pwc-archive/papers-with-abstracts huggingface dataset:
Differentially Private Distributed Stochastic Optimization with Time-Varying Sample Sizes
Authors: Jimin Wang, Ji-Feng Zhang
Date: 2023-10-18 00:00:00
Tasks: Stochastic Optimization

Differentially private distributed stochastic optimization has become a hot topic due to the urgent need of privacy protection in distributed stochastic optimization. In this paper, two-time scale stochastic approximation-type algorithms for differentially private distributed stochastic optimization with time-varying sample sizes are proposed using gradient- and output-perturbation methods. For both gradient- and output-perturbation cases, the convergence of the algorithm and differential privacy with a finite cumulative privacy budget $\varepsilon$ for an infinite number of iterations are simultaneously established, which is substantially different from the existing works. By a time-varying sample sizes method, the privacy level is enhanced, and differential privacy with a finite cumulative privacy budget $\varepsilon$ for an infinite number of iterations is established. By properly choosing a Lyapunov function, the algorithm achieves almost-sure and mean-square convergence even when the added privacy noises have an increasing variance. Furthermore, we rigorously provide the mean-square convergence rates of the algorithm and show how the added privacy noise affects the convergence rate of the algorithm. Finally, numerical examples including distributed training on a benchmark machine learning dataset are presented to demonstrate the efficiency and advantages of the algorithms.
Representation of Distribution Grid Expansion Costs in Power System Planning
Authors: Luis Böttcher, Christian Fröhlich, Steffen Kortmann, Simon Braun, Julian Saat, Andreas Ulbig
Date: 2023-10-18 00:00:00

The shift towards clean energy brings about notable transformations to the energy system. In order to optimally plan a future energy system, it is necessary to consider the influence of several sectors as well as the interaction of the transmission grid and distribution grid. The concept of Feasible Operation Region (FOR) is a detailed approach to representing the operational dependencies between the transmission and distribution grid. However, in previous planning procedures, only a simplified expansion of the distribution grids can be taken into account. With the method presented in this paper, a Feasible Planning Region (FPR) is developed, which represents the operational boundaries of the distribution grids for several expansion stages and thus represents an admissible solution space for the planning of distribution grids in systemic planning approaches. It hence enables a more detailed representation of the necessary distribution grid expansion for the integration of distributed technologies in an optimized energy system of the future. In this paper, we present the method by which the FPR is formed and its integration into an energy system planning formulation. In the results, the FPR is presented for different voltage levels, and its use in power system planning is demonstrated.
Finite Sample Performance Analysis of MIMO Systems Identification
Authors: Shuai Sun, Jiayun Li, Yilin Mo
Date: 2023-10-18 00:00:00

This paper is concerned with the finite sample identification performance of an n dimensional discrete-time Multiple-Input Multiple-Output (MIMO) Linear Time-Invariant system, with p inputs and m outputs. We prove that the widely-used Ho-Kalman algorithm and Multivariable Output Error State Space (MOESP) algorithm are ill-conditioned for MIMO systems when n/m or n/p is large. Moreover, by analyzing the Cra\'mer-Rao bound, we derive a fundamental limit for identifying the real and stable (or marginally stable) poles of MIMO system and prove that the sample complexity for any unbiased pole estimation algorithm to reach a certain level of accuracy explodes superpolynomially with respect to n/(pm). Numerical results are provided to illustrate the ill-conditionedness of Ho-Kalman algorithm and MOESP algorithm as well as the fundamental limit on identification.
High Efficiency Polymer based Direct Multi-jet Impingement Cooling Solution for High Power Devices
Authors: Tiwei Wei
Date: 2023-10-18 00:00:00

