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on Computational Economics |
| By: | LiYang Wang (Washington University in St. Louis); Zhen Zhong (Georgetown University); Zhen Tian (University of Glasgow); Keyu Chen (Wuyi University); Keyu Chen (Wuyi University) |
| Abstract: | Advancements in data fusion and real-time analytics technologies have opened new avenues for addressing complex domain challenges. Financial risk early warning systems often suffer from inefficiency due to information silos and monitoring delays. This paper proposes a credit risk early warning system based on heterogeneous information fusion. The system employs a model architecture integrating deep neural networks and attention mechanisms to extract multidimensional features from diverse data sources such as transaction behaviors and social networks, thereby establishing an early identification mechanism for corporate and individual credit risks. System testing demonstrates that this approach significantly enhances the accuracy and timeliness of risk warnings, outperforming traditional rule-based engine solutions. The findings offer innovative insights for early intervention in financial risks, holding practical significance for safeguarding financial stability. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.15744 |
| By: | Kemal Kirtac |
| Abstract: | Financial language models can transform unstructured firm-specific news into structured decision signals, but financial AI research lacks an integrated deployment framework for evaluating whether those signals remain useful in financial decision systems. Computer science research has developed strong methods for time-series forecasting, text classification, multimodal stock prediction, graph-based market modeling, and machine-learning operations, yet these streams do not provide a domain-specific protocol that jointly tests financial language-model outputs under event-time observability, probability calibration, execution timing, transaction costs, liquidity constraints, capacity limits, operational diagnostics, and statistical inference. We introduce MFAST, a Market-Friction-Aware Sentiment-to-Trading framework that converts timestamped financial text into auditable, reproducible, and market-feasible trading decisions. The application is news-based trading, where firm-specific text must be linked to securities before portfolio decisions can be evaluated. The framework links Refinitiv News Analytics to Center for Research in Security Prices (CRSP) equity data, restricts the primary out-of-sample evaluation to post-release news outside disclosed foundation-model data-freshness periods, and adds a public replication arm using open financial text and public price data. Results show that decoder-only language models outperform encoder baselines and dictionary sentiment in classification, calibration, return prediction, and net portfolio performance, while operational diagnostics reveal trade-offs among accuracy, latency, memory, throughput, and inference cost. The paper shows that credible evaluation of financial language models requires an end-to-end engineering approach combining language understanding, temporal discipline, market-friction-aware deployment, and reproducible validation. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.23703 |
| By: | Gabriel Nova; Stephane Hess; Sander Van Cranenburgh |
| Abstract: | Discrete choice model specification is a time-consuming task in which modellers often specify and estimate multiple models while balancing goodness-of-fit, parsimony, and behavioural plausibility. We present Delphos, a multitask reinforcement learning framework that learns transferable specification strategies across transport choice datasets. Delphos frames model specification as a sequential decision-making problem in which it applies a sequence of modelling actions and receives feedback from an estimation environment based on model performance and convergence. To transfer modelling decisions across datasets with different sets of variables, Delphos represents utility specifications as sets of modelling terms using a DeepSet-Q architecture, allowing a shared specification policy to learn across multiple datasets. Trained on nine transport choice datasets, Delphos consistently outperforms independently trained single-task agents, indicating that sharing modelling experience improves learning efficiency and helps identify promising sequences of modelling decisions with fewer unsuccessful estimation attempts. When applied without further training to the unseen Swissmetro and Decisions datasets, the same agent identifies competitive specifications in less than 20 minutes on a standard CPU. It achieves a higher log-likelihood per observation than the VNS metaheuristic on Swissmetro and performance comparable to a published MNL specification developed by expert modellers on Decisions. These findings show that accumulating and reusing modelling experience enables Delphos to function as an intelligent assistant for discrete choice model specification. It reduces manual trial-and-error while allowing modellers to retain control over model diagnosis, refinement, and final selection. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.18441 |
| By: | Asser Moustafa; Rares-Mihail Neagu; Jugal Kalita |
