pwc-archive/papers-with-abstracts huggingface dataset:
QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation
Authors: Zhuo Chen, Rumen Dangovski, Charlotte Loh, Owen Dugan, Di Luo, Marin Soljačić
Date: 2024-05-31 00:00:00
Tasks: Arithmetic Reasoning

We propose Quantum-informed Tensor Adaptation (QuanTA), a novel, easy-to-implement, fine-tuning method with no inference overhead for large-scale pre-trained language models. By leveraging quantum-inspired methods derived from quantum circuit structures, QuanTA enables efficient high-rank fine-tuning, surpassing the limitations of Low-Rank Adaptation (LoRA)--low-rank approximation may fail for complicated downstream tasks. Our approach is theoretically supported by the universality theorem and the rank representation theorem to achieve efficient high-rank adaptations. Experiments demonstrate that QuanTA significantly enhances commonsense reasoning, arithmetic reasoning, and scalability compared to traditional methods. Furthermore, QuanTA shows superior performance with fewer trainable parameters compared to other approaches and can be designed to integrate with existing fine-tuning algorithms for further improvement, providing a scalable and efficient solution for fine-tuning large language models and advancing state-of-the-art in natural language processing.
Identification of structural shocks in Bayesian VEC models with two-state Markov-switching heteroskedasticity
Authors: Justyna Wróblewska, Łukasz Kwiatkowski
Date: 2024-06-05 00:00:00

We develop a Bayesian framework for cointegrated structural VAR models identified by two-state Markovian breaks in conditional covariances. The resulting structural VEC specification with Markov-switching heteroskedasticity (SVEC-MSH) is formulated in the so-called B-parameterization, in which the prior distribution is specified directly for the matrix of the instantaneous reactions of the endogenous variables to structural innovations. We discuss some caveats pertaining to the identification conditions presented earlier in the literature on stationary structural VAR-MSH models, and revise the restrictions to actually ensure the unique global identification through the two-state heteroskedasticity. To enable the posterior inference in the proposed model, we design an MCMC procedure, combining the Gibbs sampler and the Metropolis-Hastings algorithm. The methodology is illustrated both with a simulated as well as real-world data examples.
Is local opposition taking the wind out of the energy transition?
Authors: Federica Daniele, Guido de Blasio, Alessandra Pasquini
Date: 2024-06-05 00:00:00

Local opposition to the installation of renewable energy sources is a potential threat to the energy transition. Local communities tend to oppose the construction of energy plants due to the associated negative externalities (the so-called 'not in my backyard' or NIMBY phenomenon) according to widespread belief, mostly based on anecdotal evidence. Using administrative data on wind turbine installation and electoral outcomes across municipalities located in the South of Italy during 2000-19, we estimate the impact of wind turbines' installation on incumbent regional governments' electoral support during the next elections. Our main findings, derived by a wind-speed based instrumental variable strategy, point in the direction of a mild and not statistically significant electoral backlash for right-wing regional administrations and of a strong and statistically significant positive reinforcement for left-wing regional administrations. Based on our analysis, the hypothesis of an electoral effect of NIMBY type of behavior in connection with the development of wind turbines appears not to be supported by the data.
When does IV identification not restrict outcomes?
Authors: Leonard Goff
Date: 2024-06-05 00:00:00
Tasks: counterfactual

Many identification results in instrumental variables (IV) models hold without requiring any restrictions on the distribution of potential outcomes, or how those outcomes are correlated with selection behavior. This enables IV models to allow for arbitrary heterogeneity in treatment effects and the possibility of selection on gains in the outcome. I provide a necessary and sufficient condition for treatment effects to be point identified in a manner that does not restrict outcomes, when the instruments take a finite number of values. The condition generalizes the well-known LATE monotonicity assumption, and unifies a wide variety of other known IV identification results. The result also yields a brute-force approach to reveal all selection models that allow for point identification of treatment effects without restricting outcomes, and then enumerate all of the identified parameters within each such selection model. The search uncovers new selection models that yield identification, provides impossibility results for others, and offers opportunities to relax assumptions on selection used in existing literature. An application considers the identification of complementarities between two cross-randomized treatments, obtaining a necessary and sufficient condition on selection for local average complementarities among compliers to be identified in a manner that does not restrict outcomes. I use this result to revisit two empirical settings, one in which the data are incompatible with this restriction on selection, and another in which the data are compatible with the restriction.
The Impact of Acquisition on Product Quality in the Console Gaming Industry
Authors: Shivam Somani
Date: 2024-06-04 00:00:00

