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© 2022 International Monetary Fund
WP/22/52
IMF Working Paper
European Department
Nowcasting GDP A Scalable Approach Using DFM, Machine Learning and Novel Data, Applied to European Economies
Prepared by Jean-Francois Dauphin, Kamil Dybczak, Morgan Maneely, Marzie Taheri Sanjani, Nujin Suphaphiphat, Yifei Wang, and Hanqi Zhang
March 2022
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 describes recent work to strengthen nowcasting capacity at the IMF’s European department. It motivates and compiles datasets of standard and nontraditional variables, such as Google search and air quality. It applies standard dynamic factor models (DFMs) and several machine learning (ML) algorithms to now cast GDP growth across a heterogenous group of European economies during normal and crisis times. Most of our methods significantly outperform the AR(1) bench mark model. Our DFMs tend to perform better during normal times while many of the ML methods we used performed strongly at identifying turning points. Our approach is easily applicable to other countries, subject to data availability.
| JEL Classification Numbers: | E52,C33,C53 |
| Keywords: | Nowcasting, Factor Model, Machine Learning, Large Data Sets |
| Author’s E-Mail Address: | jdauphin@imf.org; kdybczak@imf.org; mmaneely@imf.org; nsuphaphiphat@imf.org; mtaherisanjani@imf.org; ywang6@imf.org; hqzhang@nyu.edu |
| JEL Classification Numbers: | E52,C33,C53 |
| Keywords: | Nowcasting, Factor Model, Machine Learning, Large Data Sets |
| Author’s E-Mail Address: | jdauphin@imf.org; kdybczak@imf.org; mmaneely@imf.org; nsuphaphiphat@imf.org; mtaherisanjani@imf.org; ywang6@imf.org; hqzhang@nyu.edu |
Title Page
WORKING PAPERS
Nowcasting GDP
A Scalable Approach Using DFM, Machine Learning and Novel Data, Applied to European Economies
Prepared by Jean-Francois Dauphin, Kamil Dybczak, Morgan Maneely, Marzie Taheri Sanjani, Nujin Suphaphiphat, Yifei Wang, and Hanqi Zhang 1
Contents
Glossary
Introduction
Literature Review
Data
Models and Methodology
DFMs
Machine Learning
Assessment of Predictive Performance
Results
Summary and Conclusion
Appendices
1. Nowcasting Variables by Country
2. Sample Results—Hungary
3. An Integrated Tool
4. Machine Learning Algorithms
1. Regularized Regression methods
2. Support Vector Machine
3. Random Forest
4. Neural Network
5. Indicators of predictive accuracy: Models by Country
References
FIGURES
Figure 1. Examples of Model Performances
TABLES
Table 1. A Brief Introduction to ML Algorithms
Table 2. Full Sample- Model Performance (RMSE) Until 2021Q1
Table 3. Pre-COVID Samples- Model Performance (RMSE) Until 2019Q4
Table 4. During COVID-19 Sample- Model Performance (RMSE) Between 2020Q1-2021Q1
Glossary
| AR | Auto Regressive |
| CNN | Convolutional Neural Network |
| COVID | Coronavirus Disease |
| CPI | Consumer Price Index |
| DFM | Dynamic Factor Model |
| ECB | European Central Bank |
| EM | Expectation Maximization |
| GDP | Gross Domestic Product |
| GPReg | Gaussian Process Regression |
| IMF | International Monetary Fund |
| LASSO | Least Absolute Shrinkage and Selection Operator |
| Lin Reg | Linear Regression |
| LSTM | Long Short-Term Memory |
| MAE | Mean Absolute Error |
| MDA | Mean Directional Accuracy |
| MIDAS | Mixed Data Sampling |
| ML | Machine Learning |
| NN | Neural Network |
| OECD | Organization for Economic Co-operation and Development |
| OLS | Ordinary Least Squares |
| ReLU | Rectifier Linear Unit |
| RF | Random Forest |
| RMSE | Root Mean Squared Error |
| RNN | Recursive Neural Networks |
| SVM | Support Vector Machine |
| VAR | Vector Autoregressive Model |
| WEI | Weekly Economic Index |
The author(s) would like to thank participants of the IMF Big Data Talks and European Department seminars for suggestions. All errors and omissbns are our own.