| Abstract: | Reinforcement learning trading systems published in the academic literature overwhelmingly rely on price-aggregate state representations (OHLCV bars) or limit-order-book depth features, leaving microstructure pattern theories from the practitioner literature, namely Auction Market Theory and Market Profile, without a peer-reviewed computational instantiation. We present ViperQ, a reinforcement learning system whose state representation is built explicitly from Auction Market Theory primitives: Volume Point of Control, Value Area position, Low Volume Node flags, Cumulative Volume Delta divergence, and tape-velocity signatures, assembled into a 20-dimensional Z-normalised vector. Two Proximal Policy Optimisation agents are trained with a prospect theory-grounded asymmetric reward function that penalises losing holds at a magnitude consistent with Kahneman and Tversky's loss-aversion coefficient. Evaluated on a held-out twelve-month partition of institutional tick data the agents have never seen, ViperQ achieves +163.6% ROI on TSLA (-27.5% max drawdown, 27, 019 trades) and +116.5% ROI on NVDA (-47.8% max drawdown, 12, 892 trades) under zero leverage. The results establish Auction Market Theory features as a tractable structured input modality for sequential decision-making on financial time series and motivate further work on microstructure-aware policy learning. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.13825 |
| By: | Moore, Alexander K.; Li, Bingqing; Wang, Shane; Thomson, Matt |
| Abstract: | Large language models (LLMs) are increasingly adopted in the social sciences as surrogates for human coders. Standard practice validates them by their correspondence with human measurements of the same stimuli using statistics like correlation. However, overall correspondence can conceal biases that distort specific claims. We demonstrate this with open-weight LLM ratings of 568 food and 1, 811 scene photographs. Ratings inflate, attenuate, or reverse effects, and improving overall correspondence with prompts can improve one contrast while worsening another. Rankings also elevate some categories and demote others beyond what random errors of the same size typically produce. Across six prompts, some of these errors are shared, so sampling across prompts may continue to mask biases. We propose validating LLM coders against specific claims they will test rather than the general coding task. |
| Date: | 2026–09–21 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:dz3qe_v1 |
| By: | Aashish Bohra; Vivek Vijay |
| Abstract: | Stock price prediction remains challenging due to the non-stationary and noisy nature of financial time series. Existing deep learning models often rely on rigid decision-level fusion, ad hoc hyperparameter tuning, and compressed final-layer outputs, causing information loss, overfitting, and limited cross-market generalization. We propose VertiFuseX, a hybrid LSTM architecture using penultimate-layer vertical fusion of multi-scale temporal representations. VertiFuseX stacks and reweights penultimate features from LSTM, Bi-LSTM, and St-LSTM branches, integrates a parallel DNN stream, and jointly optimizes all components via backpropagation under a fixed hyperparameter configuration. This preserves richer intermediate temporal information across scales. Evaluated on 15 years (2010-2024) of closing prices from 10 global equity indices using strict chronological out-of-sample testing with the final 365 trading days held out, VertiFuseX achieves 30-54% MAPE reductions and over 40% improvements in MAE and RMSE versus LSTM-based baselines, and outperforms seven state-of-the-art models across 33 metric-dataset comparisons. Ablation studies confirm penultimate-layer fusion drives these gains over final-layer fusion and decision-level ensembling. Gradient-based saliency analysis shows consistent emphasis on mid-range dependencies at lags 9-15 days. Economic validation via algorithmic trading simulation under extreme market regimes shows reduced maximum drawdowns and superior risk-adjusted returns. With 675k parameters, a 2.6 MB memory footprint, and 1.5 ms/sample inference latency, VertiFuseX offers a lightweight, interpretable, deployment-ready framework for robust financial forecasting. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.12793 |
| By: | Ruimeng Hu; Yihan Zou |
| Abstract: | Reflected backward stochastic differential equations (RBSDEs) provide a probabilistic formulation for obstacle constrained problems, but existing deep learning methods for their high dimensional solution remain limited. In this paper, we propose two deep learning schemes for RBSDEs, a deep forward scheme (DFS) and a deep backward scheme (DBS), by first reducing the reflected problem to a family of regularized BSDEs. Our main theoretical contribution concerns the DBS: we establish an explicit error bound showing that, for each fixed regularization parameter $\varepsilon>0$, the approximation error between the DBS solution and the solution to the regularized BSDE is controlled by the associated training loss. We prove that this training loss can be controlled by the universal approximation capability of neural networks. Together, these results yield a theoretical foundation for the deep learning-based solution and complement existing analysis for forward type methods. We illustrate the framework on high dimensional American option pricing, where the reflected formulation allows us to address the continuous time exercise feature directly rather than through a Bermudan approximation. Numerical experiments demonstrate that both DFS and DBS deliver accurate solutions in high dimensions. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.05434 |
