pwc-archive/papers-with-abstracts huggingface dataset:
Information index augmented eRG to model vaccination behaviour: A case study of COVID-19 in the US
Authors: Bruno Buonomo, Alessandra D'Alise, Rossella Della Marca, Francesco Sannino
Date: 2024-07-30 00:00:00

Recent pandemics triggered the development of a number of mathematical models and computational tools apt at curbing the socio-economic impact of these and future pandemics. The need to acquire solid estimates from the data led to the introduction of effective approaches such as the \emph{epidemiological Renormalization Group} (eRG). A recognized relevant factor impacting the evolution of pandemics is the feedback stemming from individuals' choices. The latter can be taken into account via the \textit{information index} which accommodates the information--induced perception regarding the status of the disease and the memory of past spread. We, therefore, show how to augment the eRG by means of the information index. We first develop the {\it behavioural} version of the eRG and then test it against the US vaccination campaign for COVID-19. We find that the behavioural augmented eRG improves the description of the pandemic dynamics of the US divisions for which the epidemic peak occurs after the start of the vaccination campaign. Our results strengthen the relevance of taking into account the human behaviour component when modelling pandemic evolution. To inform public health policies, the model can be readily employed to investigate the socio-epidemiological dynamics, including vaccination campaigns, for other regions of the world.
Inferring financial stock returns correlation from complex network analysis
Authors: Ixandra Achitouv
Date: 2024-07-29 00:00:00

Financial stock returns correlations have been studied in the prism of random matrix theory, to distinguish the signal from the "noise". Eigenvalues of the matrix that are above the rescaled Marchenko Pastur distribution can be interpreted as collective modes behavior while the modes under are usually considered as noise. In this analysis we use complex network analysis to simulate the "noise" and the "market" component of the return correlations, by introducing some meaningful correlations in simulated geometric Brownian motion for the stocks. We find that the returns correlation matrix is dominated by stocks with high eigenvector centrality and clustering found in the network. We then use simulated "market" random walks to build an optimal portfolio and find that the overall return performs better than using the historical mean-variance data, up to 50% on short time scale.
A Path Integral Approach for Time-Dependent Hamiltonians with Applications to Derivatives Pricing
Authors: Mark Stedman, Luca Capriotti
Date: 2024-08-04 00:00:00
Tasks: Computational Efficiency

We generalize a semi-classical path integral approach originally introduced by Giachetti and Tognetti [Phys. Rev. Lett. 55, 912 (1985)] and Feynman and Kleinert [Phys. Rev. A 34, 5080 (1986)] to time-dependent Hamiltonians, thus extending the scope of the method to the pricing of financial derivatives. We illustrate the accuracy of the approach by presenting results for the well-known, but analytically intractable, Black-Karasinski model for the dynamics of interest rates. The accuracy and computational efficiency of this path integral approach makes it a viable alternative to fully-numerical schemes for a variety of applications in derivatives pricing.
Scheduling Quantum Annealing for Active User Detection in a NOMA Network
Authors: Romain Piron, Claire Goursaud
Date: 2024-07-31 00:00:00
Tasks: Decoder, Scheduling

Active user detection in a non-orthogonal multiple access (NOMA) network is a major challenge for 5G/6G applications. However, classical algorithms that can perform this task suffer either from complexity or reduced performances. This work aims at proposing a quantum annealing approach to overcome this trade-off. Firstly, we show that the maximum a posteriori decoder of the activity pattern of the network can be seen as the ground state of an Ising Hamiltonian. For N users in a network with perfect channels, we propose a universal control function to schedule the annealing process. Our approach avoids to continuously compute the optimal control function but still ensures high success probability while demanding a lower annealing time than a linear control function. This advantage holds even in the presence of imperfections in the network.
Efficient Asymmetric Causality Tests
Authors: Abdulnasser Hatemi-J
Date: 2024-08-06 00:00:00

Asymmetric causality tests are increasingly gaining popularity in different scientific fields. This approach corresponds better to reality since logical reasons behind asymmetric behavior exist and need to be considered in empirical investigations. Hatemi-J (2012) introduced the asymmetric causality tests via partial cumulative sums for positive and negative components of the variables operating within the vector autoregressive (VAR) model. However, since the residuals across the equations in the VAR model are not independent, the ordinary least squares method for estimating the parameters is not efficient. Additionally, asymmetric causality tests mean having different causal parameters (i.e., for positive or negative components), thus, it is crucial to assess not only if these causal parameters are individually statistically significant, but also if their difference is statistically significant. Consequently, tests of difference between estimated causal parameters should explicitly be conducted, which are neglected in the existing literature. The purpose of the current paper is to deal with these issues explicitly. An application is provided, and ten different hypotheses pertinent to the asymmetric causal interaction between two largest financial markets worldwide are efficiently tested within a multivariate setting.
A nonparametric test for diurnal variation in spot correlation processes
Authors: Kim Christensen, Ulrich Hounyo, Zhi Liu
Date: 2024-08-05 00:00:00

