This paper characterizes DSGE models as fixed-point selection devices for self-referential economic specifications. We formalize this structure as $(S, T, Π)$: specification, self-referential operator, and equilibrium selector. The framework applies to any DSGE model through compositional pipelines where specifications are transformed, fixed points computed, and equilibria selected. We provide formal results and computational implementation for linear rational-expectations systems, reinterpreting Blanchard-Kahn conditions as a specific selection operator and verifying that standard solution methods (such as QZ decomposition and OccBin) realize this operation. We show that alternative selectors (minimal-variance, fiscal anchoring) become available under indeterminacy, revealing selection as a policy choice rather than a mathematical necessity. Our framework reveals the formal structure underlying DSGE solution methods, enabling programmatic verification and systematic comparison of selection rules.

Empirical evidence suggests that there is little to no correlation between the rate of inflation and the size of price change. Economists have hitherto taken this to mean that monetary shocks do not generate much deviation in relative prices and therefore inflation does not hurt the economy by impeding the workings of the price system. This paper presents a production network model of inflationary dynamics in which it is well possible for inflation to have near-zero correlation with the size of price change yet cause significant distortion of relative prices. The relative price distortion caused by inflation critically depends on the spectral gap, degree distribution, and assortativity of the production network.

We study how product specialization choices affect supply chain resilience. We propose a theory of supply chain formation in which only compatible inputs can be used in final production. Intermediate producers choose how much to specialize their goods, trading off higher value added against a smaller pool of compatible final producers. Final producers operate complex supply chains, requiring multiple complementary inputs. Specialization choices determine how quickly final producers can replace suppliers after disruptions, and thus supply chain resilience. In equilibrium, production inputs are over-specialized due to a novel network externality. Intermediate producers fail to internalize how their specialization choices affect the likelihood that final producers source all required inputs, and therefore the lost value added from complementary inputs if production halts. As a result, supply chains are more productive in normal times but less resilient than socially desirable. We characterize the optimal transfer that restores the efficient allocation and show that non-fiscal interventions, such as compatibility standards, are generally welfare-enhancing.

Motivated by Tweedie's formula for the Compound Decision problem, we examine the theoretical foundations of empirical Bayes estimators that directly model the marginal density $m(y)$. Our main result shows that polynomial log-marginals of degree $k \ge 3 $ cannot arise from any valid prior distribution in exponential family models, while quadratic forms correspond exactly to Gaussian priors. This provides theoretical justification for why certain empirical Bayes decision rules, while practically useful, do not correspond to any formal Bayes procedures. We also strengthen the diagnostic by showing that a marginal is a Gaussian convolution only if it extends to a bounded solution of the heat equation in a neighborhood of the smoothing parameter, beyond the convexity of $c(y)=\tfrac12 y^2+\log m(y)$.

While lobbying has been demonstrated to have an important effect on public opinion and policy making, existing models of opinion formation do not specifically include its effect. In this work we introduce a new model of lobbying-driven opinion influence within opinion dynamics, where lobbyists can implement complex strategies and are characterised by a finite budget. Individuals update their opinions through a learning process resembling Bayes-rule updating but using signals generated by the other agents (a form of social learning), modulated by under-reaction and confirmation bias. We study the model numerically and demonstrate rich dynamics both with and without lobbyists. In the presence of lobbying, we observe two regimes: one in which lobbyists can have full influence on the agent network, and another where the peer-effect generates polarisation. When lobbyists are symmetric, the lobbyist-influence regime is characterised by prolonged opinion oscillations. If lobbyists temporally differentiate their strategies, frontloading is advantageous in the peer-effect regime, whereas backloading is advantageous in the lobbyist-influence regime. These rich dynamics pave the way for studying real lobbying strategies to validate the model in practice.

This paper discusses three key themes in forecasting for monetary policy highlighted in the Bernanke (2024) review: the challenges in economic forecasting, the conditional nature of central bank forecasts, and the importance of forecast evaluation. In addition, a formal evaluation of the Bank of England's inflation forecasts indicates that, despite the large forecast errors in recent years, they were still accurate relative to common benchmarks.

This paper provides a formal econometric framework behind the newly developed difference-in-discontinuities design (DiDC). Despite its increasing use in applied research, there are currently limited studies of its properties. We formalize the theory behind the difference-in-discontinuity approach by stating the identification assumptions, proposing a nonparametric estimator, and deriving its asymptotic properties. We also provide comprehensive tests for one of the identification assumption of the DiDC and sensitivity analysis methods that allow researchers to evaluate the robustness of DiDC estimates under violations of the identifying assumptions. Monte Carlo simulation studies show that the estimators have desirable finite-sample properties. Finally, we revisit Grembi et al. (2016), which studies the effects of relaxing fiscal rules on public finance outcomes. Our results show that most of the qualitative takeaways of the original work are robust to time-varying confounding effects.

