pwc-archive/papers-with-abstracts huggingface dataset:
Why Topological Data Analysis Detects Financial Bubbles?
Authors: Samuel W. Akingbade, Marian Gidea, Matteo Manzi, Vahid Nateghi
Date: 2023-04-14 00:00:00
Tasks: Time Series, Topological Data Analysis

We present a heuristic argument for the propensity of Topological Data Analysis (TDA) to detect early warning signals of critical transitions in financial time series. Our argument is based on the Log-Periodic Power Law Singularity (LPPLS) model, which characterizes financial bubbles as super-exponential growth (or decay) of an asset price superimposed with oscillations increasing in frequency and decreasing in amplitude when approaching a critical transition (tipping point). We show that whenever the LPPLS model is fitting with the data, TDA generates early warning signals. As an application, we illustrate this approach on a sample of positive and negative bubbles in the Bitcoin historical price.
Adjustment with Many Regressors Under Covariate-Adaptive Randomizations
Authors: Liang Jiang, Liyao Li, Ke Miao, Yichong Zhang
Date: 2023-04-17 00:00:00
Tasks: Causal Inference, regression

Our paper discovers a new trade-off of using regression adjustments (RAs) in causal inference under covariate-adaptive randomizations (CARs). On one hand, RAs can improve the efficiency of causal estimators by incorporating information from covariates that are not used in the randomization. On the other hand, RAs can degrade estimation efficiency due to their estimation errors, which are not asymptotically negligible when the number of regressors is of the same order as the sample size. Ignoring the estimation errors of RAs may result in serious over-rejection of causal inference under the null hypothesis. To address the issue, we construct a new ATE estimator by optimally linearly combining the estimators with and without RAs. We then develop a unified inference theory for this estimator under CARs. It has two features: (1) the Wald test based on it achieves the exact asymptotic size under the null hypothesis, regardless of whether the number of covariates is fixed or diverges no faster than the sample size; and (2) it guarantees weak efficiency improvement over estimators both with and without RAs.
Sparse time-varying parameter VECMs with an application to modeling electricity prices
Authors: Niko Hauzenberger, Michael Pfarrhofer, Luca Rossini
Date: 2020-11-09 00:00:00
Tasks: parameter estimation

In this paper we propose a time-varying parameter (TVP) vector error correction model (VECM) with heteroskedastic disturbances. We propose tools to carry out dynamic model specification in an automatic fashion. This involves using global-local priors, and postprocessing the parameters to achieve truly sparse solutions. Depending on the respective set of coefficients, we achieve this via minimizing auxiliary loss functions. Our two-step approach limits overfitting and reduces parameter estimation uncertainty. We apply this framework to modeling European electricity prices. When considering daily electricity prices for different markets jointly, our model highlights the importance of explicitly addressing cointegration and nonlinearities. In a forecast exercise focusing on hourly prices for Germany, our approach yields competitive metrics of predictive accuracy.
Coarsened Bayesian VARs -- Correcting BVARs for Incorrect Specification
Authors: Florian Huber, Massimiliano Marcellino
Date: 2023-04-16 00:00:00

Model mis-specification in multivariate econometric models can strongly influence quantities of interest such as structural parameters, forecast distributions or responses to structural shocks, even more so if higher-order forecasts or responses are considered, due to parameter convolution. We propose a simple method for addressing these specification issues in the context of Bayesian VARs. Our method, called coarsened Bayesian VARs (cBVARs), replaces the exact likelihood with a coarsened likelihood that takes into account that the model might be mis-specified along important but unknown dimensions. Coupled with a conjugate prior, this results in a computationally simple model. As opposed to more flexible specifications, our approach avoids overfitting, is simple to implement and estimation is fast. The resulting cBVAR performs well in simulations for several types of mis-specification. Applied to US data, cBVARs improve point and density forecasts compared to standard BVARs, and lead to milder but more persistent negative effects of uncertainty shocks on output.
Generalized Automatic Least Squares: Efficiency Gains from Misspecified Heteroscedasticity Models
Authors: Bulat Gafarov
Date: 2023-04-14 00:00:00

