The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Automobile Prices in Market Equilibrium
Abstract
This paper develops techniques for empirically analyzing demand and supply in differentiated product markets and then applies these techniques to the U.S. automobile industry. The authors' framework enables one to obtain estimates of demand and cost parameters for a class of oligopolistic differentiated products markets. These estimates can be obtained using only widely available product-level and aggregate consumer-level data, and they are consistent with a structural model of equilibrium in an oligopolistic industry. Applying these techniques, the authors obtain parameters for essentially all autos sold over a twenty-year period. Copyright 1995 by The Econometric Society.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Simulation and the Monte Carlo Method
Rubinstein, R. Y., & Kroese, D. P. (2016) · Wiley Series in Probability and Statistics · doi:10.1002/9781118631980 · Cited by 3,356
Cited by
Discrete Choice Methods with Simulation
Train, K. E. (2001) · doi:10.1017/cbo9780511805271 · Cited by 7,784
Globalization and the Gains From Variety
Broda, C., & Weinstein, D. E. (2006) · The Quarterly Journal of Economics · doi:10.1162/qjec.2006.121.2.541 · Cited by 2,478
The rise of market power and the macroeconomic implications
De Loecker, J., Eeckhout, J., & Unger, G. (2020) · The Quarterly Journal of Economics · doi:10.1093/qje/qjz041 · Cited by 1,893
Carbon Taxes, Path Dependency, and Directed Technical Change: Evidence from the Auto Industry
Aghion, P., Dechezleprêtre, A., Hémous, D., Martin, R., & Van Reenen, J. (2016) · Journal of Political Economy · doi:10.1086/684581 · Cited by 1,604
Angrist, J. D., & Pischke, J.-S. (2010) · Journal of Economic Perspectives · doi:10.1257/jep.24.2.3 · Cited by 1,566
Marketing Analytics for Data-Rich Environments
Wedel, M., & Kannan, P. K. (2016) · Journal of Marketing · doi:10.1509/jm.15.0413 · Cited by 1,261
Firms and Labor Market Inequality: Evidence and Some Theory
Card, D., Cardoso, A. R., Heining, J., & Kline, P. (2018) · Journal of Labor Economics · doi:10.1086/694153 · Cited by 919
Machine learning and AI in marketing–Connecting computing power to human insights
Ma, L., & Sun, B. (2020) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2020.04.005 · Cited by 602
Conspicuous conservation: The Prius halo and willingness to pay for environmental bona fides
Sexton, S. E., & Sexton, A. L. (2014) · Journal of Environmental Economics and Management · doi:10.1016/j.jeem.2013.11.004 · Cited by 413
A two-sided, empirical model of television advertising and viewing markets
Wilbur, K. C. (2008) · Marketing Science · doi:10.1287/mksc.1070.0303 · Cited by 342
Brand and Quantity Choice Dynamics Under Price Uncertainty
Erdem, T., Imai, S., & Keane, M. P. (2003) · Quantitative Marketing and Economics · doi:10.1023/a:1023536326497 · Cited by 331
Ursu, R. M. (2018) · Marketing Science · doi:10.1287/mksc.2017.1072 · Cited by 328
Program evaluation and causal inference with high-dimensional data
Belloni, A., Chernozhukov, V., Fern�ndez-Val, I., & Hansen, C. (2017) · Econometrica · doi:10.3982/ecta12723 · Cited by 317
De Loecker, J., & Eeckhout, J. (2018) · doi:10.3386/w24768 · Cited by 308
Tracing the Woes: An Empirical Analysis of the Airline Industry
Berry, S., & Jia, P. (2010) · American Economic Journal: Microeconomics · doi:10.1257/mic.2.3.1 · Cited by 246
Self-citationContent Contribution for Revenue Sharing and Reputation in Social Media: A Dynamic Structural Model
Tang, Q., Gu, B., & Whinston, A. B. (2012) · Journal of Management Information Systems · doi:10.2753/mis0742-1222290203 · Cited by 243
Software exclusivity and the scope of indirect network effects in the U.S. home video game market
Corts, K. S., & Lederman, M. (2009) · International Journal of Industrial Organization · doi:10.1016/j.ijindorg.2008.08.002 · Cited by 218
Providing the Spark: Impact of financial incentives on battery electric vehicle adoption
Clinton, B. C., & Steinberg, D. C. (2019) · Journal of Environmental Economics and Management · doi:10.1016/j.jeem.2019.102255 · Cited by 201
Structural Estimation of the Effect of Out-of-Stocks
Musalem, A., Olivares, M., Bradlow, E. T., Terwiesch, C., & Corsten, D. (2010) · Management Science · doi:10.1287/mnsc.1100.1170 · Cited by 196
Online and Offline Demand and Price Elasticities: Evidence from the Air Travel Industry
Granados, N., Gupta, A., & Kauffman, R. J. (2012) · Information Systems Research · doi:10.1287/isre.1100.0312 · Cited by 189
Nonparametric Counterfactual Predictions in Neoclassical Models of International Trade
Adao, R., Costinot, A., & Donaldson, D. (2017) · American Economic Review · doi:10.1257/aer.20150956 · Cited by 158
The Role of Government in the Market for Electric Vehicles: Evidence from China
Li, S., Zhu, X., Ma, Y., Zhang, F., & Zhou, H. (2022) · Journal of Policy Analysis and Management · doi:10.1002/pam.22362 · Cited by 108
Uniform Pricing in US Retail Chains
DellaVigna, S., & Gentzkow, M. (2017) · doi:10.3386/w23996 · Cited by 75
Gluten-Free Products: From Dietary Necessity to Premium Price Extraction Tool
Gorgitano, M. T., & Sodano, V. (2019) · Nutrients · doi:10.3390/nu11091997 · Cited by 34
Some like it Healthy: Demand for functional and conventional yogurts in the italian market
Bonannoxs, A. (2012) · Agribusiness · doi:10.1002/agr.20288 · Cited by 33
Switching costs in the US seed industry: Technology adoption and welfare impacts
Luo, J., Moschini, G., & Perry, E. D. (2023) · International Journal of Industrial Organization · doi:10.1016/j.ijindorg.2023.102977 · Cited by 4
The High Cost of Low Range: Estimating the Hidden Cost of Range Anxiety in Electric Vehicles
Scott, J. B., & Qin, M. (2026) · The Journal of Industrial Economics · doi:10.1111/joie.70015
Metadata
- Authors
- Berry, S., Levinsohn, J., & Pakes, A.
- Venue
- Econometrica
- Publisher
- JSTOR
- Published
- 1995-07
- Volume
- 63
- Issue
- 4
- Pages
- 841
- ISSN
- 0012-9682 (ISSN-L)
- OpenAlex
- W1978907431
- Cited by
- 4,883
- References
- 1
- FWCI
- 53.94
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
