Working Papers

The Unattractiveness of Indeterminate Dynamic Equilibria, CEPR Discussion Paper, No. DP16822, with Paul Beaudry and Martin Ellison. 

Macroeconomic forces that generate multiple equilibria often support locally-indeterminate dynamic equilibria in which a continuum of perfect foresight paths converge towards the same steady state. The set of rational expectations equilibria (REE) in such environments can be very large, although the relevance of many of them has been questioned on the basis that they may not be learnable. In this paper we document the existence of a learnable REE in such situations. However, we show that the dynamics of this learnable REE do not resemble perturbations around any of the convergent perfect foresight paths. Instead, the learnable REE treats the locally-indeterminate steady state as unstable, in contrast to it resembling a stable attractor under perfect foresight.

International Trends in Senescent Mortality: Implications for Life Expectancy, Lifespan and Lifespan Equality, with Andrew Scott

Whether past improvements in life expectancy, lifespan and lifespan equality can continue depends on the rate of further reductions in senescent mortality. Using data from 1900 for 41 countries and a dynamic Bayesian state space model allowing for differential mortality trends at different ages we examine historical drivers of change and make projections for life expectancy and related variables. We find: i) changes in the speed and timing of ageing have both driven life expectancy improvements with the latter now increasingly dominant ii) no general sign of an approaching limit to lifespan iii) the optimal forecast for best practice life expectancy is a continuation of past trends but for most countries trend growth continues at a slower rate iv) the past positive relationship between life expectancy and lifespan equality will lessen and even turn negative in some countries. The United States is a notable exception to these findings and is subject to adverse trends which are projected to continue. Although expected to continue to see a rise in the modal age of death the U.S is also expected to see a rise in lifespan inequality driven by an expansion rather than a compression of mortality rates.

A Method to Scale-Up Interpretative Qualitative Analysis, with an Application to Aspirations in Cox’s Bazaar, Bangladesh, World Bank Policy Research Working Papers, with Vijayendra Rao, Monica Biradavolu, Aditya Chhabra, Arshia Haque, Afsana Khan and Nandini Krishnan. Methodology implemented in iQual Python package. 

Blog post at Development Impact.

The qualitative analysis of open-ended interviews has found limited use in economics. This is partly because the interpretative, nuanced human reading of text and coding that it requires is time consuming and hard. This paper presents a method with which to extend a small set of interpretative human-codes to a much larger set of documents using natural language processing and thus analyze qualitative data at scale. Through a series of simulations the authors find that extending the sample of interviews, rather than the human-coded training set, is likely to be optimal. The paper shows how to assess the robustness and reliability of this approach and applies it to analyze 2,200 open-ended interviews on parent’s aspirations for children from Rohingya refugees and their Bangladeshi hosts in Cox’s Bazaar, Bangladesh. It draws on work in anthropology and philosophy to expand conceptions of aspirations in economics to distinguish between material goals, moral and religious values, and navigational capacity—the ability to achieve goals and aspirations, showing that they have very different correlates.

Financial News Media and Volatility: is there more to Newspapers than News?, (Revise and Resubmit, Journal of Financial Markets)

Does media coverage of a firm have a causal effect on the volatility of its stock price and, if so, is this of aggregate importance? This paper identifies a robust link between media coverage in the Financial Times print newspaper and a firm's intra-day stock price volatility. This effect is not driven by persistence in volatility or anticipation of future newsworthy events, but is explained by an increase in trading volume, supporting a salience interpretation. The effect spills over into firms related by the structure of the production network, but does not affect the aggregate level of volatility. 

Nowcasting euro area GDP with news sentiment: a tale of two crises, ECB Working Paper Series, No 2616, with Eleni Kalamara and Lorena Saiz (Revise and Resubmit, Journal of Applied Econometrics)

This paper shows that newspaper articles contain timely economic signals that can materially improve nowcasts of real GDP growth for the euro area. Our text data is drawn from fifteen popular European newspapers, that collectively represent the four largest Euro area economies, and are machine translated into English. Daily sentiment metrics are created from these news articles and we assess their value for nowcasting. By comparing to competitive and rigorous benchmarks, we find that newspaper text is helpful in nowcasting GDP growth especially in the first half of the quarter when other lower-frequency soft indicators are not available. The choice of the sentiment measure matters when tracking economic shocks such as the Great Recession and the Great Lockdown. Non-linear machine learning models can help capture extreme movements in growth, but require sufficient training data in order to be effective so become more useful later in our sample. 

