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Nowcasting US GDP Using Tree-Based Ensemble Models and Dynamic Factors.

Barış Soybilgen1, Ege Yazgan1

  • 1Istanbul Bilgi University, Istanbul, Turkey.

Computational Economics
|January 13, 2021
PubMed
Summary
This summary is machine-generated.

Machine learning models accurately nowcast US GDP growth using dynamic factors from economic data. Tree-based models outperformed linear methods, with real variables being most influential for GDP prediction.

Keywords:
BaggingBoostingDynamic Factor ModelMachine LearningNowcastingRandom forests

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Area of Science:

  • Economics
  • Econometrics
  • Machine Learning

Background:

  • Accurate and timely estimation of Gross Domestic Product (GDP) growth is crucial for economic policy and financial markets.
  • Traditional forecasting models face challenges with high-dimensional and time-varying economic data.
  • The ragged edge problem, where the latest data is incomplete, complicates real-time GDP nowcasting.

Purpose of the Study:

  • To nowcast quarter-over-quarter US GDP growth rates from 2000Q2 to 2018Q4.
  • To evaluate the performance of tree-based ensemble machine learning models against linear dynamic factor models.
  • To identify the influence of different economic variable groups on GDP nowcasting.

Main Methods:

  • Utilized dynamic factor models to address the ragged edge problem and reduce data dimensionality.
  • Extracted dynamic factors from 10 groups of financial and macroeconomic variables.
  • Employed tree-based ensemble machine learning models: bagged decision trees, random forests, and stochastic gradient tree boosting.

Main Results:

  • Tree-based ensemble models generally outperformed linear dynamic factor models in nowcasting US GDP.
  • Dynamic factors derived from real economic variables demonstrated the most significant influence on machine learning model predictions.
  • Factors from financial and price variables became more important for GDP prediction post-2008 financial crisis, linked to monetary policy.

Conclusions:

  • Tree-based machine learning models offer a robust alternative for nowcasting US GDP, outperforming traditional linear methods.
  • The predictive power of different economic factors for GDP varies over time and is influenced by major economic events and policy changes.
  • Understanding the differential impact of real, financial, and price factors is key to improving real-time economic forecasting.