus-noise-ratio as the design metric.In addition, to ensure the quality of the service for each communication user, we enforce a constraint on the synthesis error of every communication signals.An iterative algorithm, which is based on cyclic optimization, Dinkinbach's transform, and alternating direction of method of multipliers, is proposed to tackle the encountered non-convex optimization problem.Simulations illustrate that the CM waveforms synthesized by the proposed algorithm allow to suppress the clutter efficiently and control the synthesis error of communication signals to a low level.
Low-Complexity Blind Parameter Estimation in Wireless Systems with Noisy Sparse Signals
Authors: Alexandra Gallyas-Sanhueza, Christoph Studer
Date: 2023-02-27 00:00:00
Tasks: Denoising, parameter estimation

Baseband processing algorithms often require knowledge of the noise power, signal power, or signal-to-noise ratio (SNR). In practice, these parameters are typically unknown and must be estimated. Furthermore, the mean-square error (MSE) is a desirable metric to be minimized in a variety of estimation and signal recovery algorithms. However, the MSE cannot directly be used as it depends on the true signal that is generally unknown to the estimator. In this paper, we propose novel blind estimators for the average noise power, average receive signal power, SNR, and MSE. The proposed estimators can be computed at low complexity and solely rely on the large-dimensional and sparse nature of the processed data. Our estimators can be used (i) to quickly track some of the key system parameters while avoiding additional pilot overhead, (ii) to design low-complexity nonparametric algorithms that require such quantities, and (iii) to accelerate more sophisticated estimation or recovery algorithms. We conduct a theoretical analysis of the proposed estimators for a Bernoulli complex Gaussian (BCG) prior, and we demonstrate their efficacy via synthetic experiments. We also provide three application examples that deviate from the BCG prior in millimeter-wave multi-antenna and cell-free wireless systems for which we develop nonparametric denoising algorithms that improve channel-estimation accuracy with a performance comparable to denoisers that assume perfect knowledge of the system parameters.
Test of 5G System in the Reverberation Chamber at mm-wave
Authors: Davide Micheli, Riccardo Diamanti, Luca Bastianelli, Emanuel Colella, Valter Mariani Primiani, Franco Moglie, Andrea Allasia, Maurizio Crozzoli, Michele Colombo
Date: 2023-02-26 00:00:00

The performance of a real fifth generation base station was studied by using a reverberation chamber as a real life propagating environment. Preliminary tests were conducted in order to define 5G base station operation conditions at mm-wave and emulated scenarios where reconfigurable intelligent surface(s) (RISs) will successively be tested. Measurements campaign was carried out under the H2020 European project RISE-6G and a collaboration program between TIM S.p.A., Nokia and Universita` Politecnica delle Marche.
Exploring locust hopper bands emergent patterns using parallel computing
Authors: Adrian Bach
Date: 2023-02-28 00:00:00

To date, the mechanisms underlying the diversity of the emergent patterns of collective motion in locust hopper bands remain to be unveiled. This study investigates the role of speed heterogeneity in the emergence of the most common patterns (frontal and columnar), following the Self-Organization framework. To address whether marching activity intermittency and density-dependant hopping individual behaviours could underlie the formation of such patterns, a three-zone Self-Propelled Particles model variant was formulated. In this model, individuals alternated between marching and resting periods, and were more likely to hop when crowded. The model successfully predicted the emergence of both patterns of interest, with the presence of a density-dependent hopping probability being a necessary condition. Short to absent pause periods mostly resulted in columnar shapes, similar to the ones observed in the brown locust (Locustana pardalina) and long pause periods rather resulted in frontal shapes, such as exhibited by the Australian plague locust (Chortoicetes terminifera). Furthermore, the density profiles of simulated frontal formations displayed the same shape as empirical profiles of Australian plague locust hopper bands. Both simulated and experimental paint marking experiments showed that locusts initially located at different positions in the band were find together at its front after a few hours marching; an expected global behavior in hopper bands undergoing activity intermittency. These results represent an important first step towards a cross-species comparison of locust mass migration patterns.
Structural constraints on the emergence of oscillations in multi-population neural networks
Authors: Jie Zang, Shenquan Liu, Pascal Helson, Arvind Kumar
Date: 2023-02-27 00:00:00

Oscillations arise in many real-world systems and are associated with both functional and dysfunctional states. Whether a network can oscillate can be estimated if we know the strength of interaction between nodes. But in real-world networks (in particular in biological networks) it is usually not possible to know the exact connection weights. Therefore, it is important to determine the structural properties of a network necessary to generate oscillations. Here, we provide a proof that uses dynamical system theory to prove that an odd number of inhibitory nodes and strong enough connections are necessary to generate oscillations in a single cycle threshold-linear network. We illustrate these analytical results in a biologically plausible network with either firing-rate based or spiking neurons. Our work provides structural properties necessary to generate oscillations in a network. We use this knowledge to reconcile recent experimental findings about oscillations in basal ganglia with classical findings.
Formalising and Learning a Quantum Model of Concepts
Authors: Sean Tull, Razin A. Shaikh, Sara Sabrina Zemljic, Stephen Clark
Date: 2023-02-07 00:00:00
Tasks: model

In this report we present a new modelling framework for concepts based on quantum theory, and demonstrate how the conceptual representations can be learned automatically from data. A contribution of the work is a thorough category-theoretic formalisation of our framework. We claim that the use of category theory, and in particular the use of string diagrams to describe quantum processes, helps elucidate some of the most important features of our quantum approach to concept modelling. Our approach builds upon Gardenfors' classical framework of conceptual spaces, in which cognition is modelled geometrically through the use of convex spaces, which in turn factorise in terms of simpler spaces called domains. We show how concepts from the domains of shape, colour, size and position can be learned from images of simple shapes, where individual images are represented as quantum states and concepts as quantum effects. Concepts are learned by a hybrid classical-quantum network trained to perform concept classification, where the classical image processing is carried out by a convolutional neural network and the quantum representations are produced by a parameterised quantum circuit. We also use discarding to produce mixed effects, which can then be used to learn concepts which only apply to a subset of the domains, and show how entanglement (together with discarding) can be used to capture interesting correlations across domains. Finally, we consider the question of whether our quantum models of concepts can be considered conceptual spaces in the Gardenfors sense.
The self-organization of selfishness: Reinforcement Learning shows how selfish behavior can emerge from agent-environment interaction dynamics
Authors: Aamir Sahil Chandroth, Nithya Ramakrishnan, Sanjay Chandrasekharan
Date: 2023-02-28 00:00:00

When biological communities use signaling structures for complex coordination, 'free-riders' emerge. The free-riding agents do not contribute to the community resources (signals), but exploit them. Most models of such 'selfish' behavior consider free-riding as evolving through mutation and selection. Over generations, the mutation -- which is considered to create a stable trait -- spreads through the population. This can lead to a version of the 'Tragedy of the Commons', where the community's coordination resource gets fully depleted or deteriorated. In contrast to this evolutionary view, we present a reinforcement learning model, which shows that both signaling-based coordination and free-riding behavior can emerge within a generation, through learning based on energy minimisation. Further, we show that there can be two types of free-riding, and both of these are not stable traits, but dynamic 'coagulations' of agent-environment interactions. Our model thus shows how different kinds of selfish behavior can emerge through self-organization, and suggests that the idea of selfishness as a stable trait presumes a model based on mutations. We conclude with a discussion of some social and policy implications of our model.