Multi-dimensional uncertainty and central bank communication: what do central bankers talk about and why? 

Peter Sinclair prize (1st place) for best paper at 9th MMF Society PhD conference. Featured on The Finance Bro podcast. 

When communicating with the public about the state of the economy, central bankers not only need to decide on the messages they want to convey, but also on how much to focus on various relevant economic variables. A natural assumption is that central banks should focus more on variables where their communication will be more useful. This paper proposes a model of communication in the context of multi-dimensional uncertainty to make this idea of usefulness concrete. By quantifying central bank focus on and uncertainty around various macroeconomic variables, I show that published minutes of the Federal Reserve's Open Market Committee follow the co-movement patterns predicted by the model, while speeches made by committee members do not. Finally, an event study approach shows that the publication of both minutes and speeches can influence the focus and variable-specific tone of media coverage, with speeches having a greater impact than minutes. Even if agents are not directly exposed to central bank communication, a central bank may still be able to transmit their focus on particular dimensions of the economy through media coverage. 


International Gains to Achieving Healthy Longevity (2023) in Cold Spring Harbor Perspectives in Medicine, with Andrew Scott, Martin Ellison and David Sinclair. Working paper version. Code available here

Utilizing economic tools, we evaluate the gains from improving the relationship between biological and chronological age in dollar terms. We show that the gains to individuals are substantial because targeting aging exploits synergies between health and life expectancy and the complementarities across different diseases. Gains are boosted by improvements in life expectancy and a rising number of older people. We compute the value of slowing aging in a range of countries and estimate that increasing life expectancy by 1 year has an annual benefit of ∼4%–5% of gross domestic product (GDP). Augmenting GDP with these measures of health gains reveals the growing importance of achieving healthy longevity as a means of boosting welfare, with the need being particularly acute in the United States.

An Adaptive Dynamical Model of Default Contagion (2022) in Quantitative Finance (1-11)with Damian Smug, Peter Ashwin and Didier Sornette 

We present a model of the dynamics of the contagion in financial networks. We assume that the health of a financial institution is described by a single variable represent net worth as a proportion of asset holdings, that becomes zero at default. We argue that differences in the growth of assets and liabilities can give a stable defaulted as well as a stable healthy state. Stochastic balance sheet shocks can push an institution to the bankruptcy state and lead to further bankruptcy cascades. We introduce contagion between institutions by adapting the shape of the potential landscape so as to make it easier to default given others defaulted shortly beforehand, motivated by links between institutions' balance sheets. The introduced model provides a microscopic dynamical description of the default process, since the default events are constructed via a stochastic dynamical process, rather than just point event modelled by a point process. The correspondence that we find provides a stochastic micro-foundation of the models of defaults' intensity.

Bayesian Topic Regression for Causal Inference (2021) in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (pp. 8162-8188), with Maximillian Ahrens, Jan-Peter Calliess and Vu Nguyen. Methodology implemented in BTR Julia package.  

Causal inference using observational text data is becoming increasingly popular in many research areas. This paper presents the Bayesian Topic Regression (BTR) model that uses both text and numerical information to model an outcome variable. It allows estimation of both discrete and continuous treatment effects. Furthermore, it allows for the inclusion of additional numerical confounding factors next to text data. To this end, we combine a supervised Bayesian topic model with a Bayesian regression framework and perform supervised representation learning for the text features jointly with the regression parameter training, respecting the Frisch-Waugh-Lovell theorem. Our paper makes two main contributions. First, we provide a regression framework that allows causal inference in settings when both text and numerical confounders are of relevance. We show with synthetic and semi-synthetic datasets that our joint approach recovers ground truth with lower bias than any benchmark model, when text and numerical features are correlated. Second, experiments on two real-world datasets demonstrate that a joint and supervised learning strategy also yields superior prediction results compared to strategies that estimate regression weights for text and non-text features separately, being even competitive with more complex deep neural networks.

Policy Papers

 Central Bank Hawkishness in EM — Tracking and Trading the Shifts (2022) in Global Markets Analyst, Goldman Sachs, with Kamakshya Trivedi and Davide Crosilla

The ECB’s communication on the economic outlook: a comparative analysis (2021) , in Economic Bulletin Issue 8, 2021, with Maarten Dossche, Katrin Forster van Aerssen, Ramon Gomez-Salvador, Eleni Kalamara and Beatrice Pierluigi

Summary of Proceedings: 2nd Stranded Assets Forum at Waddesdon Manor (2014), with Ben Caldecott and Dane Rook