It is well known that in the presence of heteroscedasticity ordinary least squares estimator is not efficient. I propose a generalized automatic least squares estimator (GALS) that makes partial correction of heteroscedasticity based on a (potentially) misspecified model without a pretest. Such an estimator is guaranteed to be at least as efficient as either OLS or WLS but can provide some asymptotic efficiency gains over OLS if the misspecified model is approximately correct. If the heteroscedasticity model is correct, the proposed estimator achieves full asymptotic efficiency. The idea is to frame moment conditions corresponding to OLS and WLS squares based on miss-specified heteroscedasticity as a joint generalized method of moments estimation problem. The resulting optimal GMM estimator is equivalent to a feasible GLS with estimated weight matrix. I also propose an optimal GMM variance-covariance estimator for GALS to account for any remaining heteroscedasticity in the residuals.
Predictive Incrementality by Experimentation (PIE) for Ad Measurement
Authors: Brett R. Gordon, Robert Moakler, Florian Zettelmeyer
Date: 2023-04-13 00:00:00
Tasks: Causal Inference, Decision Making

We present a novel approach to causal measurement for advertising, namely to use exogenous variation in advertising exposure (RCTs) for a subset of ad campaigns to build a model that can predict the causal effect of ad campaigns that were run without RCTs. This approach -- Predictive Incrementality by Experimentation (PIE) -- frames the task of estimating the causal effect of an ad campaign as a prediction problem, with the unit of observation being an RCT itself. In contrast, traditional causal inference approaches with observational data seek to adjust covariate imbalance at the user level. A key insight is to use post-campaign features, such as last-click conversion counts, that do not require an RCT, as features in our predictive model. We find that our PIE model recovers RCT-derived incremental conversions per dollar (ICPD) much better than the program evaluation approaches analyzed in Gordon et al. (forthcoming). The prediction errors from the best PIE model are 48%, 42%, and 62% of the RCT-based average ICPD for upper-, mid-, and lower-funnel conversion outcomes, respectively. In contrast, across the same data, the average prediction error of stratified propensity score matching exceeds 491%, and that of double/debiased machine learning exceeds 2,904%. Using a decision-making framework inspired by industry, we show that PIE leads to different decisions compared to RCTs for only 6% of upper-funnel, 7% of mid-funnel, and 13% of lower-funnel outcomes. We conclude that PIE could enable advertising platforms to scale causal ad measurement by extrapolating from a limited number of RCTs to a large set of non-experimental ad campaigns.
Economic consequences of the spatial and temporal variability of climate change
Authors: Francisco Estrada, Richard S. J. Tol, Wouter Botzen
Date: 2023-04-17 00:00:00

Damage functions in integrated assessment models (IAMs) map changes in climate to economic impacts and form the basis for most of estimates of the social cost of carbon. Implicit in these functions lies an unwarranted assumption that restricts the spatial variation (Svar) and temporal variability (Tvar) of changes in climate to be null. This could bias damage estimates and the climate policy advice from IAMs. While the effects of Tvar have been studied in the literature, those of Svar and their interactions with Tvar have not. Here we present estimates of the economic costs of climate change that account for both Tvar and Svar, as well as for the seasonality of damages across sectors. Contrary to the results of recent studies which show little effect that of Tvar on expected losses, we reveal that ignoring Svar produces large downward biases, as warming is highly heterogeneous over space. Using a conservative calibration for the damage function, we show that previous estimates are biased downwards by about 23-36%, which represents additional losses of about US$1,400-US$2,300 billion by 2050 and US$17-US$28 trillion by the end of the century, under a high emissions scenario. The present value of losses during the period 2020-2100 would be larger than reported in previous studies by $47-$66 trillion or about 1/2 to 3/4 of annual global GDP in 2020. Our results imply that using global mean temperature change in IAMs as a summary measure of warming is not adequate for estimating the costs of climate change. Instead, IAMs should include a more complete description of climate conditions.
Unlocking Democratic Efficiency: How Coordinated Outcome-Contingent Promises Shape Decisions
Authors: Ali Lazrak, Jianfeng Zhang
Date: 2023-04-17 00:00:00
Tasks: Decision Making

