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  5. Next Gen Nowcasting: Signatures, Distributions, and Simplified Workflows
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Next Gen Nowcasting: Signatures, Distributions, and Simplified Workflows

Paper Session

Sunday, Jan. 4, 2026 2:30 PM - 4:30 PM (EST)

Philadelphia Convention Center, 309
Hosted By: American Economic Association
  • Chair: Arthur Turrell, Bank of England

Nowcasting with Regression on Signatures

Giulia Mantoan
,
Bank of England
Lars Nesheim
,
University College London
Áureo de Paula
,
University College London
Arthur Turrell
,
Bank of England
Lingyi Yang
,
University of Oxford

Abstract

We introduce a new method of nowcasting using regression on path signatures. Path signatures capture the geometric properties of sequential data. Because signatures embed observations in continuous time, they naturally handle mixed frequencies and missing data. We also prove theoretically and with simulations that regression on signatures both subsumes the linear Kalman filter and retains the consistency properties of ordinary least squares. Nowcasting with signatures is more robust to disruptions in data series than previous methods, making it useful in stressed times (for example, during COVID-19). This approach is performant in nowcasting US GDP growth, and in nowcasting UK unemployment.

Mixed Frequency Functional VARs for Nowcasting the Income Distribution in the UK

Andrea de Polis
,
University of Strathclyde
Gary Koop
,
University of Strathclyde
Stuart McIntyre
,
University of Strathclyde
James Mitchell
,
Federal Reserve Bank of Cleveland
Ping Wu
,
University of Strathclyde

Abstract

Cross-sectional survey data on the annual income in the UK are released with more than a one-year delay, precluding real-time assessment of the effects of economic developments on inequality. This paper develops a Bayesian mixed-frequency functional Vector Autoregression (MF-fVAR) that jointly models higher-frequency macroeconomic time series with low-frequency micro-data about the annual income distribution. The MF-fVAR facilitates nowcasting and the generation of high-frequency historical estimates of the income distribution informed by developments in key macroeconomic indicators. We show that the model offers several advantages over existing methods, providing more timely and accurate insights into the relationship between macroeconomic conditions and income inequality. The paper also proposes a new way to evaluate density forecasts with functional data realizations. Real-time estimates of the income distribution are essential for effective policymaking, informing the design of timely and targeted fiscal and monetary policies to address economic instability and inequality.

Nowcasting Made Easier: A Toolbox for Economists

Baptiste Meunier
,
European Central Bank
Jan Linzenich
,
European Central Bank

Abstract

We provide a versatile nowcasting toolbox that supports three model classes (dynamic factor models, large Bayesian VAR, bridge equations) and offers methods to manage data selection and adjust for Covid-19 observations. The toolbox aims at simplifying two key tasks: creating new nowcasting models and improving the policy analysis. For model creation, the toolbox automatizes testing input variables, assessing model accuracy, and checking robustness to the Covid period. The toolbox is organized along a structured three-step approach: variable pre-selection, model selection, and Covid robustness. Non-specialists can easily follow these steps to develop high-performing models, while experts can leverage the automated tests and analyses. For regular policy use, the toolbox generates a large range of outputs to aid conjunctural analysis like news decomposition, confidence bands, alternative forecasts, and heatmaps. These multiple outputs aim at opening the black box often associated with nowcasts and at gauging the reliability of real-time predictions. We showcase the toolbox features to create a nowcasting model for global GDP growth. Overall, the toolbox aims at facilitating creation, evaluation, and deployment of nowcasting models. Code and templates are available on GitHub: https://github.com/baptiste-meunier/Nowcasting_toolbox.

Discussant(s)
Chris Kurz
,
Federal Reserve Board
Giulia Mantoan
,
Bank of England
Chad Fulton
,
Federal Reserve Board
JEL Classifications
  • C3 - Multiple or Simultaneous Equation Models; Multiple Variables
  • E1 - General Aggregative Models