IMF-Nowscasting中的参数扩散：问题与途径——在中国实际GDP Nowscasted中的应用（英）

Parameter Proliferation in Nowcasting: Issues and Approaches An Application to Nowcasting China’s Real GDP Paul Cashin, Fei Han, Ivy Sabuga, Jing Xie, and Fan Zhang WP/25/217 IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management. 2025 OCT© 2025 International Monetary Fund WP/25/217IMF Working Paper Institute for Capacity Development Parameter Proliferation in Nowcasting: Issues and Approaches—An Application to Nowcasting China’s Real GDP Prepared by Paul Cashin, Fei Han, Ivy Sabuga, Jing Xie, and Fan Zhang* Authorized for distribution by Natan Epstein October 2025 IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management. ABSTRACT: This paper evaluates three approaches to address parameter proliferation issue in nowcasting: (i) variable selection using adjusted stepwise autoregressive integrated moving average with exogenousvariables (AS-ARIMAX); (ii) regularization in machine learning (ML); and (iii) dimensionality reduction via principal component analysis (PCA). Utilizing 166 variables, we estimate our models from 2007Q2 to 2019Q4 using rolling-window regression, while applying these three approaches. We then conduct a pseudo out-of-sample performance comparison of various nowcasting models—including Bridge, MIDAS, U-MIDAS, dynamic factor model (DFM), and machine learning techniques including Ridge Regression, LASSO, and Elastic Net to predict China's annualized real GDP growth rate from 2020Q1 to 2023Q1. Our findings suggest that the LASSO method outperform all other models, but only when guided by economic judgment and sign restrictions in variable selection. Notably, simpler models like Bridge with AS-ARIMAX variable selection yield reliable estimates nearly comparable to those from LASSO, underscoring the importance of effective variable selection in capturing strong signals. JEL Classification Numbers: C18, C53 Keywords: China; GDP; Nowcasting Authors’ E-Mail Address: PCashin@imf.org, FHan@imf.org, ISabuga@imf.org, JX3770@princeton.edu, FZhang@imf.org WORKING PAPERS Parameter Proliferation in Nowcasting: Issues and Approaches An Application to Nowcasting China’s Real GDP Prepared by Paul Cashin, Fei Han, Ivy Sabuga, Jing Xie, and Fan Zhang1 1 We are grateful for comments from Mr. Sam Ouliaris, Mr. Dimitre Milkov, and Ms. Hongbo Wang. The views expressed in this paper are those of the authors and not necessarily those of the International Monetary Fund, or its Executive Board. IMF WORKING PAPERS Parameter Proliferation in Nowcasting: Issues and Approaches INTERNATIONAL MONE