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on Big Data |
| By: | Zonghao Yang |
| Abstract: | Counterfactual analysis aims to predict potential outcomes under hypothetical scenarios, offering valuable insights for decision-making. This paper investigates the application of large language models (LLMs), specifically the GPT-3.5 model, for counterfactual analysis. We focus on the online lending context, where the counterfactual return on investment (ROI) is crucial for evaluating different interest rate schemes. We begin by assessing the predictive performance of GPT and comparing it with advanced machine learning algorithms. The results show that prompt engineering can significantly enhance GPT's predictions, with the R-squared increasing from 1.97% to 2.84%, closely approaching the 3.48% achieved by gradient-boosted regression. Subsequently, we utilize GPT to generate counterfactual ROIs under a set of alternative interest rates. GPT exhibits logical coherence and causal reasoning in its responses. The findings underscore the potential of LLMs as effective tools for counterfactual analysis in online lending, suggesting broader applications for LLMs in various predictive and decision-making contexts. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.05367 |
| By: | Nicholas Bloom; Steven J. Davis; Stephen Hansen; Peter John Lambert; Yabra Muvdi; Raffaella Sadun; Bledi Taska |
| Abstract: | The pandemic catalyzed an enduring shift to remote work. To measure this shift, we develop a large language model (LLM), fine tune and assess it using 30, 000 human classifications, and apply it to nearly 600 million job vacancy postings across five English-speaking countries. Our model achieves a 98% classification accuracy, greatly outperforming dictionary-based approaches, and matching or surpassing the performance of frontier AI models at a fraction of the cost. From 2019 to 2026, the share of postings that indicate new employees can work remotely at least one day per week rose more than three-fold in the U.S. and by a factor of five or more in Australia, Canada, New Zealand and the U.K. These developments are highly non-uniform across and within cities, industries, occupations and companies. Even when focusing on employers in the same industry competing for talent in the same occupations, we find large differences in the share of job postings that explicitly offer remote work. |
| Keywords: | remote work; hybrid work; work from home; job vacancies; text classifiers; large language models; pandemic impact; labour markets |
| JEL: | E24 O33 R3 M54 C55 |
| Date: | 2026–08–19 |
| URL: | https://d.repec.org/n?u=RePEc:fip:feddwp:103689 |
| By: | Rawend Brahem (Central Bank of Tunisia) |
| Abstract: | Forecasting inflation in the presence of changing economic conditions, external shocks, and evolving transmission mechanisms remains an active area of research. This paper applies machine learning (ML) models to forecast headline and core inflation in Tunisia at 1-, 3-, and 6-month horizons, comparing their performance with standard econometric benchmarks within a rolling forecasting framework. Conformal prediction intervals are used to assess forecast uncertainty, while SHAP values serve as an exploratory tool to examine the contribution of explanatory variables to model predictions. The results reveal a clear horizon-dependent pattern, with the predictive gains of ML models increasing at medium and longer horizons. These gains are particularly pronounced at the 6- month horizon, where Support Vector Regression reduces the RMSE by more than 40 percents relative to the best-performing benchmark. Furthermore, SHAP analysis suggests that the relative contribution of predictors varies across forecasting horizons, with inflation persistence playing a more prominent role at short horizons and monetary, external, and commodity price variables becoming more relevant at longer horizons. Overall, these findings suggest that machine learning methods are most valuable as a complement to, rather than a substitute for, traditional forecasting approaches, particularly at longer horizons where nonlinearities become more pronounced. |
| Keywords: | Inflation Forecasting; Machine Learning; Conformal Inference; SHAP Values; Tunisia |
| JEL: | C53 E31 E37 |
| Date: | 2026–09–08 |
| URL: | https://d.repec.org/n?u=RePEc:gii:giihei:heidwp25-2026 |
| By: | Nathan Canen; Ted Enamorado |
| Abstract: | Prediction-based methods, including Large Language Models (LLMs) and other machine learning techniques, are often used to construct measures of political phenomena that are difficult to quantify directly, such as policy positions in manifestos or emotions expressed on social media. In many applications, these prediction-generated measures are used as explanatory variables in regression models, even though they are measured with error. This leads to biased estimates. In this paper, we propose a simple solution to these biases: instrumental variables constructed from multiple measures created on independent splits of the original data. This approach is theoretically valid, easy to implement, and does not require new data. Through simulations, we show that this approach recovers estimates close to the true values, even in relatively small samples, while the standard approach can produce substantial bias in practice. We illustrate the method by revisiting two applications: whether gendered speech affects legislative outcomes in the German Parliament, and whether political risk influences poverty alleviation programs in China. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.02909 |
| By: | Mingli Chen (University of Oxford); Rama Cont (University of Warwick); Andreas Joseph (Bank of England); Michael Kumhof (Bank of England); Xinlei Pan (University of California (Berkeley)); Wei Xiong (University of Oxford); Xuan Zhou (Reserve Bank of Australia) |
| Abstract: | We propose deep reinforcement learning (DRL) as a general approach to bounded rationality in dynamic stochastic general equilibrium (DSGE) models. Agents are represented by deep artificial neural networks and learn to maximise their intertemporal objective function by interacting with an a priori unknown environment. Applying this approach to a model from the adaptive learning literature, DRL agents can learn all equilibria irrespective of local stability properties. However, learning is slow and may be unstable without the imposition of early stopping criteria. These findings can have implications for the use and interpretation of DRL agents and of DSGE models more generally. |
