In this paper, we propose a triple (or double-debiased) Lasso estimator for inference on a low-dimensional parameter in high-dimensional linear regression models. The estimator is based on a moment function that satisfies not only first- but also second-order Neyman orthogonality conditions, thereby eliminating both the leading bias and the second-order bias induced by regularization. We derive an asymptotic linear representation for the proposed estimator and show that its remainder terms are never larger and are often smaller in order than those in the corresponding asymptotic linear representation for the standard double Lasso estimator. Because of this improvement, the triple Lasso estimator often yields more accurate finite-sample inference and confidence intervals with better coverage. Monte Carlo simulations confirm these gains. In addition, we provide a general recursive formula for constructing higher-order Neyman orthogonal moment functions in Z-estimation problems, which underlies the proposed estimator as a special case.

This paper studies how platform design shapes strategic behavior in decentralized electricity trading. We develop a finite-horizon dynamic game in which photovoltaic- and battery-equipped players ("prosumers") trade on a platform that maps aggregate imports and exports into internal buy and sell prices. We establish existence of a perfect conditional epsilon-equilibrium and characterize a Cournot-like market-power mechanism in an observable-types benchmark of the game: because the producer price is decreasing in aggregate exports, strategic prosumers withhold supply and underutilize storage relative to the price-taking benchmark. To quantify these effects, we use a multi-agent computational framework that exploits the differentiable structure of the platform's clearing rule to compare planner, price-taking, and strategic outcomes under alternative pricing mechanisms. In our baseline calibration, strategic play raises grid settlement cost by about 6 percent relative to price-taking. The magnitude of the distortion depends strongly on platform design: some designs can largely eliminate strategic incentives, while increased competition in storage ownership sharply reduces withholding, with most of the distortion disappearing once storage is split across more than three owners. We also find that information disclosure can improve competitive coordination but also increase the market power effects. Despite these distortions, the platform remains highly valuable overall, reducing a passive consumer's annual electricity bill by roughly 40 percent relative to exclusive grid settlement, with strategic behavior clawing back only about 8 percent of that saving. The results show that pricing rules, information disclosure, and ownership structure determine how much of the gains from decentralized electricity trading are realized.

AI coding agents make empirical specification search fast and cheap, but they also widen hidden researcher degrees of freedom. Building on an open-source agent-loop architecture, this paper adapts that framework to an empirical economics workflow and adds a post-search holdout evaluation. In a forecast-combination illustration, multiple independent agent runs outperform standard benchmarks in the original rolling evaluation, but not all continue to do so on a post-search holdout. Logged search and holdout evaluation together make adaptive specification search more transparent and help distinguish robust improvements from sample-specific discoveries.

We generalize the seminal framework of Kyle (1985) to a many-asset setting, bridging the gap between informed-trading theory and modern trading practices. Specifically, we formulate an infinite-dimensional Bayesian trading game in which the informed trader's private information may concern arbitrary aspects of the cross-sectional payoff structure across a continuum of traded assets. In this general setting, we obtain a parsimonious equilibrium characterized by a single scalar fixed point, which yields closed-form characterizations of equilibrium trading strategy, price impact within and across markets, and the information efficiency of equilibrium prices.

This paper introduces a version of decoupling and randomization to establish concentration inequalities for double-indexed permutation statistics. The results yield, among other applications, a new combinatorial Hanson-Wright inequality and a new combinatorial Bennett inequality. Several illustrative examples from rank-based statistics, graph-based statistics, and causal inference are also provided.

Many resources are provided by an ecological system that is vulnerable to tipping when exceeding a certain level of pollution, with a sudden big loss of ecosystem services. An ecological system is usually also a common-pool resource and therefore vulnerable to suboptimal use resulting from non-cooperative behavior. An analysis requires methods to derive cooperative and non-cooperative solutions for managing a dynamical system with tipping points. Such a game is a differential game which has two well-defined non-cooperative solutions, the open-loop and feedback Nash equilibria. This paper provides new numerical methods for deriving open-loop and feedback Nash equilibria, for one-dimensional and two-dimensional dynamical systems. The methods are applied to the lake game, which is the classical example for these types of problems. Especially, two-dimensional feedback Nash equilibria are a novelty of this paper. This Nash equilibrium is close to the cooperative solution which has important policy implications.

We study price discovery in a model where an informed agent has arbitrary private information about state probabilities and trades state-contingent claims. The model unifies the key elements of Arrow-Debreu (1954) and Kyle (1985). When the claims are options, the informed agent has arbitrary information about the underlying asset's payoff distribution and trades option portfolios. Our setting provides the first equilibrium framework that encompasses longs-tanding option-market practices and regularities, including common trading strategies and the volatility smile across strikes.

The Federal Trade Commission has recently filed an administrative complaint against the Big 3 pharmacy benefit managers claiming they engaged in unfair conduct in violation of Section 5 of the FTC Act. They never used the word collusion in the complaint and chose not to sue under The Sherman Act, Section 1. We view this as a novel case of market design collusion rather than a case of price collusion. The Big 3 PBMs are conceptualized as auctioneers soliciting rebate bids off unit list prices in exchange for favored positions on formularies. We will show how the fairness standard of the FTC Act can be made operational by judging fairness against economic theories of good auction design. Discovery is focused on finding explicit communication among the Big 3 PBMs in 2012 to change the so-called winner s determination equation of this auction, adding high gross rebates as a basis for formulary position assignments. On the other hand, we will argue that a case based on a bevy of anecdotes comparing only net unit prices will fail due to complexities in the winners determination equation.