| By: | Jan Lisicki (University of Warsaw, Faculty of Economic Sciences); Tomasz Kopczewski (University of Warsaw, Faculty of Economic Sciences) |
| Abstract: | Economic agents adapt their behavior to experience generated by interactions with other market participants. When several agents update their behavioral rules simultaneously, these adaptation processes become interdependent. We refer to this mechanism as co-adaptation. The paper develops a formal representation that separates the continuation of an agent's own rule updates from cross-agent effects on subsequent updates. In a two-agent system, mutual influence can increase the cumulative effect of an initial change and prolong the resulting adjustment process. A simple model of price adjustment between two firms shows how these effects depend on adjustment speed and the response to a competitor's price change. With more agents, interdependencies between adaptation processes form a directed network. This perspective links economic adjustment to multi-agent reinforcement learning, where learning by one agent changes the environment experienced by others, and clarifies when such non-stationarity is part of economic dynamics rather than only a computational difficulty. |
| Keywords: | economic adjustment, multi-agent reinforcement learning, adaptive learning, agent-based computational economics, co-adaptation |
| JEL: | B41 C61 C63 C73 D83 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:war:wpaper:2026-31 |
| By: | Nacira Agram; Jan Rems; Emanuela Rosazza Gianin |
| Abstract: | We study optimal stopping under dynamic risk measures with simultaneous ambiguity in the probability model and the discount rate. We introduce a paired ambiguity framework combining Girsanov model uncertainty with cash subadditive risk evaluation and characterize the stopping value by an upper reflected backward stochastic differential equation (BSDE). We establish structural properties of the resulting stopping operator and study quadratic drivers associated with entropic risk measures, obtaining explicit stopping rules in several benchmark cases. We then develop a deep learning scheme for the reflected quadratic BSDE. The convergence analysis uses discrete reflection and truncation to reduce the quadratic problem to a globally Lipschitz system and combines reflected BSDE discretization estimates with neural network approximation errors. Numerical experiments for American options illustrate the effects of discount rate and entropic ambiguity on stopping values and exercise decisions. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.23768 |
| By: | Marco Albori (Bank of Italy); Andrea Zaghini (Bank of Italy) |
| Abstract: | We study how the emergence of generative artificial intelligence (AI) affected corporate bond pricing. Using bond-level issuance data for U.S. non-financial corporations and a difference-in-differences design centered on the launch of ChatGPT, we examine whether debt investors differentiate firms according to their position in the AI value chain. We find that hyperscalers experienced a significant decline in borrowing costs relative to comparable issuers, while software firms faced less favorable financing conditions. The results suggest that credit markets rapidly incorporated expectations regarding the distribution of gains and losses from generative AI. More broadly, the evidence indicates that technological shocks affect not only firm valuations but also the cost of external finance, thereby influencing the allocation of capital across firms during periods of technological transition. |
| Keywords: | artificial intelligence, asset pricing, bonds, corporate finance |
| JEL: | G12 G32 C23 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:bdi:opques:qef_1057_26 |
| By: | Masahiro Kato; Daiki Honma; Taka Kato |
| Abstract: | Generative artificial intelligence changes how firms reach customers, but standard marketing data do not record how often users see and notice a firm's name in generated answers. We develop Generative Marketing Mix Modeling (GMMM) to estimate the causal effects of Generative Engine Optimization (GEO) and Generative Engine Marketing (GEM). For GEO, GMMM combines repeated generated answers with question counts, shares of use across generative systems, and notice probabilities. For GEM, it combines records of sponsored placements with notice probabilities. GMMM compares expected business responses under alternative treatment sequences and establishes sufficient conditions for identifying the resulting effects. We investigate the empirical performance of the proposed method using simulated answers to product recommendation in English and Japanese. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.11915 |
| By: | Borgioli, Stefano; Gallo, Giampiero M.; Ongari, Chiara; Mavillonio, Maria Saveria; Giannetti, Caterina |
