arXiv 28 Feb 2018 · Econometrics · 1 citations (OpenAlex)
arXiv:1803.00096 · PDF · DOI · OpenAlex · Extracted main text
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.
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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 | Alberto Abadie, Alexis Diamond \ Jens Hainmueller (2010) Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program | 1.000 | 10 | 5 | 100% |
| 2 | Kay H Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven… (2015) Inferring causal impact using Bayesian structural time-series models | 1.000 | 6 | 4 | 100% |
| 3 | Susan Athey \ Guido W Imbens (2017) The state of applied econometrics: Causality and policy evaluation | 1.000 | 6 | 3 | 100% |
| 4 | Nikolay Doudchenko \ Guido W Imbens (2016) Balancing, regression, difference-in-differences and synthetic control methods: A synthesis | 1.000 | 5 | 3 | 100% |
| 5 | Andreas Billmeier \ Tommaso Nannicini (2013) Assessing economic liberalization episodes: A synthetic control approach | 0.843 | 4 | 3 | 75% |
| 6 | Joshua D Angrist \ Jörn-Steffen Pischke (2008) Mostly harmless econometrics: An empiricist's companion. Princeton university press | 0.843 | 3 | 3 | 100% |
| 7 | Stephen O’Neill, Noémi Kreif, Richard Grieve, Matthew Sutton \ Jasje… (2016) Estimating causal effects: considering three alternatives to difference-in-differences estimation | 0.843 | 3 | 3 | 100% |
| 8 | Ella Getz Wold \ Ragnar Enger Juelsrud (2016) The Consequences of Increasing Risk-Based Capital Requirements for Banks: Evidence from a 2013 Policy Reform in Norway | 0.737 | 3 | 2 | 100% |
| 9 | Jerome Friedman, Trevor Hastie \ Robert Tibshirani (2011) The elements of statistical learning: Data mining, Inference and Predicition. Vol. 2, Springer | 0.644 | 2 | 2 | 100% |
| 10 | James Durbin \ Siem Jan Koopman (2012) Time series analysis by state space methods. Oxford University Press | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 34 scored citations.