## Nowcasting GDP - A Scalable Approach Using DFM, Machine Learning and Novel Data, Applied to European Economies

_IMF Working Papers, March 11, 2022_

## Source details

**Canonical URL:** [Nowcasting GDP - A Scalable Approach Using DFM, Machine Learning and Novel Data, Applied to European Economies](https://www.imf.org/en/publications/wp/issues/2022/03/11/nowcasting-gdp-a-scalable-approach-using-dfm-machine-learning-and-novel-data-applied-to-513703)

## Other formats

- [Markdown version](/en/publications/wp/issues/2022/03/11/nowcasting-gdp-a-scalable-approach-using-dfm-machine-learning-and-novel-data-applied-to-513703/index.md)
- [Structured JSON version](/en/publications/wp/issues/2022/03/11/nowcasting-gdp-a-scalable-approach-using-dfm-machine-learning-and-novel-data-applied-to-513703/index.json)
- [Bundle manifest](/en/publications/wp/issues/2022/03/11/nowcasting-gdp-a-scalable-approach-using-dfm-machine-learning-and-novel-data-applied-to-513703/bundle-manifest.json)

## Bibliographic details
- Authors: Jean-Francois Dauphin, Kamil Dybczak, Morgan Maneely, Marzie Taheri Sanjani, Nujin Suphaphiphat, Yifei Wang, Hanqi Zhang
- Published: March 11, 2022
- Series: IMF Working Papers
- DOI: https://doi.org/10.5089/9798400204425.001

---

### Summary and purpose
- Describes recent work to strengthen nowcasting capacity at the IMF’s European department.
- Motivates and compiles datasets of standard and nontraditional variables, such as Google search and air quality.
- Applies standard dynamic factor models (DFMs) and several machine learning (ML) algorithms to nowcast GDP growth across a heterogeneous group of European economies during normal and crisis times.
- Conclusion: Most methods significantly outperform the AR(1) benchmark model.
- Conclusion: DFMs tend to perform better during normal times.
- Conclusion: Many of the ML methods used performed strongly at identifying turning points.
- Claim: The approach is easily applicable to other countries, subject to data availability.

### Data and inputs
- Datasets include standard and nontraditional variables, explicitly mentioning Google search and air quality.
- Focus on large and varied data inputs suitable for factor models and machine learning algorithms.

### Methods and modeling approach
- Standard dynamic factor models (DFMs) are a primary modeling framework.
- Several machine learning (ML) algorithms are applied alongside DFMs.
- Benchmark comparison against an AR(1) model.

### Key findings and performance
- Most methods significantly outperform the AR(1) benchmark model.
- DFMs perform relatively better in normal (non-crisis) periods.
- Many ML methods perform strongly at identifying turning points in GDP growth.
- The framework is scalable and intended for application across a heterogeneous group of European economies.

### Applicability and limitations
- Approach is easily applicable to other countries, subject to data availability.
- Explicit focus on both normal and crisis times, highlighting differential strengths of DFMs and ML methods.

---

## Content in this bundle

- **Working Paper**
  - [Working Paper (Markdown version)](/-/media/files/publications/wp/2022/english/wpiea2022052-print-pdf.pdf.md){rel="alternate" type="text/markdown"}
  - [Working Paper (PDF)](/-/media/files/publications/wp/2022/english/wpiea2022052-print-pdf.pdf){rel="external" type="application/pdf"}

---

_Source: https://www.imf.org/en/publications/wp/issues/2022/03/11/nowcasting-gdp-a-scalable-approach-using-dfm-machine-learning-and-novel-data-applied-to-513703_