Liquid jet impingement cooling is an efficient cooling technique where the liquid coolant is directly ejected from nozzles on the chip backside resulting in a high cooling efficiency due to the absence of the TIM and the lateral temperature gradient. In literature, several Si-fabrication based impingement coolers with nozzle diameters of a few distributed returns or combination of micro-channels and impingement nozzles. The drawback of this Si processing of the cooler is the high fabrication cost. Other fabrication methods for nozzle diameters for ceramic and metal. Low cost fabrication methods, including injection molding and 3D printing have been introduced for much larger nozzle diameters (mm range) with larger cooler dimensions. These dimensions and processes are however not compatible with the chip packaging process flow. This PhD focuses on the modeling, design, fabrication and characterization of a micro-scale liquid impingement cooler using advanced, yet cost efficient, fabrication techniques. The main objectives are: (a) development of a modeling methodology to optimize the cooler geometry; (b) exploring low cost fabrication methods for the package level impingement jet cooler; (c) experimental thermal and hydraulic characterization and analysis of the fabricated coolers; (d) applying the direct impingement jet cooling solutions to different applications.
A Set-Based Approach for Robust Control Co-Design
Authors: Trevor J. Bird, Jacob A. Siefert, Herschel C. Pangborn, Neera Jain
Date: 2023-10-18 00:00:00

Control Co-Design (CCD) considers the coupled effects of both the plant and control parameters to optimize a system's closed-loop transient performance during the design stage. This paper presents a new method for CCD with guarantees on robustness to nondeterministic disturbances for all initial conditions within a specified region of operation. This is accomplished by calculating the reachable sets of a candidate closed-loop system directly within the optimization problem. Using this approach, the plant and control parameters are simultaneously chosen to shape these reachable sets to be robustly positive invariant and thus safe for all time. Compared to conventional approaches that perform the optimization for a single initial condition and an a priori chosen sequence of disturbances, the proposed set-based method avoids sensitivity to variations in the assumed design scenario. As a representative example, the proposed method is applied to an active suspension system.
Connecting the Dots: Range Expansions across Landscapes with Quenched Noise
Authors: Jimmy Gonzalez Nuñez, Jayson Paulose, Wolfram Möbius, Daniel A. Beller
Date: 2023-10-17 00:00:00

When biological populations expand into new territory, the evolutionary outcomes can be strongly influenced by genetic drift, the random fluctuations in allele frequencies. Meanwhile, spatial variability in the environment can also significantly influence the competition between sub-populations vying for space. Little is known about the interplay of these intrinsic and extrinsic sources of noise in population dynamics: When does environmental heterogeneity dominate over genetic drift or vice versa, and what distinguishes their population genetics signatures? Here, in the context of neutral evolution, we examine the interplay between a population's intrinsic, demographic noise and an extrinsic, quenched random noise provided by a heterogeneous environment. Using a multi-species Eden model, we simulate a population expanding over a landscape with random variations in local growth rates and measure how this variability affects genealogical tree structure, and thus genetic diversity. We find that, for strong heterogeneity, the genetic makeup of the expansion front is to a great extent predetermined by the set of fastest paths through the environment. The landscape-dependent statistics of these optimal paths then supersede those of the population's intrinsic noise as the main determinant of evolutionary dynamics. Remarkably, the statistics for coalescence of genealogical lineages, derived from those deterministic paths, strongly resemble the statistics emerging from demographic noise alone in uniform landscapes. This cautions interpretations of coalescence statistics and raises new challenges for inferring past population dynamics.
Desynchrony induced by higher-order interactions in triplex metapopulations
Authors: Palash Kumar Pal, Md Sayeed Anwar, Dibakar Ghosh
Date: 2023-10-18 00:00:00

In a predator-prey metapopulation, the two traits are adversely related: synchronization and persistence. A decrease in synchrony apparently leads to an increase in persistence and, therefore, necessitates the study of desynchrony in a metapopulation. In this article, we study predator-prey patches that communicate with one another while being interconnected through distinct dispersal structures in the layers of a three-layer multiplex network. We investigate the synchronization phenomenon among the patches of the outer layers by introducing higher-order interactions (specifically three-body interactions) in the middle layer. We observe a decrease in the synchronous behavior or, alternatively, an increase in desynchrony due to the inclusion of group interactions among the patches of the middle layer. The advancement of desynchrony becomes more prominent with increasing strength and numbers of three-way interactions in the middle layer. We analytically validated our numerical results by performing the stability analysis of the referred synchronous solution using the master stability function approach. Additionally, we verify our findings by taking into account two distinct predator-prey models and dispersal topologies, which ultimately assert that the findings are generalizable across various models and dispersal structures.
Break-up and Recovery of Harmony between Direct and Indirect Pathways in The Basal Ganglia; Huntington's Disease and Treatment
Authors: Sang-Yoon Kim, Woochang Lim
Date: 2023-10-18 00:00:00