This paper evaluates the effect of a short, interactive popularisation talk on upper-secondary students' interest in Economics. This contrasts with previous research, which has primarily examined impersonal interventions to boost interest in Economics. The intervention presents Economics as an empirical social science engaged with real-world social problems. Using a cluster-randomised field experiment conducted during secondary-school campus visits in Spain, we find no statistically significant average effect on stated interest in studying Economics. However, the intervention generates substantial heterogeneity: those with stronger altruistic preferences become significantly more likely to express interest after the talk. These findings suggest that informational outreach may shape who perceives the discipline as aligned with their motivations, even if it does not substantially increase overall interest. More broadly, they indicate that presenting Economics as empirical and socially relevant may broaden the profile of those who consider the field.

Generative AI has transformed the economics of information production, making explanations, proofs, examples, and analyses available at very low cost. Yet the value of information still depends on whether downstream users can absorb and act on it. A signal conveys meaning only to a learner with the structural capacity to decode it: an explanation that clarifies a concept for one user may be indistinguishable from noise to another who lacks the relevant prerequisites. This paper develops a mathematical model of that learner-side bottleneck. We model the learner as a mind, an abstract learning system characterized by a prerequisite structure over concepts. A mind may represent a human learner, an artificial learner such as a neural network, or any agent whose ability to interpret signals depends on previously acquired concepts. Teaching is modeled as sequential communication with a latent target. Because instructional signals are usable only when the learner has acquired the prerequisites needed to parse them, the effective communication channel depends on the learner's current state of knowledge and becomes more informative as learning progresses. The model yields two limits on the speed of learning and adoption: a structural limit determined by prerequisite reachability and an epistemic limit determined by uncertainty about the target. The framework implies threshold effects in training and capability acquisition. When the teaching horizon lies below the prerequisite depth of the target, additional instruction cannot produce successful completion of teaching; once that depth is reached, completion becomes feasible. Across heterogeneous learners, a common broadcast curriculum can be slower than personalized instruction by a factor linear in the number of learner types.

Discrete Choice Experiments (DCEs) are widely used to elicit preferences for products or services by analyzing choices among alternatives described by their attributes. The quality of the insights obtained from a DCE heavily depends on the properties of its experimental design. While early DCEs often relied on linear criteria such as orthogonality, these approaches were later found to be inappropriate for discrete choice models, which are inherently non-linear. As a result, statistically efficient design methods, based on minimizing the D-error to reduce parameter variance, have become the standard. Although such methods are implemented in several commercial tools, researchers seeking free and accessible solutions often face limitations. This paper presents DCEtool, an R package with a Shiny-based graphical interface designed to support both novice and experienced users in constructing, decoding, and analyzing statistically efficient DCE designs. DCEtool facilitates the implementation of serial DCEs, offers flexible design settings, and enables rapid estimation of discrete choice models. By making advanced design techniques more accessible, DCEtool contributes to the broader adoption of rigorous experimental practices in choice modelling.

In observational causal inference, domain knowledge often leaves multiple covariate adjustments plausible, yet which sets satisfy ignorability is untestable. Different adjustment sets can yield conflicting estimates of the average treatment effect, and standard remedies (adjusting for their union or intersection, or reporting the union or convex hull of confidence intervals) can fail or produce intervals whose width does not vanish with sample size. We propose a specification-robust procedure that returns a single point estimate and a confidence interval that is valid as long as at least one candidate adjustment set is valid and has width shrinking at the parametric $n^{-1/2}$ rate. Our approach mirrors how trimming and overlap weighting handle overlap violations:~We shift the target to a reweighted population, closest in KL-divergence to the original population, for which credible, specification-robust inference is feasible. We also provide diagnostic plots to assess the population shift and an extension to protect any function of the covariates used for reweighting, similar to calipers in matching. Synthetic and real-data examples demonstrate that our procedure provides substantially tighter confidence intervals than the convex hull while maintaining nominal coverage.

A fast-paced policy context is characteristic of energy and climate research, which strives to develop solutions to wicked problems such as climate change. Funding agencies in the European Union recognize the importance of linking research and policy in climate and energy research. This calls for an increased understanding of how stakeholder engagement can effectively be used to co-design research questions that include stakeholders' concerns. This paper reviews the current literature on stakeholder engagement, from which we create a set of criteria. These are used to critically assess recent and relevant papers on stakeholder engagement in climate and energy projects. We obtained the papers from a scoping review of stakeholder engagement through workshops in EU climate and energy research. With insights from the literature and current EU climate and energy projects, we developed a workshop programme for stakeholder engagement. This programme was applied to the European Climate and Energy Modelling Forum project, aiming to co-design the most pressing and urgent research questions according to European stakeholders. The outcomes include 82 co-designed and ranked research questions for nine specific climate and energy research themes. Findings from the scoping review indicate that papers rarely define the term 'stakeholder'. Additionally, the concepts of co-creation, co-design, and co-production are used interchangeably and often without definition. We propose that workshop planners use stakeholder identification and selection methods from the broader stakeholder engagement literature.