We consider a committee voting on whether to adopt a reform under a quota rule, where members differ in how much they value the reform some supporting it, others opposing it. We examine how members can influence each other's votes through coordinated non-negative transfer promises, made prior to voting and contingent on the vote outcome. In equilibrium, these transfers are structured to prevent any coalition from profitably deviating in a coordinated way, while minimizing total transfers. We provide a complete characterization of these `strong' equilibria and show that they exist, are indeterminate, efficient, and involve transfers from high- to low-utility members. Such transfers prevent opponents from swaying less enthusiastic supporters and may be directed not only to opponents but also to lukewarm supporters.
Economic Origins of the Sicilian Mafia: A Simulation Feedback Model
Authors: Oleg V. Pavlov, Jason M. Sardell
Date: 2023-04-17 00:00:00

This chapter develops a feedback economic model that explains the rise of the Sicilian mafia in the 19th century. Grounded in economic theory, the model incorporates causal relationships between the mafia activities, predation, law enforcement, and the profitability of local businesses. Using computational experiments with the model, we explore how different factors and feedback effects impact the mafia activity levels. The model explains important historical observations such as the emergence of the mafia in wealthier regions and its absence in the poorer districts despite the greater levels of banditry.
Study on the tea market in India
Authors: Adit Vinod Nair, Adarsh Damani, Devansh Khandelwal, Harshita Sachdev, Sreayans Jain
Date: 2023-04-16 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

India's tea business has a long history and plays a significant role in the economics of the nation. India is the world's second-largest producer of tea, with Assam and Darjeeling being the most well-known tea-growing regions. Since the British introduced tea cultivation to India in the 1820s, the nation has produced tea. Millions of people are employed in the tea sector today, and it contributes significantly to the Indian economy in terms of revenue. The production of tea has changed significantly in India over the years, moving more and more towards organic and sustainable practices. The industry has also had to deal with difficulties like competition from other nations that produce tea, varying tea prices, and labor-related problems. Despite these obstacles, the Indian tea business is still growing and produces a wide variety of teas, such as black tea, green tea, and chai tea. Additionally, the sector encourages travel through "tea tourism," which allows tourists to see how tea is made and discover its origins in India. Overall, India's tea business continues to play a significant role in its history, culture, and economy.
The Impact of Automation on Income Inequality: A Cross-Country Analysis
Authors: Asuna Gilfoyle
Date: 2023-04-16 00:00:00

This study examines the relationship between automation and income inequality across different countries, taking into account the varying levels of technological adoption and labor market institutions. The research employs a panel data analysis using data from the World Bank, the International Labour Organization, and other reputable sources. The findings suggest that while automation leads to an increase in productivity, its effect on income inequality depends on the country's labor market institutions and social policies.
Game theoretical models of geopolitical processes. Part I
Authors: O. A. Malafeyev, N. D. Redinskikh, V. F. Bogachev
Date: 2023-04-15 00:00:00

In this paper, the interaction of geopolitical actors in the production and sale of military equipment is studied. In section 2 the production of military equipment is considered as the two person zero-sum game. In such game, the strategies of the players are defined by the information state of the actors. The optimal strategy of geopolitical actors is found. In section 3, the conflict process is considered, the optimal strategy is determined for each geopolitical actor.
Mean-field equilibrium price formation with exponential utility
Authors: Masaaki Fujii, Masashi Sekine
Date: 2023-04-14 00:00:00

