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학술

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Bayesian State-Space Modeling and Model-Based Counterfactual Analysis of Dynamic Income Distributions from Grouped Data

arXiv:2605.18138v2 Announce Type: replace Abstract: Grouped income data contain only limited information about the evolution of income distributions over time. This paper develops a Bayesian state-space model for the generalized beta distribution of the second kind (GB2) to estimate dynamic income distributions using repeated grouped income data. By borrowing information across adjacent periods through the latent GB2 parameters, the proposed framework improves estimation precision relative to independent cross-sectional estimation. Building on the estimated latent-state dynamics, we further construct a model-based counterfactual framework that quantifies the contribution of demographic covariates while preserving the estimated evolution of the remaining model components. Using Japanese household income data from 1969--2007, we find that population aging and declining household size affect different parts of the income distribution through distinct channels, with population aging becoming an increasingly important driver of income inequality after around 2000. More generally, the proposed framework provides a unified Bayesian approach to dynamic distributional analysis and model-based counterfactual inference using repeated grouped income data.

arXiv Econ

Dynamic Cheap Talk without Feedback

arXiv:2604.26443v2 Announce Type: replace Abstract: We study a dynamic sender-receiver game in which the sender observes a state evolving according to a Markov chain but does not observe the receiver's action. Despite the absence of feedback, dynamic interaction partially restores commitment. We show that any equilibrium payoff of a persuasion model with partial commitment, where the sender can deviate to signaling policies that preserve the marginal distribution over messages, can be achieved as a uniform equilibrium payoff in the dynamic game. Moreover, any convex combination of such payoffs across message distributions can also be sustained. When the sender's payoff is state-independent, she achieves the Bayesian persuasion payoff.

arXiv Econ

A Nonlinear Target-Factor Model with Attention Mechanism for Mixed-Frequency Data

arXiv:2601.16274v2 Announce Type: replace Abstract: We propose the Mixed-Panels-Transformer Encoder (MPTE), a framework for estimating factor models in panels with mixed frequencies and nonlinear signals. Classical factor models rely on linear signal extraction and homogeneous sampling frequencies, limiting their use when variables arrive at different frequencies. MPTE instead uses Transformer-style attention to construct context-aware signals, replacing fixed linear combinations with adaptive reweighting. We extend principal component analysis to accommodate general temporal and cross-sectional attention operators, so the model learns to aggregate information across frequencies without manual alignment. Under linear activations, we establish consistency and asymptotic normality of factor and loading estimators, show that the framework nests classical factor models as a special case, and obtain efficiency gains through transfer learning across auxiliary panels. A Transformer architecture handles the nonlinear case, which we assess through simulations and an empirical application. In simulations, MPTE outperforms linear benchmarks under nonlinear designs. On 13 quarterly U.S. macroeconomic targets drawn from 48 monthly and quarterly FRED series, it remains competitive with established benchmarks. By averaging learned attention across variables and time, we recover target-specific variable importance and lag relevance, and ablations quantify the contribution of each model component.

arXiv Econ

Agentic Economic Modeling

arXiv:2510.25743v3 Announce Type: replace Abstract: We introduce Agentic Economic Modeling (AEM), a framework that aligns synthetic LLM choices with small-sample human evidence for econometric inference. AEM first generates task-conditioned synthetic choices via LLMs, then learns a bias-correction mapping from task features and raw LLM choices to human-aligned choices, upon which standard econometric estimators perform inference to recover demand elasticities and treatment effects. We validate AEM in two experiments. In a large scale conjoint study, using only 10% of the original data to fit the correction model lowers the error of the demand-parameter estimates, while uncorrected LLM choices increase the errors. In a regional field experiment, a mixture model calibrated on 10% of geographic regions estimates a treatment effect of -65$\pm$10 bps on the hold-out regions, closely matching the full human experiment (-60$\pm$8 bps). These results demonstrate AEM's potential to improve RCT efficiency and represent a step toward LLM-based counterfactual generation.

arXiv Econ

The Large and Likely Inefficiency of Stable Matching Mechanisms

arXiv:2407.19831v5 Announce Type: replace Abstract: We prove that any stable matching mechanism suffers from systematic inefficiency of striking magnitude: in large random markets, any stable allocation is Pareto-inefficient with high probability, and almost all students can simultaneously improve their placements without harming anyone else. We establish this result by showing that the envy digraph generated by the student-proposing Deferred Acceptance mechanism contains a unique giant strongly connected component, implying that nearly all students are improvable via trading cycles. Finally, we show that every maximal cycle packing covers almost all students, revealing a surprising asymptotic equivalence among all efficient mechanisms that Pareto-dominate DA.