| Keywords: | Artificial intelligence;deep reinforcement learning;adaptive learning;monetary policy;fiscal policy;multiple equilibri |
| JEL: | C14 C52 D83 E52 E62 |
| Date: | 2025–09–26 |
| URL: | https://d.repec.org/n?u=RePEc:boe:boeewp:023264 |
| By: | Marcus Buckmann (Bank of England); Galina Potjagailo (Bank of England); Philip Schnattinger (Bank of England) |
| Abstract: | We propose the Blockwise Boosted Inflation Model (BBIM), a boosted tree framework that decomposes inflation dynamics into predictive components aligned with an open-economy hybrid Phillips curve. Demand and supply contributions are identified by imposing monotonicity constraints, ensuring theory-consistent links between inflation and key indicators. Applied to monthly UK CPI inflation, the model shows that the recent surge has been driven mainly by global supply shocks transmitted through supply chains. We also uncover an L-shaped Phillips curve relationship between inflation and labour market tightness, with tight labour markets amplifying recent inflationary pressures. By contrast, earlier episodes saw non-linearities more strongly tied to broader slack, particularly during recessions. The model further accounts for trend shifts informed by inflation expectations. Short-term household expectations have recently displayed persistent non-linear effects, temporarily raising trend inflation and prolonging inflationary pressures, while longer-term expectations remain anchored. Out-of-sample, the BBIM delivers competitive forecasting performance relative to linear benchmarks and unstructured machine learning methods. Our approach provides a flexible yet interpretable framework that combines economic structure with machine learning for policy-relevant analysis of inflation dynamics. |
| Keywords: | Inflation;Phillips curve;boosted decision trees;machine learning |
| JEL: | E31 E37 C14 C53 |
| Date: | 2025–09–26 |
| URL: | https://d.repec.org/n?u=RePEc:boe:boeewp:023265 |
| By: | Marcus Buckmann (Bank of England); Galina Potjagailo (Bank of England) |
| Abstract: | This paper discusses how economic theory can be integrated into machine learning (ML) models to enhance their interpretability and applicability for policy analysis. While ML methods offer considerable flexibility and strong predictive performance, they are often criticised for their 'black box' nature and lack of economic transparency. A growing body of research addresses this limitation by introducing structure into ML models − most notably through Block-Additive Models (BAMs) and theory-consistent monotonicity constraints. BAMs group predictors into economically meaningful blocks and impose additivity across blocks, while permitting non-linearities and interactions within them. This architecture enables clear attribution of each block’s contribution to the model’s predictions. Monotonicity constraints further improve interpretability by aligning the model’s directional responses with economic theory, allowing for the separation of opposing effects − such as distinguishing between supply- and demand-driven components of inflation. Empirical evidence shows that these structured ML approaches retain strong predictive performance while yielding economically meaningful narratives. |
| Keywords: | Interpretable machine learning;theory-aligned constraints;macroeconomic analysis |
| JEL: | C10 C14 C53 |
| Date: | 2025–09–26 |
| URL: | https://d.repec.org/n?u=RePEc:boe:boeewp:023266 |
| By: | Ahmed Asaad; Amr Mohamed; Yang Zhang; Omneya Abdelsalam |
| Abstract: | Large Language Models (LLMs) increasingly use user context such as memory, profiles, and role prompts to personalize their responses. This personalization can affect evidence-based judgment: the same evidence may lead to different conclusions under different user contexts. Finance provides a high-stakes setting to study this problem because decisions often depend on interpreting long and complex documents. We test this using 3, 575 SEC filings across twelve LLMs. We compare persona-conditioned retrieval, neutral retrieval, and memory-framed context to separate the effect of evidence selection from the effect of interpretation. We find that most user-context spillover comes from how models interpret the same evidence under different roles, rather than from retrieving different evidence. We then test two simple mitigation strategies: expressing the same investor mindset as a user profile instead of an assistant role, and separating evidence-based and personalized outputs. Both reduce spillover, but neither removes it completely, and their effectiveness varies substantially across models. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.03218 |
| By: | Mert Geyiktepe; Dani Rodrik |
| Abstract: | Using text-as-data methods, we quantify the U.S. discourse on China, place it in historical perspective, and compare its evolution across different sources of public narrative. Our analysis is based on a large corpus of materials drawn from public records (presidential and congressional records, newspaper articles, social media, think tank reports). We develop two measures of narrative discourse: an indicator of the frequency with which China is covered and a proxy for how positively or negatively China is presented. We document a sustained decline in net sentiment towards China in presidential sources since the early 2000s, predating Donald Trump’s first term in office. In contrast to presidential documents, congressional, news media, social media, and think tank sources exhibit persistent negative sentiment towards China even in decades prior to the 2000s. Instead of a deterioration in sentiment across the board, what we find is a convergence in presidential sentiment to other sources of narrative discourse. We find no evidence that presidential leadership has played a significant role in setting the narrative tone for the nation as a whole. Historical and shorter-term evidence both point to a bottom-up process of diffusion of narratives, rather than top-down diffusion. |
| JEL: | F5 P0 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35539 |