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Recession Prediction using Machine Learning | by Terrence Zhang | Towards Data Science
Source: towardsdatascience.com
Article summary:
1. This article presents a machine learning approach for predicting U.S. recessions over 6-month, 12-month, and 24-month time frames using employment, inflation, interest rate, and market indicators.
2. The feature selection process was guided by economic fundamentals and domain knowledge to avoid overfitting the model to prior data.
3. The testing process was designed to account for the time series nature of the data and used NBER recession definitions to label outputs.
Article analysis:
The article is generally trustworthy and reliable in its presentation of a machine learning approach for predicting U.S. recessions over 6-month, 12-month, and 24-month time frames using employment, inflation, interest rate, and market indicators. The feature selection process is well explained with clear reasoning behind each choice made in order to avoid overfitting the model to prior data. Additionally, the testing process is designed to account for the time series nature of the data and uses NBER recession definitions to label outputs which adds credibility to the results presented in the article.
The only potential bias that could be present in this article is that it does not explore any counterarguments or alternative approaches that could be taken when predicting recessions using machine learning models. Additionally, there are no risks noted in regards to implementing such a model which could lead readers astray if they were unaware of any potential risks associated with such an approach.
In conclusion, this article is generally trustworthy and reliable but could benefit from exploring alternative approaches or counterarguments as well as noting any potential risks associated with implementing such a model when predicting recessions using machine learning models.