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Economics

New submissions

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New submissions for Tue, 20 Mar 18

[1]  arXiv:1803.06401 [pdf, ps, other]
Title: Evaluating Conditional Cash Transfer Policies with Machine Learning Methods
Authors: Tzai-Shuen Chen
Subjects: Econometrics (econ.EM); Machine Learning (stat.ML)

This paper presents an out-of-sample prediction comparison between major machine learning models and the structural econometric model. Over the past decade, machine learning has established itself as a powerful tool in many prediction applications, but this approach is still not widely adopted in empirical economic studies. To evaluate the benefits of this approach, I use the most common machine learning algorithms, CART, C4.5, LASSO, random forest, and adaboost, to construct prediction models for a cash transfer experiment conducted by the Progresa program in Mexico, and I compare the prediction results with those of a previous structural econometric study. Two prediction tasks are performed in this paper: the out-of-sample forecast and the long-term within-sample simulation. For the out-of-sample forecast, both the mean absolute error and the root mean square error of the school attendance rates found by all machine learning models are smaller than those found by the structural model. Random forest and adaboost have the highest accuracy for the individual outcomes of all subgroups. For the long-term within-sample simulation, the structural model has better performance than do all of the machine learning models. The poor within-sample fitness of the machine learning model results from the inaccuracy of the income and pregnancy prediction models. The result shows that the machine learning model performs better than does the structural model when there are many data to learn; however, when the data are limited, the structural model offers a more sensible prediction. The findings of this paper show promise for adopting machine learning in economic policy analyses in the era of big data.

Cross-lists for Tue, 20 Mar 18

[2]  arXiv:1803.06738 (cross-list from stat.ME) [pdf, other]
Title: Large-Scale Dynamic Predictive Regressions
Subjects: Methodology (stat.ME); Econometrics (econ.EM); Statistical Finance (q-fin.ST)

We develop a novel "decouple-recouple" dynamic predictive strategy and contribute to the literature on forecasting and economic decision making in a data-rich environment. Under this framework, clusters of predictors generate different latent states in the form of predictive densities that are later synthesized within an implied time-varying latent factor model. As a result, the latent inter-dependencies across predictive densities and biases are sequentially learned and corrected. Unlike sparse modeling and variable selection procedures, we do not assume a priori that there is a given subset of active predictors, which characterize the predictive density of a quantity of interest. We test our procedure by investigating the predictive content of a large set of financial ratios and macroeconomic variables on both the equity premium across different industries and the inflation rate in the U.S., two contexts of topical interest in finance and macroeconomics. We find that our predictive synthesis framework generates both statistically and economically significant out-of-sample benefits while maintaining interpretability of the forecasting variables. In addition, the main empirical results highlight that our proposed framework outperforms both LASSO-type shrinkage regressions, factor based dimension reduction, sequential variable selection, and equal-weighted linear pooling methodologies.

Replacements for Tue, 20 Mar 18

[3]  arXiv:1710.03830 (replaced) [pdf, other]
Title: Inference on Auctions with Weak Assumptions on Information
Subjects: Econometrics (econ.EM); Computer Science and Game Theory (cs.GT); Learning (cs.LG); Statistics Theory (math.ST)
[4]  arXiv:1802.08667 (replaced) [pdf, ps, other]
Title: Double/De-Biased Machine Learning Using Regularized Riesz Representers
Comments: 15 pages; fixed several typos + updated references
Subjects: Machine Learning (stat.ML); Econometrics (econ.EM); Statistics Theory (math.ST)
[ total of 4 entries: 1-4 ]
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