We study how idiosyncratic firm-level shocks generate aggregate volatility and tail risk when they propagate through a production network under overlapping adjustment: new productivity draws arrive before the economy reaches the static equilibrium associated with earlier draws. Each innovation generates a `productivity wave' that mixes and dissipates over time as it travels through the production network. Macroeconomic fluctuations emerge from the interference between these waves of different vintages. The interference between these waves is governed by the dominant transient eigenvalue of the production network, and therefore so is the macroeconomic fluctuations they generate. In such a dynamic regime, the tail of the degree distribution is a markedly weaker determinant of macro fluctuations than in the fully adjusted static benchmark. And the macroeconomic significance of the degree-heterogeneity of production networks cannot be known without knowing the rate at which the economy converges to equilibrium or equivalently the spectral properties of the production network. More concretely, once we permit the time-averaging of shocks, granular shocks may account for only a small fraction of the empirically observed aggregate volatility.

Backtesting risk measures is a central task in financial regulation. While standard backtests evaluate whether a forecasting model is statistically consistent with observed losses, regulatory practice often requires assessing the performance of an internal model relative to benchmark models. We develop a non-parametric sequential framework for comparative backtests of general elicitable risk measures using e-values and e-processes. The proposed methods provide anytime-valid inference and remain robust under dependence and model misspecification. In particular, we propose a modified three-zone approach based on weak dominance, which yields more informative conclusions in comparative backtesting. As a technical building block, we also construct general standard e-backtests for identifiable risk measures and characterize the associated e-values and e-processes. The resulting procedures apply to a broad class of commonly used risk measures, including the mean, variance, Value-at-Risk, Expected Shortfall, and expectiles. Simulation studies and empirical analyses illustrate the effectiveness of the proposed approach.

A fundamental problem with nonlinear models is that maximum likelihood estimates are not guaranteed to exist. Though nonexistence is a well known problem in the binary response model literature, it presents significant challenges for other models and is not as well understood in more general settings. These challenges are only magnified for models that feature many fixed effects and other high-dimensional parameters. We address the current ambiguity surrounding this topic by studying the conditions that govern the existence of estimates for (pseudo-)maximum likelihood estimators used to estimate a wide class of generalized linear models (GLMs). We show that some, but not all, of these GLM estimators can still deliver consistent estimates of at least some of the linear parameters when these conditions fail to hold. We also demonstrate how to verify these conditions in models with high-dimensional parameters, such as panel data models with multiple levels of fixed effects.

This paper analyses how firms' skill development strategies affect their propensity to introduce innovation. We develop an adjustment-cost framework that links human capital theory and institutionalist and evolutionary approaches, considering innovation as an activity that entails costs in labour adjustment arising either from the training activities of workers or the recruitment of skilled employees. Using a two-wave panel of Italian manufacturing firms observed in 2017-2018 and 2019-2020, we analyse firms' adoption of total, product, process, and circular innovation as a function of internal training practices and of external skills acquisition. Overall, the empirical analysis confirms the expected positive relationship between training and innovation, while also revealing important nuances in the workforce upskilling strategies required for different types of innovation. Moreover, while training activities and skills development are essential across all forms of innovation, our findings indicate that internal training is particularly effective in supporting the implementation of circular innovations. By contrast, external recruitment appears to be consistently necessary whenever innovations are introduced, regardless of their type.

This paper studies patenting trends in artificial intelligence (AI) and robotics from 1980 to 2019. We introduce a novel distinction between traditional robotics and robotics embedding AI functionalities. Using patent data and a time-series econometric approach, we examine whether these domains share common long-run dynamics and how their trajectories differ across major innovation systems. Three main findings emerge. First, patenting activity in core AI, traditional robots, and AI-enhanced robots follows distinct trajectories, with AI-enhanced robotics accelerating sharply from the early 2010s. Second, structural breaks occur predominantly after 2010, indicating an acceleration in the technological dynamics associated with AI diffusion. Third, long-run relationships between AI and robotics vary systematically across countries: China exhibits strong integration between core AI and AI-enhanced robots, alongside a substantial contribution from universities and the public sector, whereas the United States displays a more market-oriented patenting structure and weaker integration between AI and robots. Europe, Japan, and South Korea show intermediate patterns.

Structural break identification methods are an important tool for evaluating the effectiveness of climate change mitigation policies. In this paper, we introduce a unified probabilistic framework for detecting structural breaks with unknown timing and arbitrary sequence in longitudinal data. The proposed Bayesian setup uses indicator-saturated regression and a spike-and-slab prior with an inverse-moment density as the slab component to ensure model selection consistency. Simulation results show that the method outperforms comparable frequentist approaches, particularly in environments with a high probability of structural breaks. We apply the framework to identify and evaluate the effects of climate policies in the European road transport sector.

Artificial intelligence is undergoing a structural transformation marked by the rise of agentic systems capable of open-ended action trajectories, generative representations and outputs, and evolving objectives. These properties introduce structural uncertainty into human-AI teaming (HAT), including uncertainty about behavior trajectories, epistemic grounding, and the stability of governing logics over time. Under such conditions, alignment cannot be secured through agreement on bounded outputs; it must be continuously sustained as plans unfold and priorities shift. We advance Team Situation Awareness (Team SA) theory, grounded in shared perception, comprehension, and projection, as an integrative anchor for this transition. While Team SA remains analytically foundational, its stabilizing logic presumes that shared awareness, once achieved, will support coordinated action through iterative updating. Agentic AI challenges this presumption. Our argument unfolds in two stages: first, we extend Team SA to reconceptualize both human and AI awareness under open-ended agency, including the sensemaking of projection congruence across heterogeneous systems. Second, we interrogate whether the dynamic processes traditionally assumed to stabilize teaming in relational interaction, cognitive learning, and coordination and control continue to function under adaptive autonomy. By distinguishing continuity from tension, we clarify where foundational insights hold and where structural uncertainty introduces strain, and articulate a forward-looking research agenda for HAT. The central challenge of HAT is not whether humans and AI can agree in the moment, but whether they can remain aligned as futures are continuously generated, revised, enacted, and governed over time.