arXiv Econ

Shared Bidding Algorithms and Competition: Evidence from Electricity Markets

arXiv:2607.13002v1 Announce Type: new Abstract: Competing firms increasingly delegate pricing and bidding decisions to algorithms supplied by the same third-party providers. We study whether a shared algorithm leads competitors to internalise one another's profits, using data from the Australian National Electricity Market, where every battery's bids are observed at 5-minute frequency and can be linked to an identifiable autobidding provider. Bids constructed by the same provider co-move, and do so more strongly after a disclosure reform made the common scarcity state easier to observe: the same information that steers batteries towards efficient arbitrage also synchronises the bids of competitors who share a provider. To separate co-movement due to shared information from joint profit maximisation, we estimate each battery's dynamic value of stored energy and reclear the market under counterfactual bids. Owner-level profits cannot rationalise observed bidding: batteries forgo profitable dispatch where it would depress the prices earned by same-provider batteries owned by rival firms, and the estimated weight on those rivals' profits is close to one. We find evidence of this conduct only where a provider's share of near-margin battery capacity exceeds roughly 30%, corresponding to an installed share of roughly 20%. The identified conduct costs consumers an annualised $5.5 million on the current fleet, and it arises at the level of the algorithm provider rather than the asset owner, a layer that ownership-based concentration screens do not capture.

arXiv Econ

Bivariate Isotonic Regression by Dynamic Programming

arXiv:2607.12629v1 Announce Type: new Abstract: This article extends the dynamic programming framework introduced by (Rote, 2019) from the univariate to the bivariate isotonic problem, using an anti-diagonal traversal procedure. The proposed algorithm is applied to the well-known baseball data set that describes the association of salary with a collection of player properties, including the number of runs batted and hits. The new algorithm is relevant in the sense that dynamic programming has a wide range of applications in economics, such as the savings problem, economic growth, job search, business cycles, oligopoly equilibrium, recursive contracts, and forecasting.

arXiv Econ

Orthogonal Integrated Conditional Moment Tests for Treatment Effect Heterogeneity

arXiv:2607.12622v1 Announce Type: new Abstract: We propose a nonparametric integrated conditional moment (ICM) test for treatment effect heterogeneity across subpopulations defined by a given covariate subvector. Under unconfoundedness, the null is recast as a conditional moment restriction based on a Neyman-orthogonal score, which reduces the first-order sensitivity of the empirical process to nuisance parameter estimation. The test statistics are constructed as continuous functionals of a marked empirical process. We establish a uniform feasible-to-oracle approximation and derive the asymptotic properties of these test statistics under the null and fixed alternatives. We further show that the test has nontrivial power against local alternatives converging to the null at the $n^{-1/2}$ rate, and develop an easy-to-implement multiplier bootstrap for feasible inference. We also develop extensions to tests of parametric CATE specifications and to settings with endogenous treatment and a binary instrument. Finally, we apply the proposed testing approach to study whether the effect of maternal smoking during pregnancy on infant birth weight varies with maternal age.

arXiv Econ

The Limits of Price Discrimination with a Bayesian Seller

arXiv:2607.12615v1 Announce Type: new Abstract: We study the limits of third-degree price discrimination when the production cost is Bayesian and private to the seller, generalizing the seminal work of Bergemann, Brooks and Morris (2015). The rough setup is the following: A monopoly seller sets different prices for buyers in different "segments" of the market so as to maximize seller surplus. Different ways in which the aggregate market is decomposed into segments lead to different welfare outcomes, i.e., (seller surplus, buyer surplus) pairs. When the production cost is Bayesian, the region of achievable welfare outcomes can exhibit complex shapes beyond the clean characterization by Bergemann, Brooks and Morris for the case with a fixed cost. We show that with a Bayesian cost, this region coincides with a proper projection of a polytope defined by a polynomial number of linear constraints, the essential ones of which correspond to flow conservation in a "discounted" flow network. As a result, we give a polynomial-time algorithm that computes optimal market segmentations in terms of any linear combination of the seller surplus and the buyer surplus. En route, we establish the following structural property: Any market can be written as a convex combination of "extremal markets" in a way preserving the seller surplus and the buyer surplus. These extremal markets are piecewise equal-surplus with respect to different possible costs, generalizing a similar notion introduced by Bergemann, Brooks and Morris when the cost is fixed.