| Abstract: | Measuring sentiment from financial news is a central task in economics and finance, yet most existing indicators rely on dictionary-based approaches that infer sentiment from word counts and only partially capture context, negation, and semantic structure. This paper proposes a framework for constructing daily news mood indices using transformer-based language models and evaluates whether they better represent sentiment than dictionary-based alternatives. Using 143, 755 financial news articles from Factiva, we classify sentiment at the sentence level with FinBERT and aggregate these predictions into article-level and daily sentiment measures through alternative normalization schemes. We compare the resulting indices with benchmark measures based on Shapiro et al., 2022 and Barbaglia et al., 2025. A central contribution is the validation of alternative sentiment measures against human judgments. We conducted an incentivized annotation exercise in which 444 participants evaluated a validation subsample of 588 financial news articles. Consensus ratings from independent human evaluations serve as an external benchmark for assessing the quality of automated sentiment measures. Across correlation, regression, and classification exercises, transformer-based measures show stronger agreement with human judgments than vocabulary-based alternatives and perform substantially better in distinguishing positive, neutral, and negative articles. Overall, the results suggest that incorporating contextual information through transformer-based language models produces sentiment measures that more closely reflect human assessments of financial news. JEL Classification: C55, C81, E32, E37, G14 |
| Keywords: | daily mood indices, financial news sentiment, FinBERT, human validation, text-as-data, transformer-based language models |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20263283 |
| By: | Anna Kurowska (University of Warsaw, Faculty of Economic Sciences, Interdisciplinary Centre for Labour Market and Family Dynamics (LabFam)); Magdalena Grabowska (University of Warsaw, Faculty of Economic Sciences, Interdisciplinary Centre for Labour Market and Family Dynamics (LabFam)); Maciej Świtała (University of Warsaw, Faculty of Economic Sciences); Beata Osiewalska (University of Warsaw, Faculty of Economic Sciences, Interdisciplinary Centre for Labour Market and Family Dynamics (LabFam); Cracow University of Economics) |
| Abstract: | While studies highlight COVID-19's disruptive impact on fertility, whether pandemic-era childbearing predictors remain informative post-crisis is less clear. Using Polish Familydemic panel data from 2021–2024 (N = 1, 925, aged 18–49), we apply theory-guided machine learning to evaluate a broad set of individual, employment, partnership, and family characteristics. Because short-term fertility intentions prove most predictive of childbearing, we model both actual births and firm positive intentions using random forest. The pandemic-trained model retained strong post-pandemic predictive performance (AUC-ROC = 0.86), demonstrating that pandemic-era drivers remain informative. Childbearing and intentions shared 16 of their top 20 predictors, highlighting common relational, demographic, and work–family factors. Relationship satisfaction, work–life balance, and domestic work division were particularly prominent, whereas prolonged school and childcare closures lowered predicted probabilities for both outcomes among parents. Ultimately, post-pandemic fertility dynamics reflect persistent constraints embedded in everyday family and working lives. |
| Keywords: | childbearing, fertility intentions, COVID-19 pandemic, machine learning, relationship satisfaction, childcare closures |
| JEL: | J13 J12 J22 C53 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:war:wpaper:2026-32 |
| By: | Rafael Macalaba; Aivin V. Solatorio; Patrick Michael Brock; Olivier Dupriez |
| Abstract: | Development and humanitarian organizations produce and support surveys, administrative registries, and other data resources to inform research, policy, and operations, yet systematically identifying where these datasets are referenced remains difficult. Such references are dispersed across research papers, project documents, humanitarian reports, and other unstructured text, limiting both the ability to trace data use and to identify potential gaps in data availability or dissemination. We present a weakly supervised framework for adapting dataset extraction to forced displacement and Fragile, Conflict, and Violence (FCV) documents without first constructing a large manually labeled training corpus. A lightweight model trained on general research literature generates candidate dataset mentions from unlabeled domain documents, which a frontier large language model (LLM) reviews in context, validating or rejecting candidates and correcting their extraction boundaries. The resulting annotations are supplemented with targeted synthetic and contrastive examples and used to fine-tune the lightweight model for large-scale extraction. We evaluate the resulting model on an independent gold-standard benchmark of 1, 706 text passages spanning research, humanitarian, and operational documents. Across the full benchmark, the model achieves 74.1\% precision and 70.5\% recall at the mention level; among passages containing dataset references, precision reaches 89.5\%. At the passage level, the model achieves 88.2\% accuracy and 88.6\% specificity in distinguishing passages with dataset references from those without them. These results demonstrate a practical approach for constructing domain-specific supervision when labeled data are limited, and provide a technical foundation for larger-scale analysis of data use and potential gaps in the displacement data landscape. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.12107 |
| By: | Lilian Hu; Congxin He; Yue Kuen Kwok; Gongqiu Zhang |
