AI Economics Research Podcast

This episode breaks down a new Bank of England research paper that explores how economic cycles, like recessions, impact the stability of earnings for UK households. It reveals that during downturns, negative income shocks become more frequent, even if the overall spread of income changes remains similar, and introduces a new model to track these crucial dynamics for policymakers. Tune in to understand why this matters for financial stability and macroeconomic policy, and share your thoughts with us at [email protected]. This episode explains a real academic paper in plain English for a general audience. Source paper: Modelling income risk dynamics in - Bank of England https://www.bankofengland.co.uk/-/media/boe/files/working-paper/2025/modelling-income-risk-dynamics-in-the-uk-a-parametric-approach.pdf Keywords: Income Risk, UK Economy, Macroeconomics, Financial Stability, Central Banking, Earnings Dynamics

This episode breaks down a recent Bank of England working paper, exploring the best econometric methods for accurately estimating structural parameters in complex economic models. We demystify Local Projections (LP) versus Vector Autoregressions (VAR), and compare Impulse Response Function (IRF) matching with Indirect Inference. Discover why Indirect Inference is often the more robust and reliable approach for central banks and researchers. This episode explains a real academic paper in plain English for a general audience. Source paper: Local Projections vs. VARs for - Bank of England https://www.bankofengland.co.uk/-/media/boe/files/working-paper/2025/local-projections-vs-vars-for-structural-parameter-estimation.pdf Keywords: DSGE models, Econometrics, Macroeconomics, Indirect Inference, VAR models, Local Projections

This episode delves into a groundbreaking research paper from the Bank for International Settlements, exploring how artificial intelligence is being harnessed to monitor and predict financial market stress. We break down a novel approach that combines recurrent neural networks (RNNs) to forecast market dysfunction with large language models (LLMs) to explain the underlying drivers. Discover how this dual AI system can identify "canaries in the coal mine" like deviations in currency arbitrage, offering early warnings for financial stability. This episode explains a real academic paper in plain English for a general audience. Source paper: Harnessing artificial - Bank for International Settlements https://www.bis.org/publ/work1291.pdf Keywords: Financial Stability, Forecasting, Artificial Intelligence, Machine Learning, Central Banking, Market Stress

This episode delves into a Bank of England research paper exploring how machine learning is being applied in central banking and policy analysis. We break down complex AI concepts into plain English, showing how tools like neural networks are used for economic forecasting, financial regulation, and understanding market trends. Discover the future of data-driven decision-making at institutions vital to financial stability. This episode explains a real academic paper in plain English for a general audience. Source paper: https://www.bankofengland.co.uk/-/media/boe/files/working-paper/2017/machine-learning-at-central-banks.pdf Keywords: Machine Learning, Central Banking, Economic Forecasting, Financial Stability, Inflation, Macroeconomics

This episode dives into a new Bank for International Settlements (BIS) research paper investigating Artificial Intelligence (AI) adoption across European firms. Learn how AI boosts labor productivity by 4% without displacing jobs in the short term, primarily through 'capital deepening.' We explore the uneven distribution of these gains, the role of complementary investments, and the critical implications for economic policy. This episode explains a real academic paper in plain English for a general audience. Source paper: AI adoption, productivity - Bank for International Settlements Keywords: Artificial Intelligence, Productivity, Employment, Europe, Macroeconomics, Digital Transformation

This episode delves into a new research paper from the Bank for International Settlements, exploring how the lightning speed of high-frequency trading (HFT) influences the cost of capital for companies. We uncover how HFT can paradoxically raise capital costs for some stocks by amplifying systematic risk, while simultaneously reducing it for others through improved liquidity. Discover the crucial implications of these complex findings for financial market regulation and real economic outcomes. This episode explains a real academic paper in plain English for a general audience. Source paper: high-frequency trading - Bank for International Settlements https://www.bis.org/publ/work1290.pdf Keywords: high-frequency trading, cost of capital, financial markets, market regulation, systematic risk, financial stability

This episode breaks down a Bank of England research paper that significantly improves economic forecasting methods. It explains how economists use advanced statistical models, factor augmentation, and account for asymmetric loss and volatility to create more accurate predictions. Tune in to understand how these sophisticated adjustments enhance forecast performance across a range of economic variables. This episode explains a real academic paper in plain English for a general audience. Source paper: https://www.bankofengland.co.uk/-/media/boe/files/working-paper/2018/predictive-regressions-under-asymmetric-loss-factor-augmentation-and-model-selection.pdf Keywords: Forecasting, Macroeconomics, Asymmetric Loss, Volatility, Predictive Regressions, Central Banking

