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Bounds on a Slope from Size Restrictions on Economic Shocks

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  • Marco Stenborg Petterson
  • David Seim
  • Jesse M. Shapiro

Abstract

We study the problem of learning about the effect of one market-level variable (e.g., price) on another (e.g., quantity) in the presence of shocks to unobservables (e.g., preferences). We show that economic intuitions about the plausible size of the shocks can be informative about the parameter of interest. We illustrate with a main application to the grain market.

Suggested Citation

  • Marco Stenborg Petterson & David Seim & Jesse M. Shapiro, 2023. "Bounds on a Slope from Size Restrictions on Economic Shocks," American Economic Journal: Microeconomics, American Economic Association, vol. 15(3), pages 552-572, August.
  • Handle: RePEc:aea:aejmic:v:15:y:2023:i:3:p:552-72
    DOI: 10.1257/mic.20210365
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    1. Manski, Charles F, 1990. "Nonparametric Bounds on Treatment Effects," American Economic Review, American Economic Association, vol. 80(2), pages 319-323, May.
    2. Raffaella Giacomini & Toru Kitagawa & Matthew Read, 2021. "Identification and Inference Under Narrative Restrictions," Papers 2102.06456, arXiv.org.
    3. Aviv Nevo & Adam M. Rosen, 2012. "Identification With Imperfect Instruments," The Review of Economics and Statistics, MIT Press, vol. 94(3), pages 659-671, August.
    4. Isaiah Andrews & Matthew Gentzkow & Jesse M. Shapiro, 2020. "Transparency in Structural Research," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(4), pages 711-722, October.
    5. Honoré,Bo & Pakes,Ariel & Piazzesi,Monika & Samuelson,Larry (ed.), 2017. "Advances in Economics and Econometrics," Cambridge Books, Cambridge University Press, number 9781108400008.
    6. Charles F. Manski & John V. Pepper, 2018. "How Do Right-to-Carry Laws Affect Crime Rates? Coping with Ambiguity Using Bounded-Variation Assumptions," The Review of Economics and Statistics, MIT Press, vol. 100(2), pages 232-244, May.
    7. E. Glen Weyl & Michal Fabinger, 2013. "Pass-Through as an Economic Tool: Principles of Incidence under Imperfect Competition," Journal of Political Economy, University of Chicago Press, vol. 121(3), pages 528-583.
    8. Juan Antolín-Díaz & Juan F. Rubio-Ramírez, 2018. "Narrative Sign Restrictions for SVARs," American Economic Review, American Economic Association, vol. 108(10), pages 2802-2829, October.
    9. Glenn Ellison & Sara Fisher Ellison, 2009. "Search, Obfuscation, and Price Elasticities on the Internet," Econometrica, Econometric Society, vol. 77(2), pages 427-452, March.
    10. Wallace P. Mullin & Christopher M. Snyder, 2021. "A Simple Method for Bounding the Elasticity of Growing Demand with Applications to the Analysis of Historic Antitrust Cases," American Economic Journal: Microeconomics, American Economic Association, vol. 13(4), pages 172-217, November.
    11. Riccardo Fiorito & Giulio Zanella, 2012. "The Anatomy of the Aggregate Labor Supply Elasticity," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 15(2), pages 171-187, April.
    12. Robert J. Barro & Charles J. Redlick, 2011. "Macroeconomic Effects From Government Purchases and Taxes," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 126(1), pages 51-102.
    13. Tamer, Elie, 2010. "Partial Identification in Econometrics," Scholarly Articles 34728615, Harvard University Department of Economics.
    14. Robert C. Feenstra & David E. Weinstein, 2017. "Globalization, Markups, and US Welfare," Journal of Political Economy, University of Chicago Press, vol. 125(4), pages 1040-1074.
    15. E. J. Working, 1927. "What Do Statistical "Demand Curves" Show?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 41(2), pages 212-235.