| Abstract: | The standard Euler discretization schemes for numerical option pricing under stochastic volatility models are known to exhibit high biases and potential unreliability. The alternative use of the exact (unbiased) simulation approach invariably involves numerical evaluation of integrated variance (and / or volatility) conditional on terminal variance (volatility) value. To resolve the technical challenge, most simulation schemes either employ the tedious Fourier inversion of conditional characteristic function or numerical approximation by moment matched distribution. We propose a general framework of constructing efficient and reliable simulation schemes for stochastic volatility models via the Strang operator splitting approximation. The simulation procedure completely circumvents the necessity of evaluation of conditional integrated variance (and / or) volatility. Our simulation schemes compete favorably well with most existing exact simulation schemes and the biased Euler schemes in terms of accuracy, efficiency, reliability and ease of implementation. Extensive numerical tests were conducted to illustrate the versatility and success of our operator splitting approach for most common stochastic volatility models, such as the Heston-type models, lifted Heston model, Hull-White model, and Barndorff-Nielsen and Shephard model. We also establish the proof of second-order convergence of the operator splitting schemes. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.21301 |
| By: | Turner, Ryan; Jafari, Afshin; Tiwari, Sapan (RMIT University); Both, Alan; Singh, Dhirendra; Pemberton, Steve; De Gruyter, Chris |
| Abstract: | Testing transport interventions using city-scale transport models is typically resource intensive, requiring specialist expertise, substantial data and computational capacity. As a result, such modelling is often concentrated on major projects and larger metropolitan areas, while smaller interventions and mid-sized regional cities have limited access to comparable modelling capabilities. This is particularly relevant to active travel, where cycling infrastructure decisions frequently need to be made with relatively limited modelling and decision-support capacity. This study presents FASTrack, a cloud-based planning support software system that integrates agent-based modelling, behavioural simulation, and interactive scenario visualisation to enable proposed cycling infrastructure assessment. A case study is applied to the City of Greater Bendigo, a mid-sized regional Australian city representative of the planning contexts that existing platforms often overlook. FASTrack was developed through a co-design process over three years with local government planning partners, to address the need to rapidly assess infrastructure scenarios, quantify changes in cycling demand and behaviour, and inform infrastructure planning and investment appraisal. These requirements underpin its design, including the use of probabilistic modelling and a cloud-based workflow designed to support rapid, on-demand scenario testing with minimal specialist modelling expertise. Scenario outcomes are presented through interactive dashboard-style interfaces that detail estimated changes in network use, level of traffic stress, mode shift, cycling usage, emissions, and cost-benefit analysis aligned with national appraisal guidelines. This study demonstrates how co-design can directly shape the development of planning support systems by translating practitioner requirements into accessible modelling platforms for cycling infrastructure planning |
| Date: | 2026–09–16 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:q69rv_v1 |
| By: | Ana Isabel Castillo Pereda |
| Abstract: | This work presents an operator-based visual analytics pipeline for exploring synthetic systemic risk dynamics in financial networks. The framework is formulated as a composition of mathematical operators that sequentially transform synthetic financial observations into dynamic scientific visualizations. The pipeline consists of six operators: latent risk mapping, probabilistic score generation, financial network construction, distance-based contagion dynamics, visual encoding, and perspective projection. Together, these operators provide a modular computational structure linking nonlinear risk surfaces, time-dependent probabilistic states, network topology, and shock propagation. A reproducible implementation demonstrates the architecture through controlled synthetic experiments. Nonlinear latent risk representations are converted into probabilistic scores, embedded in a weighted financial network, and propagated via shortest-path contagion. The resulting states are then mapped into dynamic visual representations. The experiments illustrate how visualization can be treated as an explicit stage of the analytical process rather than a post-processing step. The proposed formulation does not aim to introduce new predictive models or contagion mechanisms. Instead, it offers a transparent and modular pipeline that connects generative risk modeling, network dynamics, and scientific visualization within a unified operator-based architecture. This approach supports reproducibility and facilitates the exploration and communication of complex systemic-risk processes in synthetic settings. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.22202 |
| By: | Congxin He; Yue Kuen Kwok |
