Authors: Hannah O’Keeffe and Katerina Petrova
-
-
The New York Innovation Center bridges the worlds of finance, technology, and innovation and generates insights into high-value central bank-related opportunities.
Do you have a request for information and records? Learn how to submit it.
Learn about the history of the New York Fed and central banking in the United States through articles, speeches, photos and video.
-
-
-
The Governance & Culture Reform hub is designed to foster discussion about corporate governance and the reform of culture and behavior in the financial services industry.
Need to file a report with the New York Fed? Here are all of the forms, instructions and other information related to regulatory and statistical reporting in one spot.
The New York Fed works to protect consumers as well as provides information and resources on how to avoid and report specific scams.
-
-
-
The New York Innovation Center bridges the worlds of finance, technology, and innovation and generates insights into high-value central bank-related opportunities.
The growing role of nonbank financial institutions, or NBFIs, in U.S. financial markets is a transformational trend with implications for monetary policy and financial stability.
The New York Fed offers the Central Banking Seminar and several specialized courses for central bankers and financial supervisors.
-
-
Staff Reports
Component-Based Dynamic Factor Nowcast Model
Number 1152
April 2025
April 2025
JEL classification: C32, C38, C53
In this paper, we propose a component-based dynamic factor model for nowcasting GDP growth. We combine ideas from “bottom-up” approaches, which utilize the national income accounting identity through modelling and predicting sub-components of GDP, with a dynamic factor (DF) model, which is suitable for dimension reduction as well as parsimonious real-time monitoring of the economy. The advantages of the new model are twofold: (i) in contrast to existing dynamic factor models, it respects the GDP accounting identity; (ii) in contrast to existing “bottom-up” approaches, it models all GDP components jointly through the dynamic factor model, inheriting its main advantages. An additional advantage of the resulting CBDF approach is that it generates nowcast densities and impact decompositions for each component of GDP as a by-product. We present a comprehensive forecasting exercise, where we evaluate the model’s performance in terms of point and density forecasts, and we compare it to existing models (e.g. the model of Almuzara, Baker, O’Keeffe, and Sbordone (2023)) currently used by the New York Fed, as well as the model of Higgins (2014) currently used by the Atlanta Fed. We demonstrate that, on average, the point nowcast performance (in terms of RMSE) of the standard DF model can be improved by 15 percent and its density nowcast performance (in terms of log-predictive scores) can be improved by 20 percent over a large historical sample.
Author Disclosure Statement(s)
Katerina Petrova
The author declares that she has no relevant or material financial interests that relate to the research described in this paper. Prior to circulation, this paper was reviewed in accordance with the Federal Reserve Bank of New York review policy, available at https://www.newyorkfed.org/research/staff_reports/index.html.
Hannah O’Keeffe
The author declares that she has no relevant or material financial interests that relate to the research described in this paper. Prior to circulation, this paper was reviewed in accordance with the Federal Reserve Bank of New York review policy, available at https://www.newyorkfed.org/research/staff_reports/index.html.
The author declares that she has no relevant or material financial interests that relate to the research described in this paper. Prior to circulation, this paper was reviewed in accordance with the Federal Reserve Bank of New York review policy, available at https://www.newyorkfed.org/research/staff_reports/index.html.
Hannah O’Keeffe
The author declares that she has no relevant or material financial interests that relate to the research described in this paper. Prior to circulation, this paper was reviewed in accordance with the Federal Reserve Bank of New York review policy, available at https://www.newyorkfed.org/research/staff_reports/index.html.
Suggested Citation:
O’Keeffe, Hannah, and Katerina Petrova. 2025. “Component-Based Dynamic Factor Nowcast Model.” Federal Reserve Bank of New York Staff Reports, no. 1152, April. https://doi.org/10.59576/sr.1152
O’Keeffe, Hannah, and Katerina Petrova. 2025. “Component-Based Dynamic Factor Nowcast Model.” Federal Reserve Bank of New York Staff Reports, no. 1152, April. https://doi.org/10.59576/sr.1152
By continuing to use our site, you agree to our Terms of Use and Privacy Statement. You can learn more about how we use cookies by reviewing our Privacy Statement. 