The console gaming industry, a dominant force in the global entertainment sector, has witnessed a wave of consolidation in recent years, epitomized by Microsoft's high-profile acquisitions of Activision Blizzard and Zenimax. This study investigates the repercussions of such mergers on consumer welfare and innovation within the gaming landscape, focusing on product quality as a key metric. Through a comprehensive analysis employing a difference-in-difference model, the research evaluates the effects of acquisition on game review ratings, drawing from a dataset comprising over 16,000 console games released between 2000 and 2023. The research addresses key assumptions underlying the difference-in-difference methodology, including parallel trends and spillover effects, to ensure the robustness of the findings. The DID results suggest a positive and statistically significant impact of acquisition on game review ratings, when controlling for genre and release year. The study contributes to the literature by offering empirical evidence on the direct consequences of industry consolidation on consumer welfare and competition dynamics within the gaming sector.
Enabling Decision-Making with the Modified Causal Forest: Policy Trees for Treatment Assignment
Authors: Hugo Bodory, Federica Mascolo, Michael Lechner
Date: 2024-06-04 00:00:00
Tasks: Decision Making

Decision-making plays a pivotal role in shaping outcomes in various disciplines, such as medicine, economics, and business. This paper provides guidance to practitioners on how to implement a decision tree designed to address treatment assignment policies using an interpretable and non-parametric algorithm. Our Policy Tree is motivated on the method proposed by Zhou, Athey, and Wager (2023), distinguishing itself for the policy score calculation, incorporating constraints, and handling categorical and continuous variables. We demonstrate the usage of the Policy Tree for multiple, discrete treatments on data sets from different fields. The Policy Tree is available in Python's open-source package mcf (Modified Causal Forest).
A sequential test procedure for the choice of the number of regimes in multivariate nonlinear models
Authors: Andrea Bucci
Date: 2024-06-04 00:00:00

This paper proposes a sequential test procedure for determining the number of regimes in nonlinear multivariate autoregressive models. The procedure relies on linearity and no additional nonlinearity tests for both multivariate smooth transition and threshold autoregressive models. We conduct a simulation study to evaluate the finite-sample properties of the proposed test in small samples. Our findings indicate that the test exhibits satisfactory size properties, with the rescaled version of the Lagrange Multiplier test statistics demonstrating the best performance in most simulation settings. The sequential procedure is also applied to two empirical cases, the US monthly interest rates and Icelandic river flows. In both cases, the detected number of regimes aligns well with the existing literature.
Random Subspace Local Projections
Authors: Viet Hoang Dinh, Didier Nibbering, Benjamin Wong
Date: 2024-06-03 00:00:00
Tasks: Dimensionality Reduction, subspace methods

We show how random subspace methods can be adapted to estimating local projections with many controls. Random subspace methods have their roots in the machine learning literature and are implemented by averaging over regressions estimated over different combinations of subsets of these controls. We document three key results: (i) Our approach can successfully recover the impulse response functions across Monte Carlo experiments representative of different macroeconomic settings and identification schemes. (ii) Our results suggest that random subspace methods are more accurate than other dimension reduction methods if the underlying large dataset has a factor structure similar to typical macroeconomic datasets such as FRED-MD. (iii) Our approach leads to differences in the estimated impulse response functions relative to benchmark methods when applied to two widely studied empirical applications.
A Robust Residual-Based Test for Structural Changes in Factor Models
Authors: Bin Peng, Liangjun Su, Yayi Yan
Date: 2024-06-03 00:00:00

In this paper, we propose an easy-to-implement residual-based specification testing procedure for detecting structural changes in factor models, which is powerful against both smooth and abrupt structural changes with unknown break dates. The proposed test is robust against the over-specified number of factors, and serially and crosssectionally correlated error processes. A new central limit theorem is given for the quadratic forms of panel data with dependence over both dimensions, thereby filling a gap in the literature. We establish the asymptotic properties of the proposed test statistic, and accordingly develop a simulation-based scheme to select critical value in order to improve finite sample performance. Through extensive simulations and a real-world application, we confirm our theoretical results and demonstrate that the proposed test exhibits desirable size and power in practice.
Comparing Experimental and Nonexperimental Methods: What Lessons Have We Learned Four Decades After LaLonde (1986)?
Authors: Guido Imbens, Yiqing Xu
Date: 2024-06-02 00:00:00
Tasks: valid