The basal ganglia (BG) in the brain exhibit diverse functions for motor, cognition, and emotion. Such BG functions could be made via competitive harmony between the two competing pathways, direct pathway (DP) (facilitating movement) and indirect pathway (IP) (suppressing movement). As a result of break-up of harmony between DP and IP, there appear pathological states with disorder for movement, cognition, and psychiatry. In this paper, we are concerned about the Huntington's disease (HD), which is a genetic neurodegenerative disorder causing involuntary movement and severe cognitive and psychiatric symptoms. For the HD, the number of D2 SPNs ($N_{\rm D2}$) is decreased due to degenerative loss, and hence, by decreasing $x_{\rm D2}$ (fraction of $N_{\rm D2}$), we investigate break-up of harmony between DP and IP in terms of their competition degree ${\cal C}_d$, given by the ratio of strength of DP (${\cal S}_{DP}$) to strength of IP (${\cal S}_{IP}$) (i.e., ${\cal C}_d = {\cal S}_{DP} / {\cal S}_{IP}$). In the case of HD, the IP is under-active, in contrast to the case of Parkinson's disease with over-active IP, which results in increase in ${\cal C}_d$ (from the normal value). Thus, hyperkinetic dyskinesia such as chorea (involuntary jerky movement) occurs. We also investigate treatment of HD, based on optogenetics and GP ablation, by increasing strength of IP, resulting in recovery of harmony between DP and IP. Finally, we study effect of loss of healthy synapses of all the BG cells on HD. Due to loss of healthy synapses, disharmony between DP and IP increases, leading to worsen symptoms of the HD.
How enlightened self-interest guided global vaccine sharing benefits all: a modelling study
Authors: Zhenyu Han, Qianyue Hao, Qiwei He, Katherine Budeski, Depeng Jin, Fengli Xu, Kun Tang
Date: 2023-10-13 00:00:00
Tasks: All

Background: Despite the consensus that vaccines play an important role in combating the global spread of infectious diseases, vaccine inequity is still rampant with deep-seated mentality of self-priority. This study aims to evaluate the existence and possible outcomes of a more equitable global vaccine distribution and explore a concrete incentive mechanism that promotes vaccine equity. Methods: We design a metapopulation epidemiological model that simultaneously considers global vaccine distribution and human mobility, which is then calibrated by the number of infections and real-world vaccination records during COVID-19 pandemic from March 2020 to July 2021. We explore the possibility of the enlightened self-interest incentive mechanism, i.e., improving one's own epidemic outcomes by sharing vaccines with other countries, by evaluating the number of infections and deaths under various vaccine sharing strategies using the proposed model. To understand how these strategies affect the national interests, we distinguish the imported and local cases for further cost-benefit analyses that rationalize the enlightened self-interest incentive mechanism behind vaccine sharing. ...
Machine Learning for Staggered Difference-in-Differences and Dynamic Treatment Effect Heterogeneity
Authors: Julia Hatamyar, Noemi Kreif, Rudi Rocha, Martin Huber
Date: 2023-10-18 00:00:00

We combine two recently proposed nonparametric difference-in-differences methods, extending them to enable the examination of treatment effect heterogeneity in the staggered adoption setting using machine learning. The proposed method, machine learning difference-in-differences (MLDID), allows for estimation of time-varying conditional average treatment effects on the treated, which can be used to conduct detailed inference on drivers of treatment effect heterogeneity. We perform simulations to evaluate the performance of MLDID and find that it accurately identifies the true predictors of treatment effect heterogeneity. We then use MLDID to evaluate the heterogeneous impacts of Brazil's Family Health Program on infant mortality, and find those in poverty and urban locations experienced the impact of the policy more quickly than other subgroups.
Trimmed Mean Group Estimation of Average Effects in Ultra Short T Panels under Correlated Heterogeneity
Authors: M. Hashem Pesaran, Liying Yang
Date: 2023-10-18 00:00:00