In this paper, using the mean-field game theory, we study a problem of equilibrium price formation among many investors with exponential utility in the presence of liabilities unspanned by the security prices. The investors are heterogeneous in their initial wealth, risk-averseness parameter, as well as stochastic liability at the terminal time. We characterize the equilibrium risk-premium process of the risky stocks in terms of the solution to a novel mean-field backward stochastic differential equation (BSDE), whose driver has quadratic growth both in the stochastic integrands and in their conditional expectations. We prove the existence of a solution to the mean-field BSDE under several conditions and show that the resultant risk-premium process actually clears the market in the large population limit.
Low-carbon Lithium Extraction Makes Deep Geothermal Plants Cost-competitive in Energy Systems
Authors: Jann Michael Weinand, Ganga Vandenberg, Stanley Risch, Johannes Behrens, Noah Pflugradt, Jochen Linßen, Detlef Stolten
Date: 2023-04-14 00:00:00

Lithium is a critical material for the energy transition, but conventional procurement methods have significant environmental impacts. In this study, we utilize regional energy system optimizations to investigate the techno-economic potential of the low-carbon alternative of direct lithium extraction in deep geothermal plants. We show that geothermal plants will become cost-competitive in conjunction with lithium extraction, even under unfavorable conditions and partially displace photovoltaics, wind power, and storage from energy systems. Our analysis indicates that if 10% of municipalities in the Upper Rhine Graben area in Germany constructed deep geothermal plants, they could provide enough lithium to produce about 1.2 million electric vehicle battery packs per year, equivalent to 70% of today`s annual electric vehicle registrations in the European Union. This approach could offer significant environmental benefits and has high potential for mass application also in other countries, such as the United States, United Kingdom, France, and Italy, highlighting the importance of further research and development of this technology.
Optimism, overconfidence, and moral hazard
Authors: Ludvig Sinander
Date: 2023-04-17 00:00:00

I revisit the standard moral-hazard model, in which an agent's preference over contracts is rooted in costly effort choice. I characterise the behavioural content of the model in terms of empirically testable axioms, and show that the model's parameters are identified. I propose general behavioural definitions of relative (over)confidence and optimism, and characterise these in terms of the parameters of the moral-hazard model. My formal results are rooted in a simple but powerful insight: that the moral-hazard model is closely related to the well-known 'variational' model of choice under uncertainty.
Non-diversified portfolios with subjective expected utility
Authors: Christopher P. Chambers, Georgios Gerasimou
Date: 2023-04-17 00:00:00

Diversification is the typical investment strategy of risk-averse agents. However, non-diversified positions that allocate all resources to a single asset, state of the world or revenue stream are common too. We show that whenever finitely many non-diversified demands under uncertainty are compatible with risk-averse subjective expected utility maximization under strictly positive beliefs, they are also rationalizable under the same beliefs by many qualitatively distinct risk-averse as well as risk-neutral and risk-seeking preferences.
Data, Competition, and Digital Platforms
Authors: Dirk Bergemann, Alessandro Bonatti
Date: 2023-04-15 00:00:00
Tasks: Federated Learning

We analyze digital markets where a monopolist platform uses data to match multiproduct sellers with heterogeneous consumers who can purchase both on and off the platform. The platform sells targeted ads to sellers that recommend their products to consumers and reveals information to consumers about their values. The revenue-optimal mechanism is a managed advertising campaign that matches products and preferences efficiently. In equilibrium, sellers offer higher qualities at lower unit prices on than off the platform. Privacy-respecting data-governance rules such as organic search results or federated learning can lead to welfare gains for consumers.
Test-Optional Admissions
Authors: Wouter Dessein, Alex Frankel, Navin Kartik
Date: 2023-04-15 00:00:00
Tasks: Diversity

Many U.S. colleges now use test-optional admissions. A frequent claim is that by not seeing standardized test scores, a college can admit a student body it prefers, say with more diversity. But how can observing less information improve decisions? This paper proposes that test-optional policies are a response to social pressure on admission decisions. We model a college that bears disutility when it makes admission decisions that "society" dislikes. Going test optional allows the college to reduce its "disagreement cost". We analyze how missing scores are imputed and the consequences for the college, students, and society.
Social Welfare Functions with Voters Qualifications: Impossibility Results
Authors: Yasunori Okumura
Date: 2023-04-14 00:00:00
Tasks: All