arXiv Econ

Interpreting (and testing) factor loadings

arXiv:2607.12568v1 Announce Type: new Abstract: Dynamic Factor Models (DFMs) are popular to reduce dimensionality being customary in the empirical analysis of large systems of macroeconomic and/or financial variables. In this context, the common underlying factors and their loadings are often extracted using Principal Components (PC), which are consistent and asymptotically normal under very general conditions. Consequently, inference on the factor loadings, which is crucial for the correct interpretation of the underlying factors, is often based on their asymptotic distribution with the limit covariance matrix of the loadings consistently estimated using HAC estimators. In this paper, we analyse the performance of the finite sample asymptotic approximation when constructing confidence intervals and testing about estimated PC loadings. We show that this approximation is seriously affected when the cross-sectional dimension is not large enough. We propose using HAR inference and a subsampling procedure to correct the MSE of the loadings to take into account the uncertainty associated with the estimation of the covariance matrix and of the factors, respectively. The relevance of the results is illustrated in an empirical analysis of economic convergence among the US states.

arXiv Econ

Beyond Consistent Scenarios: Deriving Indirect Influence, Transition Resistance, and Adjustment Dynamics

arXiv:2607.12414v1 Announce Type: new Abstract: Assessments of structural change and economic transition dynamics, such as those arising in the energy transition, depend on internally consistent qualitative scenarios specifying the policy environment, technology mix, governance arrangements, and demand conditions. Cross-Impact Balance (CIB) analysis derives such socio-technical scenarios as fixed-point attractors of an expert-elicited interdependency network, supplying structural inputs upon which assessment models (including energy system optimisation, agent-based, and general equilibrium frameworks) can draw. Standard CIB, however, delivers only this equilibrium catalogue, leaving four structural questions unanswered: how much network-weighted effort a given transition requires; which components are the true system-wide levers once indirect influence chains are counted; in what sequence the system adjusts; and how the network at a given attractor responds to an external shock. This paper extends CIB through Linear Response Theory, exploiting a structural isomorphism between the CIB drift matrix and the Leontief input-output technology matrix. Four analytical objects are derived in closed form: the Type I cross-impact multiplier, which aggregates all direct and indirect influence chains; the perturbation budget, a network-weighted and directionally asymmetric measure of transition effort; the impulse response function, which traces descriptor adjustment sequences and feedback-induced overshoots; and the unit-impulse shock profile, which characterises attractor-specific network sensitivity and yields a direct measure of structural resilience and susceptibility. The framework is applied empirically to an energy-transition cross-impact matrix, yielding all four objects for five structural equilibria, and transfers to any domain in which pairwise influence scores encode structural interdependencies.

arXiv Econ

Choice at Finite Capacity: The Bounded Agent as an Information Channel and the Recovery of Walrasian Demand

arXiv:2607.12412v1 Announce Type: new Abstract: Standard economics assumes the consumer as a flawless calculator who always buys the best basket it can afford. This paper models the shopper instead as a limited information channel: it compresses its world to the detail its attention affords, so its choice is a probability distribution, not a single basket. The textbook consumer returns exactly as the unlimited-attention limit, while at the zero-attention end the shopper falls back on pure habit. The central result is about how this shopper's demand responds to price changes. That pattern of responses is just a rescaling of how the shopper's own choices vary and move together, so it comes out symmetric. And provided the budget really binds, because the shopper wants more than it can afford, raising a good's own price lowers demand for it once buying power is held fixed. So the downward pull comes from the budget and from compression, not from rationality. The framework also covers an artificial agent running a limited-capacity policy. A worked two-good quadratic consumer carries every quantity in closed form.

arXiv Econ

First They Came for the Others: A Theory of Divide-and-Conquer

arXiv:2607.12371v1 Announce Type: new Abstract: Divide-and-conquer tactics often succeed not through mechanical coordination failures, but through epistemic friction regarding an aggressor's underlying intent. When an attacker strikes a first target, bystanders must infer whether the assault represents a localized grievance or a systemic campaign. If the attack is rationally interpreted as particularized, bystanders abstain, prompting the isolated victim to surrender. We demonstrate how higher attack costs and lower correlation between victims' fates facilitate this division. We then study how behavioral responses, rhetoric, treaty commitments, and downstream defense networks modify this inference.

