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Economic Models That Posit Simple Causal Explanations Predict Poorly

2018-05-23⊺07:25:04-05:00

“Economic Predictions with Big Data: The Illusion of Sparsity”
Domenico Giannone, Michele Lenza, and Giorgio E. Primiceri, Federal Reserve Bank of New York, April 2018
https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr847.pdf

The tl;dr version:

“Economic Predictions with Big Data: The Illusion of Sparsity”
Domenico Giannone, Michele Lenza, and Giorgio E. Primiceri, Liberty Street Economics, May 21, 2018
http://libertystreeteconomics.newyorkfed.org/2018/05/economic-predictions-with-big-data-the-illusion-of-sparsity.html

Seeking to explain why predictive economic models perform so poorly when applied to cases outside of their training set, the authors generate and study a large number of variant models for six economic phenomena (two in macroeconomics, two in microeconomics, and two in finance). Some of these models are sparse, in the sense that they posit that their predictions should depend on a small number of variables in the input data (the ones with the greatest predictive power) others are dense, allowing for dependence on many input variables.

Dense models are prone to overfitting. To prevent this, the training process identifies variables for which the training set provides only weak information and constrains their weights to be small so that their contributions to the models' predictions are limited (but usually nonzero).

The predictions of sparse models are easier to interpret because they generate simpler causal explanations. In dense models, it often turns out that very many factors contribute to the prediction so that the causal explanations are muddled and vary more from one instance to another.

The authors found that most of the economic phenomena that they tried to model actually have complex causal explanations, which is why the sparse models that economists have traditionally favored don't yield accurate predictions.

#economics #models #black-box-deciders

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John David Stone (havgl@unity.homelinux.net)

created June 1, 2014 · last revised December 10, 2018