| Abstract: | We develop an efficient Monte Carlo simulation scheme for pricing options under the Ornstein-Uhlenbeck driven stochastic volatility model via the operator splitting approach. With an ingenious splitting of the governing stochastic differential equations, our operator splitting scheme admits analytic solutions in all sub-steps, so its implementation is simplified to require simulation of a few normal variates. This resolves the two typical numerical challenges in other simulation schemes, namely, sampling of conditional integrated variance and pathwise inverse integral transform of characteristic functions. There are three pioneering simulation schemes that attempt to overcome the above two numerical challenges. These include the Hilbert interpolation scheme of Zeng et al. (2023), Karhunen-Lo`eve expansion scheme of Choi (2025) and moment matching scheme based on the Inverse Gaussian distribution of Brignone and Sgarra (2026). We performed numerical tests to compare accuracy-speed performance of pricing options using our operator splitting scheme with these three pioneering schemes. We found that our scheme competes favorably well in terms of accuracy-speed tradeoff among all these schemes, in particular for pricing path dependent options with a large number of monitoring instants. The performance of our scheme can be well enhanced by martingale-preserving control variates and variance reduction via conditioning. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.21291 |
| By: | Alexandra Glazova (Bank of Russia, Russian Federation) |
| Abstract: | In this paper, I introduce labour market mechanisms used in partial equilibrium models, microdata econometric studies, and research in psychology and sociology to an agent-based macroeconomic model. These mechanisms include skill-based worker switching, skill loss during unemployment, and the skill-dependence of wages. I show that when a large supply shock occurs, these complexities prevent GDP from fully recovering, at least in the medium term: the GDP gap between the shock and no-shock scenarios persists even five years after the shock, whereas no such difference is observed in the baseline model. I also show that the aggregate modelling shock affects different industries differently, and this effect is statistically significant depending on the level of skills required in a given industry. Finally, I demonstrate that training programmes for new workers in just the two most affected industries have a comparable impact to training a much larger number of workers with no industry-specific constraints. |
| Keywords: | Agent-Based Modelling, Simulation Modelling, Labour Market Frictions, Human Capital, Labour Mobility, Wages, Employment, Formal Training Programmes, Learning by Doing, Skill, Retraining, Skill Based, Career Change, Occupational Mobility, Job Search, Retraining Programme |
| JEL: | C63 J24 J31 J62 J63 J64 J68 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:bkr:wpaper:wps176 |
| By: | André Binette; Colleen Smith |
| Abstract: | This note presents Segmented Inflation Dynamics (SID), a data-driven tool for assessing trend inflation. SID uses time-series segmentation to divide the price index into contiguous segments with stable inflation rates. The segmentation breaks are identified from the data. The slope of each segment provides a well-defined estimate of trend inflation that is stable but responsive to persistent shifts. Using Canadian data from 1992 to 2025, the note describes the resulting inflation segments, tests the robustness of the method and assesses its real-time properties. Results show that SID offers a stable, interpretable measure of trend inflation, with limited sensitivity to short-term volatility and quick adjustment to shifts in inflation dynamics. Based on data through December 2025, SID indicates that Canada’s trend inflation has been near 2 percent since early 2024. |
| Keywords: | Models and tools; Econometric, statistical and computational methods; Monetary policy; Inflation dynamics and pressures |
| JEL: | C C2 C22 E E3 E31 E5 E52 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:bca:bocsap:26-43 |
| By: | Antonio Diez de los Rios |
| Abstract: | We show that the parameters of a broad class of portfolio-balance models can be recovered from estimates of Gaussian dynamic term structure models (GDTSMs) using a two-step estimator that bypasses the fixed-point problem that characterizes portfolio balance models. Specifically, we develop a novel canonical representation of bond supply in terms of factor-mimicking portfolios, whose returns replicate the structural shocks to the pricing factors. We identify these structural shocks by assuming that each shock corresponds to a distinct, mutually exclusive dimension of bond-supply variation. Using U.S. Treasury yields and macroeconomic data, we show the recovered shocks admit interpretations as hedging-risk-premium, risk-bearing-capacity, and monetary policy shocks. |
| Keywords: | Financial markets and funds management; Funds management; Models and tools; Econometric, statistical and computational methods |
| JEL: | E E4 E43 E44 G G1 G12 H H6 H63 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:bca:bocawp:26-33 |
| By: | Toyoda, Yuki |
