Machine Learning in Modern Econometrics

What is the role of machine learning techniques in modern econometrics?

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What is the role of machine learning techniques in modern econometrics?

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What-is-the-role-of-machine-learning-techniques-in-modern-econometrics

1. Introduction

1. Introduction

Machine learning (ML) methods are increasingly complementing modern econometrics, demonstrating a broader shift away from purely causal inference to prediction and pattern finding. In contrast to econometrics, where inferences are typically driven by theory, ML is a data-driven approach that makes fewer assumptions and allows researchers to gather and analyze complex, high-dimensional, unstructured datasets (Athey, 2018).

Integration-of-Machine-Learning-in-Econometrics

2. Integration of Machine Learning in Econometrics

Machine learning is a great way to improve econometrics in:

  • Enhancing the prediction accuracy of macroeconomic forecasts.
  • Facilitating dimension reductions for variable selections in high-dimensional situations.
  • Revealing hidden data structures through unsupervised learning.
  • Applying text analytics to enhance sentiment analysis of economic sentiment from newspapers, social media, and reports (Gentzkow et al.,2019).

These capabilities enhance the generally comprehensive econometric models used for interpretations and considerations for policy in impact evaluations (Mullainathan & Spiess, 2017).

Key Machine Learning Techniques

3. Key Machine Learning Techniques

3.1 LASSO and Ridge Regression

Regularization methods- LASSO (Tibshirani, 1996) and Ridge Regression (Hoerl & Kennard, 1970)- are useful methodologies when working with high-dimensional econometric models since we might be facing multicollinearity or overfitting.

  • LASSO introduces shrinkage and variable selection into models as it penalizes the absolute size of the coefficients.
  • Ridge regressionpenalizes the L2 and reduces the friction of predictors when they’re identified.

Example: LASSO has been employed in housing price models in which it identified key features from a theoretical thousands of variables (Belloni et al., 2014).

3.2 Tree-Based Methods: Random Forests and Gradient Boosting

Random forests and boosting techniques (XGBoost) are forms of ensemble-based learning that enhance predictive accuracy.

  • Random forests aggregating bootstrapped datasets and trees provide greater model robustness
  • Gradient boosting methods like XGBoost build accuracy by eliminating predictive error in sequence (Chen & Guestrin, 2016).

Example: XGBoost is becoming increasingly popular within the fields of credit scoring and financial risk modelling (Lessmann et al, 2015).

3.3 Clustering algorithms

K-means and DBSCAN will help to uncover any patterns present within the unlabelled data to help determine future economic segmentation strategies.

Example: Clustering algorithms are identified for discovering the districts in India, according to economic measures, that would enable a more discrete rollout of policy (Agarwal, Ghosh & Ghosh, 2020).

3.4 Natural Language Processing (NLP)

NLP results in taking unstructured text and producing relevant, measurable data in several methods, such as sentiment analysis or topic modelling.

Example: Taking the sentiment from messages from their central banks, or even financial news data, may serve to predict changes in interest or volatility (Hansen et al., 2018).

Comparative Advantages and Limitations

4. Comparative Advantages and Limitations

Feature Machine Learning Machine Learning
Objective Prediction Causal Inference
Model Flexibility High (non-linear, interaction-friendly) Moderate (requires specification)
Interpretability Often limited (black-box models) High (structural model focus)
Data Requirements Large-scale, possibly unstructured Structured, clean data

ML performed well for forecasting and classification; however, the lack of interpretability often limits their utility as a stand-alone input into the design of economic policy (Athey & Imbens, 2019).

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5. Applications and Examples

  • Labour economics: Random forests used to predict unemployment duration (Brynjolfsson & McElheran, 2016).
  • Consumer credit: Credit risk prediction by XGBoost is better than by logistic regression (Khandani et al., 2010).
  • Macroeconomics: Nowcasting GDP using real-time financial data is better with machine learning methods (Ng, 2014).
  • Sentiment-based forecasting: Consumer spending forecast using public sentiment NLP models (Gentzkow et al., 2019).

6. Conclusion

Machine learning techniques have changed the way empirical economists can do their work, providing big, scalable, flexible, and powerful prediction and exploratory tools in combination with econometric models that have some level of interpretability, enabling more evidence-based, richer decisions as academics or policy-makers. Together, the potential of machine learning and econometrics can lead to a fuller and more nuanced analysis of modern-day economics.

References

7. References

  • Agarwal, A., Ghosh, A., & Ghosh, S. (2020). Regional disparities in India: A cluster analysis approach. Economic and Political Weekly, 55(20), 40–46.
  • Athey, S. (2018). The impact of machine learning on economics. The economics of artificial intelligence: An agenda, 507-547.
  • Athey, S., & Imbens, G. W. (2019). Machine learning methods that economists should know about. Annual Review of Economics, 11, 685–725.
  • Belloni, A., Chernozhukov, V., & Hansen, C. (2014). High-dimensional methods and inference on structural and treatment effects. Journal of Economic Perspectives, 28(2), 29–50.
  • Brynjolfsson, E., & McElheran, K. (2016). Data in action: Data-driven decision making in US manufacturing. AEA Papers and Proceedings, 106, 133–139.
  • Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 785–794).
  • Gentzkow, M., Kelly, B., & Taddy, M. (2019). Text as data. Journal of Economic Literature, 57(3), 535–574.
  • Hansen, S., McMahon, M., & Prat, A. (2018). Transparency and deliberation within the FOMC: A computational linguistics approach. The Quarterly Journal of Economics, 133(2), 801–870.
  • Khandani, A. E., Kim, A. J., & Lo, A. W. (2010). Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance, 34(11), 2767–2787.
  • Mullainathan, S., & Spiess, J. (2017). Machine learning: An applied econometric approach. Journal of Economic Perspectives, 31(2), 87–106.
  • Ng, S. (2014). Viewpoint: Boosting GDP forecasting. Canadian Journal of Economics/Revue canadienne d’économique, 47(1), 1–29.
  • Tibshirani, R. (1996). Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288.

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