arXiv Econ

Forecasting Inflation with Microdata: An Adaptive Machine Learning Approach

arXiv:2607.12345v1 Announce Type: new Abstract: Does microeconomic heterogeneity help to forecast aggregate inflation in a non-stationary environment? We develop a scan test for whether one forecast outperforms another, over an interval with unknown starting point and duration. To exploit any occasional forecasting power that the scan test detects, we design an adaptive machine learning pipeline. We encode the distribution of price changes into a high-dimensional vector, which we combine with a gradient boosted trees algorithm. We then combine this micro forecast with other benchmark forecasts, using an adaptive algorithm that makes use of the micro forecast only when it performs well. We apply the pipeline to UK microdata, with four main results. First, the micro forecast outperforms a univariate benchmark, but only in the volatile period after 2020. Second, the scan test detects periods of micro outperformance, so the micro forecast enters the combined forecast. Third, the combined forecast performs comparably to the univariate benchmark before 2020 and better at every horizon after 2020. Fourth, the value of microdata for the combined forecast materializes after 2020. We conclude that microdata are valuable for forecasting aggregate inflation, but only after large shocks.

arXiv Econ

Q-SCM: A Quantum-Sequential Choice Model for Driver Mental State Evolution

arXiv:2607.12299v1 Announce Type: new Abstract: We propose a Quantum-Sequential Choice Model (Q-SCM) for modelling driver mental state evolution in interactive traffic environments. The proposed framework retains the classical latent class choice structure, but replaces the conventional class membership formulation with a quantum cognitive state model. A unique feature of this model is that the quantum component is confined to the class membership layer, while the action choice layer remains a classical RUM. The driver's latent state is represented as a two-state quantum system on the Bloch sphere including neutral and defensive states. Perceptual cues, including separation distance, closing time-to-collision (CTTC), and lane deviation induce sequential unitary rotations governed by Pauli matrices. This formulation allows the model to capture memory, phase effects, cue order dependence, and transitions between behavioural regimes that depend on prior cue history. To ensure well-behaved state evolution, we introduce three control mechanisms: a monotonicity constraint that prevents pendulum-like overshoot, a geodesic safeguard mechanism that ensures convergence toward the defensive state under sustained threat exposure, and a relaxation step that allows recovery toward the neutral baseline when the threat weakens. The model is estimated using 85,754 observations from 9,610 drivers extracted from naturalistic trajectories. The empirical results show that defensive state formation is not governed only by the instantaneous values of traffic cues, but also by the accumulated cue history and the order in which cues are processed.

arXiv Econ

KRAFT: A Transaction-Level Dataset for Korean Apartment Sales Integrated with Contextual Indicators

arXiv:2607.11961v1 Announce Type: new Abstract: Apartment transaction records are useful for studying housing markets, household finance, regional economics, and macro-financial transmission, but transaction data are often distributed separately from contextual socioeconomic indicators. We present KRAFT, a nationwide transaction-level dataset of South Korean apartment sales from January 2015 to December 2024. The dataset contains 5,320,379 apartment sale transactions across all 17 Sido regions and includes transaction timing, administrative location, exclusive residential area, reported transaction price, floor level, and construction year. KRAFT also provides auxiliary indicators covering macro-financial conditions, demographic structure, education infrastructure, private education expenditure, housing price indices, consumer sentiment, and economic policy uncertainty. The released files are organized as year-specific transaction files and separate auxiliary data tables to preserve the original temporal and spatial resolution of each source. KRAFT supports reproducible research on apartment price modeling, regional housing-market comparison, housing-demand analysis, and links between housing transactions and socioeconomic context.