This episode breaks down a Bank of England Staff Working Paper, explaining how economists use advanced statistical models called Bayesian Vector Autoregressions (VARs). Discover how these powerful tools help central bankers and researchers forecast key economic and financial variables and understand the impact of policies like interest rate changes. This episode explains a real academic paper in plain English for a general audience. Source paper: https://www.bankofengland.co.uk/-/media/boe/files/working-paper/2018/bayesian-vector-autoregressions.pdf Keywords: Bayesian VARs, Economic Forecasting, Monetary Policy, Central Banking, Macroeconomics, Interest Rates

This episode dives into a Bank of England Staff Working Paper exploring how machine learning techniques can enhance macroeconomic forecasting. We break down the paper's findings on using diverse data sources, from traditional surveys to text-based indicators from news articles, to predict economic activity. Discover which models and data combinations yield the most accurate predictions and why the biggest gains are often seen in the short term. This episode explains a real academic paper in plain English for a general audience. Source paper: https://www.bankofengland.co.uk/-/media/boe/files/working-paper/2021/forecasting-uk-gdp-growth-with-large-survey-panels.pdf Keywords: Forecasting, Machine Learning, Macroeconomics, Big Data, Economic Prediction, Central Banking

This episode unpacks a recent Bank of England research paper on the often-overlooked 'fragility' of deep learning models in finance. We explore how subtle changes can cause AI to give vastly different *explanations* for its decisions, even when predictions are identical. Discover the significant implications this has for financial stability, risk management, and regulatory practices. This episode explains a real academic paper in plain English for a general audience. Source paper: Deep learning model fragility and - Bank of England https://www.bankofengland.co.uk/-/media/boe/files/working-paper/2023/deep-learning-model-fragility-implications-for-financial-stability-regulation.pdf Keywords: Deep learning, AI in finance, financial stability, regulation, model fragility, explainability, machine learning, risk management, central banking, FinTech

This episode breaks down a Bank of England research paper on using interpretable machine learning for economic forecasting. Discover how advanced AI models can outperform traditional methods by uncovering complex, time-varying relationships in data, specifically demonstrated through forecasting US unemployment. We explain how a new workflow makes these powerful predictions transparent and actionable for decision-makers. This episode explains a real academic paper in plain English for a general audience. Source paper: An interpretable machine learning workflow with an application to economic forecasting https://www.bankofengland.co.uk/-/media/boe/files/working-paper/2022/an-interpretable-machine-learning-workflow-with-an-application-to-economic-forecasting.pdf Keywords: economic forecasting, machine learning, interpretable AI, macroeconomics, central banking, unemployment

This podcast episode explains groundbreaking Bank of England research uncovering the financial mechanics behind Britain's 1833 slavery abolition compensation. Delve into newly analyzed archival data to understand who truly benefited from the £20 million payout to slave-owners, revealing the crucial role of London's financial institutions and intermediaries in quickly monetizing these government assets. This episode explains a real academic paper in plain English for a general audience. Source paper: The collection of slavery - Bank of England http://www.bankofengland.co.uk/education/Pages/resources/inflationtools/calculator/default.aspx Keywords: Slavery, British Empire, Financial History, Central Banking, Government Debt, Financial Markets

This episode explores a Federal Reserve Bank of New York research paper on the economic effects of legalizing mobile sports betting. We unpack how betting and consumer credit distress can spill across state borders, impacting credit scores and delinquency rates, especially for younger individuals. Understand the fiscal asymmetry created for states that haven't legalized, bearing costs without the tax benefits, all explained in plain English. This episode explains a real academic paper in plain English for a general audience. Source paper: Sports Betting Across Borders: Spatial Spillovers - Federal Reserve Bank of New York Staff Reports https://doi.org/10.59576/sr.1184 Keywords: sports betting, consumer credit, spatial spillovers, financial stability, delinquency, state taxation

This episode dives into how ordinary households form their views on inflation, revealing a surprising truth: changes in food prices matter most. Discover why the cost of your groceries profoundly influences collective inflation expectations across all demographics, more so than even energy prices. This research offers crucial insights for central banks in understanding and predicting persistent inflationary pressures.

New research reveals that rising global temperatures are significantly increasing the costs of international trade, a factor largely overlooked in climate impact assessments. This study combines historical trade and weather data to show how climate change, particularly through its effects on sea ports, makes shipping goods more expensive. Discover how ignoring this trade cost channel leads to a substantial underestimation of the true economic welfare impact of a warming planet.

This episode delves into new research exploring the Phillips curve, a key model linking inflation and economic slack. Using extensive firm-level survey data from the UK and US, economists uncover compelling evidence that the Phillips curve is convex, meaning prices respond differently to positive versus negative demand shocks. This non-linear relationship has significant implications for central banks and how monetary policy can effectively manage inflation.

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