The association between log-price increments of exchange-traded equities, as measured by their spot correlation estimated from high-frequency data, exhibits a pronounced upward-sloping and almost piecewise linear relationship at the intraday horizon. There is notably lower-on average less positive-correlation in the morning than in the afternoon. We develop a nonparametric testing procedure to detect such deterministic variation in a correlation process. The test statistic has a known distribution under the null hypothesis, whereas it diverges under the alternative. It is robust against stochastic correlation. We run a Monte Carlo simulation to discover the finite sample properties of the test statistic, which are close to the large sample predictions, even for small sample sizes and realistic levels of diurnal variation. In an application, we implement the test on a monthly basis for a high-frequency dataset covering the stock market over an extended period. The test leads to rejection of the null most of the time. This suggests diurnal variation in the correlation process is a nontrivial effect in practice.
Testing identifying assumptions in Tobit Models
Authors: Santiago Acerenza, Otávio Bartalotti, Federico Veneri
Date: 2024-08-05 00:00:00

This paper develops sharp testable implications for Tobit and IV-Tobit models' identifying assumptions: linear index specification, (joint) normality of latent errors, and treatment (instrument) exogeneity and relevance. The new sharp testable equalities can detect all possible observable violations of the identifying conditions. We propose a testing procedure for the model's validity using existing inference methods for intersection bounds. Simulation results suggests proper size for large samples and that the test is powerful to detect large violation of the exogeneity assumption and violations in the error structure. Finally, we review and propose new alternative paths to partially identify the parameters of interest under less restrictive assumptions.
Analysis of Factors Affecting the Entry of Foreign Direct Investment into Indonesia (Case Study of Three Industrial Sectors in Indonesia)
Authors: Tracy Patricia Nindry Abigail Rolnmuch, Yuhana Astuti
Date: 2024-08-04 00:00:00

The realization of FDI and DDI from January to December 2022 reached Rp1,207.2 trillion. The largest FDI investment realization by sector was led by the Basic Metal, Metal Goods, Non-Machinery, and Equipment Industry sector, followed by the Mining sector and the Electricity, Gas, and Water sector. The uneven amount of FDI investment realization in each industry and the impact of the COVID-19 pandemic in Indonesia are the main issues addressed in this study. This study aims to identify the factors that influence the entry of FDI into industries in Indonesia and measure the extent of these factors' influence on the entry of FDI. In this study, classical assumption tests and hypothesis tests are conducted to investigate whether the research model is robust enough to provide strategic options nationally. Moreover, this study uses the ordinary least squares (OLS) method. The results show that the electricity factor does not influence FDI inflows in the three industries. The Human Development Index (HDI) factor has a significant negative effect on FDI in the Mining Industry and a significant positive effect on FDI in the Basic Metal, Metal Goods, Non-Machinery, and Equipment Industries. However, HDI does not influence FDI in the Electricity, Gas, and Water Industries in Indonesia.
Distributional Difference-in-Differences Models with Multiple Time Periods
Authors: Andrea Ciaccio
Date: 2024-08-02 00:00:00
Tasks: counterfactual

Researchers are often interested in evaluating the impact of a policy on the entire (or specific parts of the) distribution of the outcome of interest. In this paper, I provide a method to recover the whole distribution of the untreated potential outcome for the treated group in non-experimental settings with staggered treatment adoption by generalizing the existing quantile treatment effects on the treated (QTT) estimator proposed by Callaway and Li (2019). Besides the QTT, I consider different approaches that anonymously summarize the quantiles of the distribution of the outcome of interest (such as tests for stochastic dominance rankings) without relying on rank invariance assumptions. The finite-sample properties of the estimator proposed are analyzed via different Monte Carlo simulations. Despite being slightly biased for relatively small sample sizes, the proposed method's performance increases substantially when the sample size increases.
Testing for the Asymmetric Optimal Hedge Ratios: With an Application to Bitcoin
Authors: Abdulnasser Hatemi-J
Date: 2024-07-29 00:00:00
Tasks: Position