In 1986, Robert LaLonde published an article comparing nonexperimental estimates to experimental benchmarks (LaLonde 1986). He concluded that the nonexperimental methods at the time could not systematically replicate experimental benchmarks, casting doubt on their credibility. Following LaLonde's critical assessment, there have been significant methodological advances and practical changes, including (i) an emphasis on the unconfoundedness assumption separated from functional form considerations, (ii) a focus on the importance of overlap in covariate distributions, (iii) the introduction of propensity score-based methods leading to doubly robust estimators, (iv) methods for estimating and exploiting treatment effect heterogeneity, and (v) a greater emphasis on validation exercises to bolster research credibility. To demonstrate the practical lessons from these advances, we reexamine the LaLonde data. We show that modern methods, when applied in contexts with sufficient covariate overlap, yield robust estimates for the adjusted differences between the treatment and control groups. However, this does not imply that these estimates are causally interpretable. To assess their credibility, validation exercises (such as placebo tests) are essential, whereas goodness-of-fit tests alone are inadequate. Our findings highlight the importance of closely examining the assignment process, carefully inspecting overlap, and conducting validation exercises when analyzing causal effects with nonexperimental data.
Cluster-robust jackknife and bootstrap inference for logistic regression models
Authors: James G. MacKinnon, Morten Ørregaard Nielsen, Matthew D. Webb
Date: 2024-06-02 00:00:00
Tasks: regression

We study cluster-robust inference for logistic regression (logit) models. Inference based on the most commonly-used cluster-robust variance matrix estimator (CRVE) can be very unreliable. We study several alternatives. Conceptually the simplest of these, but also the most computationally demanding, involves jackknifing at the cluster level. We also propose a linearized version of the cluster-jackknife variance matrix estimator as well as linearized versions of the wild cluster bootstrap. The linearizations are based on empirical scores and are computationally efficient. Our results can readily be generalized to other binary response models. We also discuss a new Stata software package called logitjack which implements these procedures. Simulation results strongly favor the new methods, and two empirical examples suggest that it can be important to use them in practice.
Financial Deepening and Economic Growth in Select Emerging Markets with Currency Board Systems: Theory and Evidence
Authors: Yujuan Qiu
Date: 2024-06-01 00:00:00

This paper investigates some indicators of financial development in select countries with currency board systems and raises some questions about the connection between financial development and growth in currency board systems. Most of those cases are long past episodes of what we would now call emerging markets. However, the paper also looks at Hong Kong, the currency board system that is one of the world's largest and most advanced financial markets. The global financial crisis of 2008 09 created doubts about the efficiency of financial markets in advanced economies, including in Hong Kong, and unsettled the previous consensus that a large financial sector would be more stable than a smaller one.
Optimizing hydrogen and e-methanol production through Power-to-X integration in biogas plants
Authors: Alberto Alamia, Behzad Partoon, Eoghan Rattigan, Gorm Brunn Andresen
Date: 2024-06-01 00:00:00

The European Union strategy for net zero emissions relies on developing hydrogen and electro fuels infrastructure. These fuels will be crucial as energy carriers and balancing agents for renewable energy variability. Large scale production requires more renewable capacity, and various Power to X (PtX) concepts are emerging in renewable rich countries. However, sourcing renewable carbon to scale carbon based electro fuels is a significant challenge. This study explores a PtX hub that sources renewable CO2 from biogas plants, integrating renewable energy, hydrogen production, and methanol synthesis on site. This concept creates an internal market for energy and materials, interfacing with the external energy system. The size and operation of the PtX hub were optimized, considering integration with local energy systems and a potential hydrogen grid. The levelized costs of hydrogen and methanol were estimated for a 2030 start, considering new legislation on renewable fuels of non biological origin (RFNBOs). Our results show the PtX hub can rely mainly on on site renewable energy, selling excess electricity to the grid. A local hydrogen grid connection improves operations, and the behind the meter market lowers energy prices, buffering against market variability. We found methanol costs could be below 650 euros per ton and hydrogen production costs below 3 euros per kg, with standalone methanol plants costing 23 per cent more. The CO2 recovery to methanol production ratio is crucial, with over 90 per cent recovery requiring significant investment in CO2 and H2 storage. Overall, our findings support planning PtX infrastructures integrated with the agricultural sector as a cost effective way to access renewable carbon.
Censorship in Democracy
Authors: Marcel Caesmann, Janis Goldzycher, Matteo Grigoletto, Lorenz Gschwent
Date: 2024-06-05 00:00:00
Tasks: Misinformation