We consider the social welfare function a la Arrow, where some voters are not qualified to evaluate some alternatives. Thus, the inputs of the social welfare function are the preferences of voters on the alternatives that they are qualified to evaluate only. Our model is a generalization of the peer rating model, where each voter evaluates the other voters (except for himself/herself). We demonstrate the following three impossibility results. First, if a transitive valued social welfare function satisfies independence of irrelevant alternatives and the Pareto principle, then a dictator who is qualified to evaluate all alternatives exists. Second, a transitive valued function satisfying the Pareto principle exists if and only if at least one voter is qualified to evaluate all alternatives. Finally, if no voter is qualified to evaluate all alternatives, then under a transitive valued social welfare function satisfying the weak Pareto principle and independence of irrelevant alternatives, all alternatives are indifferent for any preference profile of voters.
Recursive Preferences and Ambiguity Attitudes
Authors: Massimo Marinacci, Giulio Principi, Lorenzo Stanca
Date: 2023-04-13 00:00:00
Tasks: Translation

We study the implications of recursivity and state monotonicity in intertemporal consumption problems under ambiguity. We show that monotone recursive preferences admit a recursive and ex-ante representation, both with translation invariant certainty equivalents. Translation invariance restricts the decision maker's absolute ambiguity attitudes to be constant. As a byproduct, this restriction implies that monotone recursive and convex preferences collapse to the variational model. Using dynamic consistency, we show that our two representations are connected by a condition that extends the standard rectangularity notion for recursive multiple priors. This "generalized rectangularity" condition allows us to uniquely retrieve the ex-ante representation starting fromthe recursive one and to obtain dynamic consistency conditions for preferences exhibiting constant absolute ambiguity aversion. Interpreting generalized rectangularity as a law of iterated nonlinear expectations, we discuss its relevance with respect to large sample theory.
Neural TTS in French: Comparing Graphemic and Phonetic Inputs Using the SynPaFlex-Corpus and Tacotron2
Authors: Samuel Delalez, Ludi Akue
Date: 2023-04-17 00:00:00

The SynPaFlex-Corpus is a publicly available TTS-oriented dataset, which provides phonetic transcriptions automatically produced by the JTrans transcriber, with a Phoneme Error Rate (PER) of 6.1%. In this paper, we analyze two mono-speaker Tacotron2 models trained on graphemic and phonetic inputs, provided by the SynPaFlex-Corpus. Through three subjective listening tests, we compare their pronunciation accuracy, sound quality and naturalness. Our results show significantly better pronunciation accuracy and prosody naturalness for the phoneme-based model, but no significant difference in terms of perceived sound quality. They demonstrate that a PER of 6.1% is sufficient to enhance pronunciation control by using phonetic transcripts instead of graphemes with 83 hours of recorded French read speech. They suggest that the SynPaFlex-Corpus is suitable for pre-training a model in mono-speaker fine-tuning approaches.
Audio coding with unified noise shaping and phase contrast control
Authors: Byeongho Jo, SeungKwon Beack, Taejin Lee
Date: 2023-04-17 00:00:00

Over the past decade, audio coding technology has seen standardization and the development of many frameworks incorporated with linear predictive coding (LPC). As LPC reduces information in the frequency domain, LP-based frequency-domain noise-shaping (FDNS) was previously proposed. To code transient signals effectively, FDNS with temporal noise shaping (TNS) has emerged. However, these mainly operated in the modified discrete cosine transform domain, which essentially accompanies time domain aliasing. In this paper, a unified noise-shaping (UNS) framework including FDNS and complex LPC-based TNS (CTNS) in the DFT domain is proposed to overcome the aliasing issues. Additionally, a modified polar quantizer with phase contrast control is proposed, which saves phase bits depending on the frequency envelope information. The core coding feasibility at low bit rates is verified through various objective metrics and subjective listening evaluations.