Demand Analysis with Many Prices / Victor Chernozhukov, Jerry A. Hausman, Whitney K. Newey.
Material type:![Text](/opac-tmpl/lib/famfamfam/BK.png)
- C13 - Estimation: General
- C14 - Semiparametric and Nonparametric Methods: General
- C21 - Cross-Sectional Models • Spatial Models • Treatment Effect Models • Quantile Regressions
- C23 - Panel Data Models • Spatio-temporal Models
- C55 - Large Data Sets: Modeling and Analysis
- D12 - Consumer Economics: Empirical Analysis
- Hardcopy version available to institutional subscribers
Item type | Home library | Collection | Call number | Status | Date due | Barcode | Item holds | |
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Working Paper | Biblioteca Digital | Colección NBER | nber w26424 (Browse shelf(Opens below)) | Not For Loan |
November 2019.
From its inception, demand estimation has faced the problem of "many prices." This paper provides estimators of average demand and associated bounds on exact consumer surplus when there are many prices in cross-section or panel data. For cross-section data we provide a debiased machine learner of consumer surplus bounds that allows for general heterogeneity and solves the "zeros problem" of demand. For panel data we provide bias corrected, ridge regularized estimators of average coefficients and consumer surplus bounds. In scanner data we find smaller panel elasticities than cross-section and that soda price increases are regressive.
Hardcopy version available to institutional subscribers
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