Reducing financial risk is of paramount importance to investors, financial institutions, and corporations. Since the pioneering contribution of Johnson (1960), the optimal hedge ratio based on futures is regularly utilized. The current paper suggests an explicit and efficient method for testing the null hypothesis of a symmetric optimal hedge ratio against an asymmetric alternative one within a multivariate setting. If the null is rejected, the position dependent optimal hedge ratios can be estimated via the suggested model. This approach is expected to enhance the accuracy of the implemented hedging strategies compared to the standard methods since it accounts for the fact that the source of risk depends on whether the investor is a buyer or a seller of the risky asset. An application is provided using spot and futures prices of Bitcoin. The results strongly support the view that the optimal hedge ratio for this cryptocurrency is position dependent. The investor that is long in Bitcoin has a much higher conditional optimal hedge ratio compared to the one that is short in the asset. The difference between the two conditional optimal hedge ratios is statistically significant, which has important repercussions for implementing risk management strategies.
Identification and Inference for Synthetic Control Methods with Spillover Effects: Estimating the Economic Cost of the Sudan Split
Authors: Shosei Sakaguchi, Hayato Tagawa
Date: 2024-08-01 00:00:00
Tasks: Bayesian Inference, Causal Inference

The synthetic control method (SCM) is widely used for causal inference with panel data, particularly when there are few treated units. SCM assumes the stable unit treatment value assumption (SUTVA), which posits that potential outcomes are unaffected by the treatment status of other units. However, interventions often impact not only treated units but also untreated units, known as spillover effects. This study introduces a novel panel data method that extends SCM to allow for spillover effects and estimate both treatment and spillover effects. This method leverages a spatial autoregressive panel data model to account for spillover effects. We also propose Bayesian inference methods using Bayesian horseshoe priors for regularization. We apply the proposed method to two empirical studies: evaluating the effect of the California tobacco tax on consumption and estimating the economic impact of the 2011 division of Sudan on GDP per capita.
Potential weights and implicit causal designs in linear regression
Authors: Jiafeng Chen
Date: 2024-07-30 00:00:00
Tasks: Causal Inference, counterfactual, regression

When we interpret linear regression estimates as causal effects justified by quasi-experiments, what do we mean? This paper characterizes the necessary implications when researchers ascribe a design-based interpretation to a given regression. To do so, we define a notion of potential weights, which encode counterfactual decisions a given regression makes to unobserved potential outcomes. A plausible design-based interpretation for a regression estimand implies linear restrictions on the true distribution of treatment; the coefficients in these linear equations are exactly potential weights. Solving these linear restrictions leads to a set of implicit designs that necessarily include the true design if the regression were to admit a causal interpretation. These necessary implications lead to practical diagnostics that add transparency and robustness when design-based interpretation is invoked for a regression. They also lead to new theoretical insights: They serve as a framework that unifies and extends existing results, and they lead to new results for widely used but less understood specifications.
On the power properties of inference for parameters with interval identified sets
Authors: Federico A. Bugni, Mengsi Gao, Filip Obradovic, Amilcar Velez
Date: 2024-07-29 00:00:00

This paper studies the power properties of confidence intervals (CIs) for a partially-identified parameter of interest with an interval identified set. We assume the researcher has bounds estimators to construct the CIs proposed by Stoye (2009), referred to as CI1, CI2, and CI3. We also assume that these estimators are "ordered": the lower bound estimator is less than or equal to the upper bound estimator. Under these conditions, we establish two results. First, we show that CI1 and CI2 are equally powerful, and both dominate CI3. Second, we consider a favorable situation in which there are two possible bounds estimators to construct these CIs, and one is more efficient than the other. One would expect that the more efficient bounds estimator yields more powerful inference. We prove that this desirable result holds for CI1 and CI2, but not necessarily for CI3.
Heterogeneous Grouping Structures in Panel Data
Authors: Katerina Chrysikou, George Kapetanios
Date: 2024-07-28 00:00:00

In this paper we examine the existence of heterogeneity within a group, in panels with latent grouping structure. The assumption of within group homogeneity is prevalent in this literature, implying that the formation of groups alleviates cross-sectional heterogeneity, regardless of the prior knowledge of groups. While the latter hypothesis makes inference powerful, it can be often restrictive. We allow for models with richer heterogeneity that can be found both in the cross-section and within a group, without imposing the simple assumption that all groups must be heterogeneous. We further contribute to the method proposed by \cite{su2016identifying}, by showing that the model parameters can be consistently estimated and the groups, while unknown, can be identifiable in the presence of different types of heterogeneity. Within the same framework we consider the validity of assuming both cross-sectional and within group homogeneity, using testing procedures. Simulations demonstrate good finite-sample performance of the approach in both classification and estimation, while empirical applications across several datasets provide evidence of multiple clusters, as well as reject the hypothesis of within group homogeneity.
Using Total Margin of Error to Account for Non-Sampling Error in Election Polls: The Case of Nonresponse
Authors: Jeff Dominitz, Charles F. Manski
Date: 2024-07-27 00:00:00
Tasks: Survey