arXiv Econ

Modeling the Dynamic Relationship Between Brent Crude Oil Prices and the Nepal Stock Exchange: An Integrated Econometric and Explainable Machine Learning Approach

arXiv:2607.11922v1 Announce Type: new Abstract: This study examines the dynamic relationship between the global oil prices and Nepal Stock Exchange (NEPSE) using an integrated approach which combines traditional econometric techniques with machine learning and explainable AI techniques. For this, Daily data of International Oil prices and NEPSE index is analyzed from approximately thirteen years (June 2013 to June 2026) using Granger causality, EGARCH(1,1), and DCC-GARCH models to examine different properties like predictive relationships, asymmetric volatility behaviour, and time-varying correlations. To further supplement the econometric analysis, Machine Learning Models like Random Forest, LightGBM, and XGBoost algorithms were used to capture nonlinear relationships, along with explainable artificial intelligence techniques like SHAP values, Partial Dependence Plots, and Individual Conditional Expectation plots to further interpret the results of the model. The results from the econometric analysis showed a statistically significant unidirectional Granger causality from Brent crude oil to NEPSE with a four-day lag, high volatility persistence in both markets, and weak yet highly time-varying conditional correlations. Among the machine learning models, XGBoost achieves the best performance, and explainability analysis reveals that NEPSE own momentum and short-term volatility mainly influence its own behaviour and oil-related information serves as a minor, method-dependent contributor. The findings demonstrate that econometric and explainable machine learning approaches provide insights into the oil and equity market relationship in a way that each approach complements the result of one another.

arXiv Econ

Quantile Vector Autoregression without Crossing

arXiv:2601.04663v4 Announce Type: replace-cross Abstract: This paper considers estimation and model selection of quantile vector autoregression (QVAR). Conventional quantile regression often yields undesirable crossing quantile curves, violating the monotonicity of quantiles. To address this issue, we propose a simplex quantile vector autoregression (SQVAR) framework, which transforms the autoregressive (AR) structure of the original QVAR model into a simplex, ensuring that the estimated quantile curves remain monotonic across all quantile levels. In addition, we impose the smoothly clipped absolute deviation (SCAD) penalty on the SQVAR model to mitigate the explosive nature of the parameter space. We further develop a Bayesian information criterion (BIC)-based procedure for selecting the optimal penalty parameter and introduce new frameworks for impulse response analysis of QVAR models. Finally, we establish asymptotic properties of the proposed method, including the convergence rate and asymptotic normality of the estimator, the consistency of AR order selection, and the validity of the BIC-based penalty selection. For illustration, we apply the proposed method to U.S. stock market data, highlighting the usefulness of our SQVAR method.

arXiv Econ

Digital Engagement, Income Disparities, and Job Seeking in the United States since 2010

arXiv:2511.05294v2 Announce Type: replace-cross Abstract: Surveys often record how frequently people use the internet without measuring the infrastructures, skills, and support systems that make digital participation possible. Using the U.S. National Longitudinal Survey of Youth 1997 cohort, we study how internet-use frequency relates to labor income, employment attachment, and job seeking after 2010. The main digital-engagement analysis uses the comparable 2011, 2013, and 2015 waves, with 2017 retained as later labor-market context. Across repeated cross sections, daily internet use consistently marks higher income and stronger employment attachment. Relative to daily use, less-than-daily use is associated with roughly 11 to 20 percent lower income, while nonuse is associated with about 18 to 21 percent lower income in 2011 and 2013. Respondents reporting no internet use are also 13 to 23 percentage points less likely to report full-year work. Job-search estimates reveal a distinct mechanism: active search is governed by employment status, search intensity, and application support, so a frequency item sorts respondents more sharply on durable labor-market attachment than on short-window search. Education accounts for a substantial share of the raw digital gradient, and pooled lagged-outcome and doubly robust transition estimates separate durable stratification from positive adoption margins. The results establish internet-use frequency as an informative behavioral marker of digitally mediated labor-market stratification and clarify why routine use should not be treated as a simple measure of digital access.

arXiv Econ

Private Languages

arXiv:2605.24730v2 Announce Type: replace Abstract: Strategic communication often relies on anchors observed by the sender but not by the receiver. An analyst may report against a proprietary valuation model, an auditor against an internal score, a manager against an accounting estimate, or an institution against its own standard. I study a sender-receiver game in which reports are costly to move away from such privately observed anchors. Anchor heterogeneity changes the geometry of communication. Rather than relying on partitions, privately anchored reporting generates continuous variation in messages because different senders find different reports costly to make. This mechanism can improve information transmission, but it can also pull reports toward noisy private anchors. I show that (i) small positive reporting costs can make communication approach full revelation, even though zero costs return the model to cheap talk, (ii) uninformative anchors can transmit information through strategic distortions. Anchored reports and cheap-talk messages can coexist as endogenous hard and soft information, but cheap-talk alone is preferred by all parties under sufficiently low misalignment, explaining why organizations may rely exclusively on informal channels.

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arXiv Econ