Cloud computing has revolutionized the way organizations manage their IT infrastructure, but it has also introduced new challenges, such as managing cloud costs. The rapid adoption of artificial intelligence (AI) and machine learning (ML) workloads has further amplified these challenges, with GPU compute now representing 40-60\% of technical budgets for AI-focused organizations. This paper provides a comprehensive review of cloud and AI infrastructure cost optimization techniques, covering traditional cloud pricing models, resource allocation strategies, and emerging approaches for managing AI/ML workloads. We examine the dramatic cost reductions in large language model (LLM) inference which has decreased by approximately 10x annually since 2021 and explore techniques such as model quantization, GPU instance selection, and inference optimization. Real-world case studies from Amazon Prime Video, Pinterest, Cloudflare, and Netflix showcase practical application of these techniques. Our analysis reveals that organizations can achieve 50-90% cost savings through strategic optimization approaches. Future research directions in automated optimization, sustainability, and AI-specific cost management are proposed to advance the state of the art in this rapidly evolving field.

We develop empirical models that efficiently process large amounts of unstructured product data (text, images, prices, quantities) to produce accurate hedonic price estimates and derived indices. To achieve this, we generate abstract product attributes (or ``features'') from descriptions and images using deep neural networks. These attributes are then used to estimate the hedonic price function. To demonstrate the effectiveness of this approach, we apply the models to Amazon's data for first-party apparel sales, and estimate hedonic prices. The resulting models have a very high out-of-sample predictive accuracy, with $R^2$ ranging from $80\%$ to $90\%$. Finally, we construct the AI-based hedonic Fisher price index, chained at the year-over-year frequency, and contrast it with the CPI and other electronic indices.

In this paper, we develop a penalized realized variance (PRV) estimator of the quadratic variation (QV) of a high-dimensional continuous Itô semimartingale. We adapt the principle idea of regularization from linear regression to covariance estimation in a continuous-time high-frequency setting. We show that under a nuclear norm penalization, the PRV is computed by soft-thresholding the eigenvalues of realized variance (RV). It therefore encourages sparsity of singular values or, equivalently, low rank of the solution. We prove our estimator is minimax optimal up to a logarithmic factor. We derive a concentration inequality, which reveals that the rank of PRV is -- with a high probability -- the number of non-negligible eigenvalues of the QV. Moreover, we also provide the associated non-asymptotic analysis for the spot variance. We suggest an intuitive data-driven subsampling procedure to select the shrinkage parameter. Our theory is supplemented by a simulation study and an empirical application. The PRV detects about three-five factors in the equity market, with a notable rank decrease during times of distress in financial markets. This is consistent with most standard asset pricing models, where a limited amount of systematic factors driving the cross-section of stock returns are perturbed by idiosyncratic errors, rendering the QV -- and also RV -- of full rank.

Multivalued treatments are commonplace in applications. We explore the use of discrete-valued instruments to control for selection bias in this setting. Our discussion revolves around the concept of targeting: which instruments target which treatments. It allows us to establish conditions under which counterfactual averages and treatment effects are point- or partially-identified for composite complier groups. We explore the additional identifying power of a positive selection assumption. We illustrate its usefulness by revisiting the findings of Kline and Walters (2016) on the Head Start Impact Study. We derive informative bounds that suggest less beneficial effects of Head Start expansions than their parametric estimates.

This paper develops a design-first econometric framework for event-study and difference-in-differences estimands under staggered adoption with heterogeneous effects, emphasising (i) exact probability limits for conventional two-way fixed effects event-study regressions, (ii) computable design diagnostics that quantify contamination and negative-weight risk, and (iii) sensitivity-robust inference that remains uniformly valid under restricted violations of parallel trends. The approach is accompanied by orthogonal score constructions that reduce bias from high-dimensional nuisance estimation when conditioning on covariates. Theoretical results and Monte Carlo experiments jointly deliver a self-contained methodology paper suitable for finance and econometrics applications where timing variation is intrinsic to policy, regulation, and market-structure changes.

Diseases originating from wildlife pose a significant threat to global health, causing human and economic losses each year. The transmission of disease from animals to humans occurs at the interface between humans, livestock, and wildlife reservoirs, influenced by abiotic factors and ecological mechanisms. Although evidence suggests that intact ecosystems can reduce transmission, disease prevention has largely been neglected in conservation efforts and remains underfunded compared to mitigation. A major constraint is the lack of reliable, spatially explicit information to guide efforts effectively. Given the increasing rate of new disease emergence, accelerated by climate change and biodiversity loss, identifying priority areas for mitigating the risk of disease transmission is more crucial than ever. We present new high-resolution (1 km) maps of priority areas for targeted ecological countermeasures aimed at reducing the likelihood of zoonotic spillover, along with a methodology adaptable to local contexts. Our study compiles data on well-documented risk factors, protection status, forest restoration potential, and opportunity cost of the land to map areas with high potential for cost-effective interventions. We identify low-cost priority areas across 50 countries, including 277,000 km2 where environmental restoration could mitigate the risk of zoonotic spillover and 198,000 km2 where preventing deforestation could do the same, 95% of which are not currently under protection. The resulting layers, covering tropical regions globally, are freely available alongside an interactive no-code platform that allows users to adjust parameters and identify priority areas at multiple scales. Ecological countermeasures can be a cost-effective strategy for reducing the emergence of new pathogens; however, our study highlights the extent to which current conservation efforts fall short of this goal.