The commonly used two-way fixed effects estimator is biased under correlated heterogeneity and can lead to misleading inference. This paper proposes a new trimmed mean group (TMG) estimator which is consistent at the irregular rate of n^{1/3} even if the time dimension of the panel is as small as the number of its regressors. Extensions to panels with time effects are provided, and a Hausman test of correlated heterogeneity is proposed. Small sample properties of the TMG estimator (with and without time effects) are investigated by Monte Carlo experiments and shown to be satisfactory and perform better than other trimmed estimators proposed in the literature. The proposed test of correlated heterogeneity is also shown to have the correct size and satisfactory power. The utility of the TMG approach is illustrated with an empirical application.
Inference for Nonlinear Endogenous Treatment Effects Accounting for High-Dimensional Covariate Complexity
Authors: Qingliang Fan, Zijian Guo, Ziwei Mei, Cun-Hui Zhang
Date: 2023-10-12 00:00:00

Nonlinearity and endogeneity are prevalent challenges in causal analysis using observational data. This paper proposes an inference procedure for a nonlinear and endogenous marginal effect function, defined as the derivative of the nonparametric treatment function, with a primary focus on an additive model that includes high-dimensional covariates. Using the control function approach for identification, we implement a regularized nonparametric estimation to obtain an initial estimator of the model. Such an initial estimator suffers from two biases: the bias in estimating the control function and the regularization bias for the high-dimensional outcome model. Our key innovation is to devise the double bias correction procedure that corrects these two biases simultaneously. Building on this debiased estimator, we further provide a confidence band of the marginal effect function. Simulations and an empirical study of air pollution and migration demonstrate the validity of our procedures.
Estimating Individual Responses when Tomorrow Matters
Authors: Stephane Bonhomme, Angela Denis
Date: 2023-10-13 00:00:00
Tasks: counterfactual, regression

We propose a regression-based approach to estimate how individuals' expectations influence their responses to a counterfactual change. We provide conditions under which average partial effects based on regression estimates recover structural effects. We propose a practical three-step estimation method that relies on panel data on subjective expectations. We illustrate our approach in a model of consumption and saving, focusing on the impact of an income tax that not only changes current income but also affects beliefs about future income. Applying our approach to Italian survey data, we find that individuals' beliefs matter for evaluating the impact of tax policies on consumption decisions.
Smoothed instrumental variables quantile regression
Authors: David M. Kaplan
Date: 2023-10-13 00:00:00
Tasks: quantile regression, regression

In this article, I introduce the sivqr command, which estimates the coefficients of the instrumental variables (IV) quantile regression model introduced by Chernozhukov and Hansen (2005). The sivqr command offers several advantages over the existing ivqreg and ivqreg2 commands for estimating this IV quantile regression model, which complements the alternative "triangular model" behind cqiv and the "local quantile treatment effect" model of ivqte. Computationally, sivqr implements the smoothed estimator of Kaplan and Sun (2017), who show that smoothing improves both computation time and statistical accuracy. Standard errors are computed analytically or by Bayesian bootstrap; for non-iid sampling, sivqr is compatible with bootstrap. I discuss syntax and the underlying methodology, and I compare sivqr with other commands in an example.
Real-time Prediction of the Great Recession and the Covid-19 Recession
Authors: Seulki Chung
Date: 2023-10-12 00:00:00
Tasks: regression

