arXiv 16 Mar 2018 · Econometrics
arXiv:1803.06401 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Todd, P. E., Wolpin, K. I (2006) Assessing the impact of a school subsidy program in Mexico: Using a social experiment to validate a dynamic behavioral model of… | 1.000 | 6 | 3 | 100% |
| 2 | Schultz, T. P (2004) School subsidies for the poor: Evaluating the Mexican Progresa poverty program | 0.811 | 4 | 2 | 100% |
| 3 | De Janvry, A., Sadoulet, E (2006) Making conditional cash transfer programs more efficient: Designing for maximum effect of the conditionality | 0.737 | 3 | 2 | 100% |
| 4 | Ahmed, N. K., Atiya, A. F., Gayar, N. E., El-Shishiny, H (2010) An empirical comparison of machine learning models for time series forecasting | 0.405 | 1 | 1 | 100% |
| 5 | Bajari, P., Nekipelov, D., Ryan, S. P., Yang, M (2015) Machine learning methods for demand estimation | 0.405 | 1 | 1 | 100% |
| 6 | Bando, R., Patrinos, H. A., Bando, R., López-Calva, L. F (2004) Child labor, school attendance, and indigenous households: Evidence from Mexico | 0.405 | 1 | 1 | 100% |
| 7 | Behrman, J. R., Sengupta, P., Todd, P (2005) Progressing through PROGRESA: an impact assessment of a school subsidy experiment in rural Mexico | 0.405 | 1 | 1 | 100% |
| 8 | Breiman, L., Friedman, J. H., Olshen, R. A., Stone, C. J (1984) Classification and Regression Trees | 0.405 | 1 | 1 | 100% |
| 9 | Gertler, P (2004) Do conditional cash transfers improve child health? evidence from PROGRESA's control randomized experiment | 0.405 | 1 | 1 | 100% |
| 10 | Hyafil, L., Rivest, R. L (1976) Constructing optimal binary decision trees is np-complete | 0.405 | 1 | 1 | 100% |
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