arXiv 9 Jun 2026 · Econometrics
arXiv:2606.11047 · PDF · DOI · OpenAlex · Extracted main text
The purpose of this paper is to consider whether and how panel data can be used to estimate individual demand, as opposed to market-level demand, while accounting for simultaneity resulting from prices being determined in markets. We consider linear demand models and random coefficient demand models, together with linear supply models. We find that the bias of individual demand estimates obtained using familiar panel data methods, like differencing, disappears as the number of consumers in each market grows, as long as the time-varying, i.e. idiosyncratic, component of preferences is orthogonal to the unobserved, time-varying component of supply. This approximate control is assumed in many panel discrete choice models and is plausible in other models where idiosyncratic preferences represent random variation in preferences over time. Macroeconomic effects can be allowed for by including regressors characterizing time effects, such as trends and time period dummies, or fixed time effects.
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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 | Berry, S., J. Levinsohn, and A. Pakes (1995) Automobile prices in market equilibrium | 0.644 | 2 | 2 | 100% |
| 2 | Kennan, J (1989) Simultaneous equations bias in disaggregated econometric models | 0.644 | 2 | 2 | 100% |
| 3 | Berry, S., J. Levinsohn, and A. Pakes (2004) Differentiated products demand systems from a combination of micro and macro data: The new car market | 0.405 | 1 | 1 | 100% |
| 4 | Berry, S. T. and P. A. Haile (2014) Identification in differentiated products markets using market level data | 0.405 | 1 | 1 | 100% |
| 5 | Berry, S. T. and P. A. Haile (2024) Nonparametric identification of differentiated products demand using micro data | 0.405 | 1 | 1 | 100% |
| 6 | Chamberlain, G (1984) Panel data | 0.405 | 1 | 1 | 100% |
| 7 | Chernozhukov, V., I. Fernández-Val, J. Hahn, and W. Newey (2013) Average and quantile effects in nonseparable panel models | 0.405 | 1 | 1 | 100% |
| 8 | Chernozhukov, V., J. A. Hausman, and W. K. Newey (2019) Demand analysis with many prices | 0.405 | 1 | 1 | 100% |
| 9 | Chernozhukov, V., B. Deaner, Y. Gao, J. A. Hausman, and W. Newey (2025) Fisher-Schultz lecture: Linear estimation of structural and causal effects for nonseparable panel data | 0.405 | 1 | 1 | 100% |
| 10 | Dubois, P., R. Griffith, and M. O’Connell (2020) How well targeted are soda taxes? | 0.405 | 1 | 1 | 100% |
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