Jianqing Fan, Ricardo P. Masini, Marcelo C. Medeiros
arXiv 8 Nov 2020 · Econometrics · publishedJournal of the American Statistical Association (2021) · 19 citations (OpenAlex)
arXiv:2011.03996 · PDF · DOI · OpenAlex · Extracted main text
Optimal pricing, i.e., determining the price level that maximizes profit or revenue of a given product, is a vital task for the retail industry. To select such a quantity, one needs first to estimate the price elasticity from the product demand. Regression methods usually fail to recover such elasticities due to confounding effects and price endogeneity. Therefore, randomized experiments are typically required. However, elasticities can be highly heterogeneous depending on the location of stores, for example. As the randomization frequently occurs at the municipal level, standard difference-in-differences methods may also fail. Possible solutions are based on methodologies to measure the effects of treatments on a single (or just a few) treated unit(s) based on counterfactuals constructed from artificial controls. For example, for each city in the treatment group, a counterfactual may be constructed from the untreated locations. In this paper, we apply a novel high-dimensional statistical method to measure the effects of price changes on daily sales from a major retailer in Brazil. The proposed methodology combines principal components (factors) and sparse regressions, resulting in a method called Factor-Adjusted Regularized Method for Treatment evaluation (FarmTreat). The data consist of daily sales and prices of five different products over more than 400 municipalities. The products considered belong to the sweet and candies category and experiments have been conducted over the years of 2016 and 2017. Our results confirm the hypothesis of a high degree of heterogeneity yielding very different pricing strategies over distinct municipalities.
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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 | Chernozhukov, V., K. Wüthrich, and Y. Zhu (2020) An exact and robust conformal inference method for counterfactual and synthetic controls | 1.000 | 16 | 5 | 100% |
| 2 | Masini, R. and M. Medeiros (2021+) (2021) Counterfactual analysis with artificial controls: Inference, high dimensions and nonstationarity self | 1.000 | 9 | 4 | 100% |
| 3 | Gobillon, L. and T. Magnac (2016) Regional policy evaluation: Interactive fixed effects and synthetic controls | 0.950 | 14 | 5 | 86% |
| 4 | Carvalho, C., R. Masini, and M. Medeiros (2018) Arco: An artificial counterfactual approach for high-dimensional panel time-series data | 0.928 | 20 | 7 | 80% |
| 5 | Fan, J., R. Masini, and M. Medeiros (2021) Bridging factor and sparse models self | 0.928 | 5 | 3 | 80% |
| 6 | Ahn, S. and A. Horenstein (2013) Eigenvalue ratio test for the number of factors | 0.874 | 5 | 2 | 100% |
| 7 | Fan, J., Y. Ke, and K. Wang (2020) Factor-adjusted regularized model selection self | 0.811 | 4 | 2 | 100% |
| 8 | Masini, R. and M. Medeiros (2020) Counterfactual analysis and inference with non-stationary data self | 0.811 | 4 | 2 | 100% |
| 9 | Hsiao, C., H. S. Ching, and S. K. Wan (2012) A panel data approach for program evaluation: Measuring the benefits of political and economic integration of Hong Kong with mai… | 0.644 | 2 | 2 | 100% |
| 10 | Bai, J. and S. Ng (2008) Large dimensional factor analysis | 0.644 | 2 | 2 | 100% |
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