The spread of propaganda, misinformation, and biased narratives from autocratic regimes, especially on social media, is a growing concern in many democracies. Can censorship be an effective tool to curb the spread of such slanted narratives? In this paper, we study the European Union's ban on Russian state-led news outlets after the 2022 Russian invasion of Ukraine. We analyze 775,616 tweets from 133,276 users on Twitter/X, employing a difference-in-differences strategy. We show that the ban reduced pro-Russian slant among users who had previously directly interacted with banned outlets. The impact is most pronounced among users with the highest pre-ban slant levels. However, this effect was short-lived, with slant returning to its pre-ban levels within two weeks post-enforcement. Additionally, we find a detectable albeit less pronounced indirect effect on users who had not directly interacted with the outlets before the ban. We provide evidence that other suppliers of propaganda may have actively sought to mitigate the ban's influence by intensifying their activity, effectively counteracting the persistence and reach of the ban.
The test of investors' behavioral bias through the price discovery process in cryptoasset exchange" Transactional-level evidence from Thailand
Authors: Roongkiat Ratanabanchuen, Kanis Saengchote, Voraprapa Nakavachara, Thitiphong Amonthumniyom, Pongsathon Parinyavuttichai, Polpatt Vinaibodee
Date: 2024-06-05 00:00:00

Analyzing investors' trading behavior in cryptoasset markets provides new evidence supporting the theory that retail investors likely exhibit behavioral biases. We investigate the price discovery process between Thailand's most highly liquid exchange and the global exchange. Under the no-arbitrage assumption, bid-offer quotes in the local exchange should quickly move to match the new price levels in the global exchange, as the price process of cryptoassets in the local exchange does not contain new information that can lead the price dynamic in the global exchange. We analyze intraday bid-offer quotes and investors' portfolio positions and find that investors exhibit the disposition effect by attempting to sell their profitable positions during market upturns. The rate of bid-offer movement is significantly slow to match a new global price level only in situations when most investors in the market are in profit. These insights are crucial as they suggest that the risk-return characteristics of asset prices between the bull and bear market may differ, resulting from investors' behavioral biases.
Distrust of social media influencers in America
Authors: Shawn Berry
Date: 2024-06-04 00:00:00

The popularity of social media influencers (SMI) as a means for businesses and causes to engage with the public and develop followers is undeniable. However, the use of SMI have been scrutinized due to various scandals that reflect poorly on brands and firms. Consequently, the distrust of SMI can create the potential for damage to a brand if audiences are not receptive to communications and messaging. This study (n=351) shares insights and findings of the apparent distrust of SMI that were discovered during the data analysis phase of my dissertation (Berry, 2024a). The study examines levels of trust and distrust toward SMI in the United States according to various demographic characteristics of respondents, specifically, age, gender, income level, education level, and region of the United States. Chi square analysis of the variables and a predictive model of trust of SMI are presented. Finally, recommendations and suggestions for future research are discussed.
Do Cure Violence Programs Reduce Gun Violence? Evidence from New York City
Authors: Rachel Avram, Eric J. Koepcke, Alaa Moussawi, Melissa Nuñez
Date: 2024-06-04 00:00:00
Tasks: counterfactual

Cure Violence is a community violence intervention program that aims to reduce gun violence by mediating conflicts, "treating" high-risk individuals, and changing community norms. Using NYC shootings data from 2006-2023, we assess the efficacy of Cure Violence using both difference-in-differences and event study models. We find that, on average, Cure Violence is associated with a 14% reduction in shootings relative to the counterfactual. This association persists in the years after treatment, neither increasing nor decreasing much over time. We exploit variation in the geography and timing of Cure implementation to argue against alternative explanations. Additionally, we find suggestive evidence of spillover effects into nearby precincts, as well as increasing returns to opening new Cure programs. Interpreted causally, our results imply that around 1,300 shootings were avoided between 2012-2023 due to Cure -- generating a net social surplus of $2.45 billion and achieving a benefit-cost ratio of 6.5:1.
Algorithmic Collusion in Dynamic Pricing with Deep Reinforcement Learning
Authors: Shidi Deng, Maximilian Schiffer, Martin Bichler
Date: 2024-06-04 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning

Nowadays, a significant share of the Business-to-Consumer sector is based on online platforms like Amazon and Alibaba and uses Artificial Intelligence for pricing strategies. This has sparked debate on whether pricing algorithms may tacitly collude to set supra-competitive prices without being explicitly designed to do so. Our study addresses these concerns by examining the risk of collusion when Reinforcement Learning algorithms are used to decide on pricing strategies in competitive markets. Prior research in this field focused on Tabular Q-learning (TQL) and led to opposing views on whether learning-based algorithms can lead to supra-competitive prices. Our work contributes to this ongoing discussion by providing a more nuanced numerical study that goes beyond TQL by additionally capturing off- and on-policy Deep Reinforcement Learning (DRL) algorithms. We study multiple Bertrand oligopoly variants and show that algorithmic collusion depends on the algorithm used. In our experiments, TQL exhibits higher collusion and price dispersion phenomena compared to DRL algorithms. We show that the severity of collusion depends not only on the algorithm used but also on the characteristics of the market environment. We further find that Proximal Policy Optimization appears to be less sensitive to collusive outcomes compared to other state-of-the-art DRL algorithms.