This paper uses standard and penalized logistic regression models to predict the Great Recession and the Covid-19 recession in the US in real time. It examines the predictability of various macroeconomic and financial indicators with respect to the NBER recession indicator. The findings strongly support the use of penalized logistic regression models in recession forecasting. These models, particularly the ridge logistic regression model, outperform the standard logistic regression model in predicting the Great Recession in the US across different forecast horizons. The study also confirms the traditional significance of the term spread as an important recession indicator. However, it acknowledges that the Covid-19 recession remains unpredictable due to the unprecedented nature of the pandemic. The results are validated by creating a recession indicator through principal component analysis (PCA) on selected variables, which strongly correlates with the NBER recession indicator and is less affected by publication lags.
Structural Vector Autoregressions and Higher Moments: Challenges and Solutions in Small Samples
Authors: Sascha A. Keweloh
Date: 2023-10-12 00:00:00

Generalized method of moments estimators based on higher-order moment conditions derived from independent shocks can be used to identify and estimate the simultaneous interaction in structural vector autoregressions. This study highlights two problems that arise when using these estimators in small samples. First, imprecise estimates of the asymptotically efficient weighting matrix and the asymptotic variance lead to volatile estimates and inaccurate inference. Second, many moment conditions lead to a small sample scaling bias towards innovations with a variance smaller than the normalizing unit variance assumption. To address the first problem, I propose utilizing the assumption of independent structural shocks to estimate the efficient weighting matrix and the variance of the estimator. For the second issue, I propose incorporating a continuously updated scaling term into the weighting matrix, eliminating the scaling bias. To demonstrate the effectiveness of these measures, I conducted a Monte Carlo simulation which shows a significant improvement in the performance of the estimator.
Marital Sorting, Household Inequality and Selection
Authors: Iván Fernández-Val, Aico van Vuuren, Francis Vella
Date: 2023-10-11 00:00:00

Using CPS data for 1976 to 2022 we explore how wage inequality has evolved for married couples with both spouses working full time full year, and its impact on household income inequality. We also investigate how marriage sorting patterns have changed over this period. To determine the factors driving income inequality we estimate a model explaining the joint distribution of wages which accounts for the spouses' employment decisions. We find that income inequality has increased for these households and increased assortative matching of wages has exacerbated the inequality resulting from individual wage growth. We find that positive sorting partially reflects the correlation across unobservables influencing both members' of the marriage wages. We decompose the changes in sorting patterns over the 47 years comprising our sample into structural, composition and selection effects and find that the increase in positive sorting primarily reflects the increased skill premia for both observed and unobserved characteristics.
Integration or fragmentation? A closer look at euro area financial markets
Authors: Martin Feldkircher, Karin Klieber
Date: 2023-10-11 00:00:00

This paper examines the degree of integration at euro area financial markets. To that end, we estimate overall and country-specific integration indices based on a panel vector-autoregression with factor stochastic volatility. Our results indicate a more heterogeneous bond market compared to the market for lending rates. At both markets, the global financial crisis and the sovereign debt crisis led to a severe decline in financial integration, which fully recovered since then. We furthermore identify countries that deviate from their peers either by responding differently to crisis events or by taking on different roles in the spillover network. The latter analysis reveals two set of countries, namely a main body of countries that receives and transmits spillovers and a second, smaller group of spillover absorbing economies. Finally, we demonstrate by estimating an augmented Taylor rule that euro area short-term interest rates are positively linked to the level of integration on the bond market.
Über wissenschaftliche Exzellenz und Wettbewerb
Authors: Gerald Schweiger
Date: 2023-10-14 00:00:00

The pursuit of excellence seems to be the True North of academia. What is meant by excellence? Can excellence be measured? This article discusses the concept of excellence in the context of research and competition.
Global Factors in Non-core Bank Funding and Exchange Rate Flexibility
Authors: Luís A. V. Catão, Jan Ditzen, Daniel Marcel te Kaat
Date: 2023-10-17 00:00:00

We show that fluctuations in the ratio of non-core to core funding in the banking systems of advanced economies are largely driven by three global factors of both real and financial natures, with country-specific factors playing only a minor role. Exchange rate flexibility helps insulate the non-core to core ratio from such global factors. This insulation is stronger in periods away from global crises. Tighter prudential regulations appear to have a complementary effect to exchange rate insulation.