The potential impact of non-sampling errors on election polls is well known, but measurement has focused on the margin of sampling error. Survey statisticians have long recommended measurement of total survey error by mean square error (MSE), which jointly measures sampling and non-sampling errors. We think it reasonable to use the square root of maximum MSE to measure the total margin of error (TME). Measurement of TME should encompass both sampling error and all forms of non-sampling error. We suggest that measurement of TME should be a standard feature in the reporting of polls. To provide a clear illustration, and because we believe the exceedingly low response rates commonly obtained by election polls to be a particularly worrisome source of potential error, we demonstrate how to measure the potential impact of nonresponse using the concept of TME. We first show how to measure TME when a pollster lacks any knowledge of the candidate preferences of nonrespondents. We then extend the analysis to settings where the pollster has partial knowledge that bounds the preferences of non-respondents. In each setting, we derive a simple poll estimate that approximately minimizes TME, a midpoint estimate, and compare it to a conventional poll estimate.
Existence and uniqueness of quadratic and linear mean-variance equilibria in general semimartingale markets
Authors: Christoph Czichowsky, Martin Herdegen, David Martins
Date: 2024-08-06 00:00:00

We revisit the classical topic of quadratic and linear mean-variance equilibria with both financial and real assets. The novelty of our results is that they are the first allowing for equilibrium prices driven by general semimartingales and hold in discrete as well as continuous time. For agents with quadratic utility functions, we provide necessary and sufficient conditions for the existence and uniqueness of equilibria. We complement our analysis by providing explicit examples showing non-uniqueness or non-existence of equilibria. We then study the more difficult case of linear mean-variance preferences. We first show that under mild assumptions, a linear mean-variance equilibrium corresponds to a quadratic equilibrium (for different preference parameters). We then use this link to study a fixed-point problem that establishes existence (and uniqueness in a suitable class) of linear mean-variance equilibria. Our results rely on fine properties of dynamic mean-variance hedging in general semimartingale markets.
The Impossible Trinity of Human Space Usage between Home, Workplace and Amenity
Authors: Shizhen Wang, Stanimira Milcheva
Date: 2024-08-06 00:00:00

We propose an impossible trinity of human space usage between home, workplace, and amenity in this paper to explain mobility pattern changes and shifts in demand for space during COVID-19. We developed detailed time usage and location visit profiles for 60,131 people in England and Wales by analyzing about 120 million cell phone location and timestamp records on March 2020 and March 2021. We found that both at-home time and amenity visits increased during COVID-19, while workplace visits decreased. Individual visits to different locations are determined by three key factors: individual preference measured by pre-pandemic location visit frequency, time constraints influenced by work-from-home, and space accessibility. We also find that WFH improves equality of individual amenity usage between people of different incomes. Low-income and middle-income people saw an 8% and 4% increase in additional amenity visits, respectively, compared to high-income people during the pandemic.
Modeling the impact of Climate transition on real estate prices
Authors: Lionel Sopgoui
Date: 2024-08-05 00:00:00

In this work, we propose a model to quantify the impact of the climate transition on a property in housing market. We begin by noting that property is an asset in an economy. That economy is organized in sectors, driven by its productivity which is a multidimensional Ornstein-Uhlenbeck process, while the climate transition is declined thanks to the carbon price, a continuous deterministic process. We then extend the sales comparison approach and the income approach to valuate an energy inefficient real estate asset. We obtain its value as the difference between the price of an equivalent efficient building following an exponential Ornstein-Uhlenbeck as well as the actualized renovation costs and the actualized sum of the future additional energy costs. These costs are due to the inefficiency of the building, before an optimal renovation date which depends on the carbon price process. Finally, we carry out simulations based on the French economy and the house price index of France. Our results allow to conclude that the order of magnitude of the depreciation obtained by our model is the same as the empirical observations.
Towards Fair Roads -- Manifesto For Fairness Paradigm in Traffic Engineering
Authors: Kevin Riehl, Anastasios Kouvelas, Michail Makridis
Date: 2024-08-02 00:00:00
Tasks: Fairness

Traffic engineering aims to control infrastructure and population behaviour to achieve optimal usage of road networks. Fairness is fundamental to stimulate cooperation in large populations, and plays an important role in traffic engineering, as it increases the well-being of users, improves driving safety by rule-adherence, and overcomes public resistance at legislative implementation. Despite the importance of fairness, only a few works have translated fairness into the transportation domain, with a focus on transportation planning rather than traffic engineering. Moreover, existing works in traffic engineering discuss fairness superficially and insufficiently, focussing on only a few definitions. The mission of this work is (i) to challenge the efficiency-driven engineering paradigm of traffic engineering, (ii) to establish a link to modern fairness theories, and (iii) to highlight the importance of fairness when allocating scarce, public good, mobility resources between road users. To achieve this, a mode-agnostic, distributive fairness framework for mobility resource allocation is proposed. It serves when designing traffic engineering solutions, and convinces in public debates with a useful, argumentative tool-set to confront equity considerations. Ultimately, this enables systematic research and design of fairness-informed control systems, demonstrated by three case studies on signalized intersection management and static road pricing.