| Abstract: | Qualitative Comparative Analysis (QCA) sets thresholds when constructing sets from continuous scales. A standard analysis, however, is fixed to a single set of thresholds, so it does not directly reveal how the sufficiency structure changes across different outcome levels. Researchers can repeat the analysis at individual thresholds, but comparing the emergence and disappearance of configurations, solution fit, and the presence of multiple minimal solutions while keeping settings consistent requires additional implementation. This article introduces the R package Threshold-Sweep Qualitative Comparative Analysis (ThSQCA), which addresses this gap. ThSQCA uses the QCA package as its computational engine and provides a common workflow to run, aggregate, visualize, and report analyses that systematically vary the outcome threshold, the condition thresholds, or both. In a consumer survey of usage intention toward Starbucks (n = 336), moderate target levels of usage intention yielded multiple sufficient paths, whereas the highest target level left only one narrow path, with solution coverage dropping substantially. Varying the threshold that defines service delivery as high further produced different sufficiency structures, including cases with no solution, for the same high level of usage intention. Such differences cannot be observed in a single-threshold analysis. ThSQCA does not automatically select the correct threshold. It is a diagnostic and comparative workflow that clarifies, over a research- or practice-relevant range, at which levels a conclusion holds and where it changes. This allows QCA results to be evaluated not as an answer specific to one threshold, but as decision-relevant information that depends on the target level. |
| Date: | 2026–09–24 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:yb8xs_v1 |
| By: | Bose, Joy |
| Abstract: | Firms adopting AI to substitute for labor face a coordination problem that recurs across two theoretically incompatible traditions: demand-side models, where aggregate demand depends on the same wage income that firms are cutting, and Marxian value-theoretic models, where the rate of profit depends on a capital-composition ratio that competitive automation drives in a self-undermining direction. We define a latent labor-intensity variable and channel-specific mappings into each tradition's own state variable, rather than assuming the two traditions share a state variable, and ask a narrower, computationally checkable question: under what conditions does decentralized, competitive automation converge on a labor intensity below the level that maximizes each channel's own aggregate outcome, a condition we call automation overshoot. Computed independently across a shared parameter space, the two channels show substantial but incomplete agreement (95% and 84% of the swept region show overshoot in the demand and Marxian channels respectively, 83% show it in both), a result that survives a fifteen-specification robustness check across alternative functional forms and internalization strengths (intersection fraction range 0.57-0.94, mean 0.82). Market concentration moves both channels toward less overshoot under fully independent native-parameter sampling that removes any shared coordinate system, a directional claim that survives four targeted falsification tests, one of which an earlier version of this model failed: a mechanical scale artifact in the demand channel, where more competing firms mechanically implied more aggregate automation activity regardless of any internalization behavior, is identified, derived away analytically, and corrected, with every result in this draft reported post-correction. A follow-up generalization addresses a further concern directly: firm count is not the same thing as market concentration. Removing the symmetric-firm assumption and re-deriving the demand channel's internalization term for heterogeneous market shares shows the correction scales as s_i^2 for each firm's own share s_i, so that aggregate internalization is governed by the Herfindahl-Hirschman Index, HHI = sum over i of s_i^2, with the earlier N-based results recovered as the symmetric-share special case. Holding firm count fixed at N=10 while redistributing shares from equal to one dominant firm moves demand-channel overshoot by a factor of 37; plotting demand-channel overshoot against HHI directly across firm counts from 5 to 50 collapses the resulting curves onto one relationship to within 5%, indicating HHI, not N, explains the concentration dependence in this channel substantially better than firm count alone, within the heterogeneous-share configurations tested and the isolating-benchmark design described in Section 4a. Separately, an analysis of the demand and Marxian channels' concentration relationships under independent sampling shows the two channels do not differ in "magnitude" on a single shared scale so much as in shape: the demand channel exhibits a sharp threshold effect concentrated at low firm counts, while the Marxian channel exhibits a shallow, gradual effect spread across the full sampled range of market structures. We extend the model with a hysteretic labor-effort function motivated by, but formalized independently of, Byung-Chul Han's account of the achievement subject: workers sustain effort under a belief that individual strategy can secure their position, a belief that collapses abruptly once a recognition threshold is crossed and recovers only past a lower threshold than the one that triggered the collapse. Wired into a simulated output trajectory, this produces a counterfactual output shortfall that persists after visible layoffs have already subsided; it is reported as an extension, not as co-equal with the core overshoot and concentration results. |
| Keywords: | automation overshoot, computational economics, artificial intelligence adoption, hysteresis |
| JEL: | C63 D43 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:343301 |