    16. Muhammad, Andrew & Meade, Birgit Gisela Saager & Regmi, Anita & Seale, James L., 2011. "International Evidence on Food Consumption Patterns: An Update Using 2005 International Comparison Program Data," Technical Bulletins 120252, United States Department of Agriculture, Economic Research Service.
    17. Elie Tamer, 2010. "Partial Identification in Econometrics," Annual Review of Economics, Annual Reviews, vol. 2(1), pages 167-195, September.
    18. Leamer, Edward E, 1981. "Is It a Demand Curve, or Is It a Supply Curve? Partial Identification through Inequality Constraints," The Review of Economics and Statistics, MIT Press, vol. 63(3), pages 319-327, August.
    19. Feenstra, Robert C, 1994. "New Product Varieties and the Measurement of International Prices," American Economic Review, American Economic Association, vol. 84(1), pages 157-177, March.
    20. Honoré,Bo & Pakes,Ariel & Piazzesi,Monika & Samuelson,Larry (ed.), 2017. "Advances in Economics and Econometrics," Cambridge Books, Cambridge University Press, number 9781108400022.
    21. Walter, Eric & Piet-Lahanier, Hélène, 1990. "Estimation of parameter bounds from bounded-error data: a survey," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 32(5), pages 449-468.
    22. Honoré,Bo & Pakes,Ariel & Piazzesi,Monika & Samuelson,Larry (ed.), 2017. "Advances in Economics and Econometrics," Cambridge Books, Cambridge University Press, number 9781316510520.
    23. Logan, Trevon D., 2006. "Nutrition and Well-Being in the Late Nineteenth Century," The Journal of Economic History, Cambridge University Press, vol. 66(2), pages 313-341, June.
    24. Steven T. Berry, 1994. "Estimating Discrete-Choice Models of Product Differentiation," RAND Journal of Economics, The RAND Corporation, vol. 25(2), pages 242-262, Summer.
    25. Hugo Valin & Ronald D. Sands & Dominique van der Mensbrugghe & Gerald C. Nelson & Helal Ahammad & Elodie Blanc & Benjamin Bodirsky & Shinichiro Fujimori & Tomoko Hasegawa & Petr Havlik & Edwina Heyhoe, 2014. "The future of food demand: understanding differences in global economic models," Agricultural Economics, International Association of Agricultural Economists, vol. 45(1), pages 51-67, January.
    26. Isaiah Andrews & Jesse M. Shapiro, 2021. "A Model of Scientific Communication," Econometrica, Econometric Society, vol. 89(5), pages 2117-2142, September.
    27. Juan Antolin-Diaz & Juan F. Rubio-Ramirez, 2016. "Narrative Sign Restrictions for SVARs," FRB Atlanta Working Paper 2016-16, Federal Reserve Bank of Atlanta.
    28. Ben Zeev, Nadav, 2018. "What can we learn about news shocks from the late 1990s and early 2000s boom-bust period?," Journal of Economic Dynamics and Control, Elsevier, vol. 87(C), pages 94-105.
    29. Michael J. Roberts & Wolfram Schlenker, 2013. "Identifying Supply and Demand Elasticities of Agricultural Commodities: Implications for the US Ethanol Mandate," American Economic Review, American Economic Association, vol. 103(6), pages 2265-2295, October.
    30. Honoré,Bo & Pakes,Ariel & Piazzesi,Monika & Samuelson,Larry (ed.), 2017. "Advances in Economics and Econometrics," Cambridge Books, Cambridge University Press, number 9781108414982.
    31. Muhammad, Andrew & Meade, Birgit Gisela Saager, 2011. "International Evidence on Food Consumption Patterns: An Update Using 2005 International Comparison Program Data," Technical Bulletins 120252, United States Department of Agriculture, Economic Research Service.
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    Cited by:

    1. Raffaella Giacomini & Toru Kitagawa & Matthew Read, 2021. "Identification and Inference Under Narrative Restrictions," Papers 2102.06456, arXiv.org.

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    More about this item

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices

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