 Synthetic Control Methods and Big DataDaniel Kinn Abstract: Many macroeconomic policy questions may be assessed in a case study framework, where the time series of a treated unit is compared to a counterfactual constructed from a large pool of control units. I provide a general framework for this setting, tailored to predict the counterfactual by minimizing a tradeoff between underfitting (bias) and overfitting (variance). The framework nests recently proposed structural and reduced form machine learning approaches as special cases. Furthermore, difference-in-differences with matching and the original synthetic control are restrictive cases of the framework, in general not minimizing the bias-variance objective. Using simulation studies I find that machine learning methods outperform traditional methods when the number of potential controls is large or the treated unit is substantially different from the controls. Equipped with a toolbox of approaches, I revisit a study on the effect of economic liberalisation on economic growth. I find effects for several countries where no effect was found in the original study. Furthermore, I inspect how a systematically important bank respond to increasing capital requirements by using a large pool of banks to estimate the counterfactual. Finally, I assess the effect of a changing product price on product sales using a novel scanner dataset. Date: 2018-02 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1803.00096 Access Statistics for this paper More papers in Papers from arXiv.org |
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