The learning crisis in the Latin American region (i.e., higher rates of students not reaching basic competencies at secondary level) is worrying, particularly post-pandemic given the stronger role of inequality behind achievement. Within this scenario, the concept of student academic resilience (SAR), students who despite coming from disadvantaged backgrounds reach good performance levels, and an analysis of its determinants, are policy relevant. In this paper, using advancements on explainable machine learning methods (the SHAP method) and relying on PISA 2022 data for 9 countries from the region, we identify leading factors behind SAR using diverse indicators. We find that household inputs (books and digital devices), gender, homework, repetition and work intensity are leading factors for one indicator of academic resilience, whereas for other indicator leading drives fall into the school domain: school size, the ratio of PC connected to the internet, STR and teaching quality proxied by certified teachers and professional development rates and school type. Also, we find negative associations of SAR with the length of school closures and barriers for remote learning during the pandemic. The paper's findings adds to the scare regional literature contributing to future policy designs where key features behind SAR can be used to lift disadvantaged students from lower achievement groups towards being academic resilient.

We introduce the concept of temporal hierarchy forecasting (THieF) in predicting day-ahead electricity prices and show that reconciling forecasts for hourly products and 2- to 24-hour blocks can significantly (up to 13%) improve accuracy at all levels. These results remain consistent throughout a challenging 4-year test period (2021-2024) in the German and Spanish power markets and across model architectures, including linear regression, shallow feedforward neural networks, gradient-boosted decision trees, and a state-of-the-art, pretrained transformer. Given that (i) trading of block products is becoming more common and (ii) the computational cost of reconciliation is comparable to that of predicting hourly prices alone, we recommend using it in daily forecasting practice.

We investigate whether and why people might adjust compensation for workers who use AI tools. Across 13 studies (N = 4,956), participants consistently lowered compensation for workers who used AI compared to those who did not. This "AI penalty" is robust across different work scenarios and work tasks, worker statuses, forms and timing of compensation, methods of eliciting compensation, and perceptions of output quality. Moreover, the effect emerges in both hypothetical compensation scenarios as well as real monetary compensation of gig workers. We find that perceived effort and perceived agency -- the degree to which an individual serves as the originating source of the core intellectual or creative contribution in a task -- explain decisions to reduce compensation for AI-users. However, the penalty is not inevitable. Workers who strategically retain creative agency over core tasks recover most of the AI penalty, and employment contracts that make compensation reductions impermissible provide structural means of reducing the AI penalty.

The United States leads the world in the number of mass shootings that occur each year, even as policy making on firearms remains polarized along party lines. In the face of increasing violence and public demand for policy action, we ask whether legislators change their voting behavior on firearm policy in the wake of mass shootings. We estimate the latent gun-policy positions of 14,585 state legislators across all 50 states using roll-call votes on firearm-related bills from 2011 to 2022. Employing a difference-in-differences design, we find that mass shootings occurring within a legislator's district do not, on average, measurably shift their positionality on firearm policy. This null effect is robust across analyses accounting for legislators' partisanship, their geographic proximity to the shooting, and characteristics of individual shootings. Our findings suggest that even acute, locally salient tragedies fail to cause changes in how legislators vote on firearm policy.

The aim of the research paper is to understand the sustainability challenges faced by resorts mainly luxury in Maldives and to implement the sustainable tourism practices. The Maldives economy is dependent mostly on the fishing, boat building, boat repairing and tourism. Over recent years there is a drastic change that has took place in Maldives in tourism industry. Maldives has progressed to be the upper middle-income country and luxury resorts are the reason for increased GDP in the country. Although there are some practices associated with the luxury resorts to follow in terms of environmental concerns. Present study focuses on the triple bottom line approach and the 12 major Sustainable Tourism Principles as a framework for sustainability practices and its implementation including the challenges associated in Maldives. The paper suggests some recommendations on several paradigm of enforcing laws and regulations, waste management facilities, fostering collaboration along with promoting local agriculture. The study also contemplates on several other areas such as on the impact of sustainability initiatives, coral restoration, and the use of sustainable supply chains. The intent of the current research is to suggest methods to promote the sustainable practices in luxury resort in Maldives.

This paper studies a linear model for multidimensional panel data of three or more dimensions with unobserved interactive fixed-effects. The main estimator uses a Neyman-orthogonal approach, and requires two preliminary steps. First, the model is embedded within a two-dimensional panel framework where factor model methods in Bai (2009) lead to consistent, but slowly converging, estimates. The second step develops a weighted-within transformation that is robust to multidimensional interactive fixed-effects and achieves the parametric rate of consistency. The estimator is shown to be asymptotically normal. The methods are implemented to estimate the demand elasticity for beer.

The theory of Monotone Comparative Statics (MCS) has traditionally required a lattice structure, excluding certain multidimensional environments such as mixed-strategy games where this property fails. We show that this structure is not essential. We introduce a weaker notion, the pseudo-lattice property, and preserve the theory's core results by generalizing the MCS theorems for individual choice and Tarski's fixed-point theorem. Our framework expands comparative statics to pseudo quasi-supermodular games. Crucially, it enables the first MCS analysis of mixed-strategy Nash equilibria and trembling-hand perfect equilibria.