## Parameter Proliferation in Nowcasting: Issues and Approaches—An Application to Nowcasting China’s Real GDP

_IMF Working Papers, October 24, 2025_

## Source details

**Canonical URL:** [Parameter Proliferation in Nowcasting: Issues and Approaches—An Application to Nowcasting China’s Real GDP](https://www.imf.org/en/publications/wp/issues/2025/10/24/parameter-proliferation-in-nowcasting-issues-and-approaches-an-application-to-nowcasting-571013)

## Other formats

- [Markdown version](/en/publications/wp/issues/2025/10/24/parameter-proliferation-in-nowcasting-issues-and-approaches-an-application-to-nowcasting-571013/index.md)
- [Structured JSON version](/en/publications/wp/issues/2025/10/24/parameter-proliferation-in-nowcasting-issues-and-approaches-an-application-to-nowcasting-571013/index.json)
- [Bundle manifest](/en/publications/wp/issues/2025/10/24/parameter-proliferation-in-nowcasting-issues-and-approaches-an-application-to-nowcasting-571013/bundle-manifest.json)

## Bibliographic details
- Authors: Paul Cashin, Fei Han, Ivy Sabuga, Jing Xie, Fan Zhang
- Published: October 24, 2025
- Series: IMF Working Papers
- DOI: https://doi.org/10.5089/9798229027212.001

---

### Research objective and scope
- Evaluate three approaches to address parameter proliferation in nowcasting:
  - Variable selection using adjusted stepwise autoregressive integrated moving average with exogenous variables (AS-ARIMAX).
  - Regularization in machine learning (ML).
  - Dimensionality reduction via principal component analysis (PCA).
- Empirical application: nowcasting China's annualized real GDP growth rate.
- Data and estimation window:
  - Utilizes 166 variables.
  - Models estimated from 2007Q2 to 2019Q4 using rolling-window regression.
  - Pseudo out-of-sample comparison for 2020Q1 to 2023Q1.

### Methodology and models compared
- Variable-selection approach: AS-ARIMAX implemented within Bridge-type models.
- Regularization approaches: Ridge Regression, LASSO, and Elastic Net.
- Dimensionality reduction approach: principal component analysis (PCA) feeding into dynamic factor models (DFM).
- Nowcasting model families evaluated:
  - Bridge
  - MIDAS
  - U-MIDAS
  - Dynamic factor model (DFM)
  - Machine learning techniques (Ridge Regression, LASSO, Elastic Net)
- Evaluation framework:
  - Rolling-window regression estimation over 2007Q2–2019Q4.
  - Pseudo out-of-sample performance comparison over 2020Q1–2023Q1.

### Key findings
- LASSO results:
  - The LASSO method outperforms all other models, but only when guided by economic judgment and sign restrictions in variable selection.
- Simpler models:
  - Bridge models combined with AS-ARIMAX variable selection yield reliable estimates nearly comparable to those from LASSO.
  - Effective variable selection is crucial for capturing strong signals, enabling simpler methods to perform well.
- Implication on approaches:
  - Regularization (LASSO) can dominate when combined with domain knowledge and sign constraints.
  - Dimensionality reduction via PCA and other techniques may be less effective than targeted variable selection in this application.

### Practical takeaways and recommended practices
- Prioritize effective variable selection to capture strong predictive signals; AS-ARIMAX is a viable approach for Bridge-type models.
- When using ML regularization methods (e.g., LASSO), incorporate economic judgment and sign restrictions to improve performance.
- Simpler models with disciplined variable selection can achieve near-state-of-the-art nowcasting accuracy without full-scale ML pipelines.

*Source: IMF Working Paper "Parameter Proliferation in Nowcasting: Issues and Approaches—An Application to Nowcasting China’s Real GDP" by Paul Cashin, Fei Han, Ivy Sabuga, Jing Xie, and Fan Zhang (October 24, 2025).*

---

## Content in this bundle

- **Working Paper**
  - [Working Paper (Markdown version)](/-/media/files/publications/wp/2025/english/wpiea2025217-source-pdf.pdf.md){rel="alternate" type="text/markdown"}
  - [Working Paper (PDF)](/-/media/files/publications/wp/2025/english/wpiea2025217-source-pdf.pdf){rel="external" type="application/pdf"}

---

_Source: https://www.imf.org/en/publications/wp/issues/2025/10/24/parameter-proliferation-in-nowcasting-issues-and-approaches-an-application-to-nowcasting-571013_