ArCo: An artificial counterfactual approach for high-dimensional panel time-series data
By Carlos M. Carvalho, Ricardo P. Masini, Marcelo C. Medeiros. Journal of Econometrics, 2018
74 citations recorded by the source corpus.
We consider a new, flexible and easy-to-implement method to estimate thecausal effects of an intervention on a single treated unit when a control group is not available and which nests previous proposals in the literature. It is a two-step methodology where in the first stage, a counterfactual is estimated based on a large-dimensional set of variables from a pool of untreated units by means of shrinkage methods, such as the least absolute shrinkage and selection operator (LASSO). In the second stage, we estimate the average intervention effect on a vector of variables, which is consistent and asymptotically normal. Our results are valid uniformly over a wide class of probability laws. We show that these results hold even when the exact date of the intervention is unknown. Tests for multiple interventions and for contamination effects are derived. By a simple transformation of the variables, it is possible to test for multivariate intervention effects on several moments of the variables of interest. Existing methods in the literature usually test for intervention effects on a single variable and assume that the time of the intervention is known. In addition, high-dimensionality is frequently ignored and inference is either conducted under a set of more stringent hypotheses and/or by permutation tests. A Monte Carlo experiment evaluates the properties of the method in finite samples and compares it with other alternatives. As an application, we evaluate the effects on inflation, GDP growth, retail sales and credit of an anti tax-evasion program.
Subfields: Economics and Econometrics, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/4241c25b3b8089a0b9baca05b2e6ecdaf4a42022
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Citations in the corpus
- On the Properties of the Synthetic Control Estimator with Many Periods and Many Controls (2019, methodology): “While increasing the number of control units increases the number of parameters to be estimated, as shown by Chernozhukov et al. (2017), the non-negativity and addingup constraints work as a regularization method.”