IDEAS home Printed from https://ideas.repec.org/a/eee/econom/v208y2019i2p563-584.html
   My bibliography  Save this article

A computationally efficient fixed point approach to dynamic structural demand estimation

Author

Listed:
  • Sun, Yutec
  • Ishihara, Masakazu

Abstract

This paper develops a computationally efficient approach to the estimation of dynamic structural demand with product panel data. The conventional GMM approach relies on two nested fixed point (NFP) algorithms, each developed by Rust (1987) and Berry, Levinsohn, and Pakes (1995). We transform the GMM into a quasi-Bayesian (Laplace type) estimator and develop a new MCMC method that efficiently solves the fixed point problems. Our approach requires no stronger assumptions than the GMM and can thus avoid bias from misspecified models. In Monte Carlo analysis, the new method outperforms both NFP and MPEC, particularly in large-scale estimations.

Suggested Citation

  • Sun, Yutec & Ishihara, Masakazu, 2019. "A computationally efficient fixed point approach to dynamic structural demand estimation," Journal of Econometrics, Elsevier, vol. 208(2), pages 563-584.
  • Handle: RePEc:eee:econom:v:208:y:2019:i:2:p:563-584
    DOI: 10.1016/j.jeconom.2018.09.021
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304407618302161
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Andriy Norets, 2010. "Continuity and differentiability of expected value functions in dynamic discrete choice models," Quantitative Economics, Econometric Society, vol. 1(2), pages 305-322, November.
    2. Gautam Gowrisankaran & Marc Rysman, 2012. "Dynamics of Consumer Demand for New Durable Goods," Journal of Political Economy, University of Chicago Press, vol. 120(6), pages 1173-1219.
    3. Rust, John, 1987. "Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher," Econometrica, Econometric Society, vol. 55(5), pages 999-1033, September.
    4. Jean‐Pierre Dubé & Jeremy T. Fox & Che‐Lin Su, 2012. "Improving the Numerical Performance of Static and Dynamic Aggregate Discrete Choice Random Coefficients Demand Estimation," Econometrica, Econometric Society, vol. 80(5), pages 2231-2267, September.
    5. Reynaert, Mathias & Verboven, Frank, 2014. "Improving the performance of random coefficients demand models: The role of optimal instruments," Journal of Econometrics, Elsevier, vol. 179(1), pages 83-98.
    6. Steven Berry & Amit Gandhi & Philip Haile, 2013. "Connected Substitutes and Invertibility of Demand," Econometrica, Econometric Society, vol. 81(5), pages 2087-2111, September.
    7. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    8. Igal Hendel & Aviv Nevo, 2006. "Measuring the Implications of Sales and Consumer Inventory Behavior," Econometrica, Econometric Society, vol. 74(6), pages 1637-1673, November.
    9. Victor Aguirregabiria & Victor Aguirregabiria & Aviv Nevo & Aviv Nevo, 2010. "Recent Developments in Empirical IO: Dynamic Demand and Dynamic Games," Working Papers tecipa-419, University of Toronto, Department of Economics.
    10. Heiss, Florian & Winschel, Viktor, 2008. "Likelihood approximation by numerical integration on sparse grids," Journal of Econometrics, Elsevier, vol. 144(1), pages 62-80, May.
    11. Jiang, Renna & Manchanda, Puneet & Rossi, Peter E., 2009. "Bayesian analysis of random coefficient logit models using aggregate data," Journal of Econometrics, Elsevier, vol. 149(2), pages 136-148, April.
    12. Andrew Ching & Susumu Imai & Masakazu Ishihara & Neelam Jain, 2012. "A practitioner’s guide to Bayesian estimation of discrete choice dynamic programming models," Quantitative Marketing and Economics (QME), Springer, vol. 10(2), pages 151-196, June.
    13. Steve Berry & Oliver B. Linton & Ariel Pakes, 2004. "Limit Theorems for Estimating the Parameters of Differentiated Product Demand Systems," Review of Economic Studies, Oxford University Press, vol. 71(3), pages 613-654.
    14. Philip Heidelberger & Peter D. Welch, 1983. "Simulation Run Length Control in the Presence of an Initial Transient," Operations Research, INFORMS, vol. 31(6), pages 1109-1144, December.
    15. Nevo, Aviv, 2001. "Measuring Market Power in the Ready-to-Eat Cereal Industry," Econometrica, Econometric Society, vol. 69(2), pages 307-342, March.
    16. Hiroyuki Kasahara & Katsumi Shimotsu, 2012. "Sequential Estimation of Structural Models With a Fixed Point Constraint," Econometrica, Econometric Society, vol. 80(5), pages 2303-2319, September.
    17. Jinhyuk Lee & Kyoungwon Seo, 2015. "A computationally fast estimator for random coefficients logit demand models using aggregate data," RAND Journal of Economics, RAND Corporation, vol. 46(1), pages 86-102, March.
    18. Patrick Bajari & Han Hong & Stephen P. Ryan, 2010. "Identification and Estimation of a Discrete Game of Complete Information," Econometrica, Econometric Society, vol. 78(5), pages 1529-1568, September.
    19. Lee, Jinhyuk & Seo, Kyoungwon, 2016. "Revisiting the nested fixed-point algorithm in BLP random coefficients demand estimation," Economics Letters, Elsevier, vol. 149(C), pages 67-70.
    20. Viktor Winschel, 2005. "Solving, Estimating and Selecting Nonlinear Dynamic Economic Models without the Curse of Dimensionality," GE, Growth, Math methods 0507014, University Library of Munich, Germany.
    21. Jean-Pierre H. Dubé & Günter J. Hitsch & Pradeep K. Chintagunta, 2010. "Tipping and Concentration in Markets with Indirect Network Effects," Marketing Science, INFORMS, vol. 29(2), pages 216-249, 03-04.
    22. Oleg Melnikov, 2013. "Demand For Differentiated Durable Products: The Case Of The U.S. Computer Printer Market," Economic Inquiry, Western Economic Association International, vol. 51(2), pages 1277-1298, April.
    23. Carranza, Juan Esteban, 2010. "Product innovation and adoption in market equilibrium: The case of digital cameras," International Journal of Industrial Organization, Elsevier, vol. 28(6), pages 604-618, November.
    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. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, January.
    26. Susumu Imai & Neelam Jain & Andrew Ching, 2009. "Bayesian Estimation of Dynamic Discrete Choice Models," Econometrica, Econometric Society, vol. 77(6), pages 1865-1899, November.
    27. Robin S. Lee, 2013. "Vertical Integration and Exclusivity in Platform and Two-Sided Markets," American Economic Review, American Economic Association, vol. 103(7), pages 2960-3000, December.
    28. Sha Yang & Yuxin Chen & Greg Allenby, 2003. "Reply to Comments on “Bayesian Analysis of Simultaneous Demand and Supply”," Quantitative Marketing and Economics (QME), Springer, vol. 1(3), pages 299-304, September.
    29. Viktor Winschel & Markus Kr‰tzig, 2010. "Solving, Estimating, and Selecting Nonlinear Dynamic Models Without the Curse of Dimensionality," Econometrica, Econometric Society, vol. 78(2), pages 803-821, March.
    30. Chernozhukov, Victor & Hong, Han, 2003. "An MCMC approach to classical estimation," Journal of Econometrics, Elsevier, vol. 115(2), pages 293-346, August.
    31. Stephen P. Ryan, 2012. "The Costs of Environmental Regulation in a Concentrated Industry," Econometrica, Econometric Society, vol. 80(3), pages 1019-1061, May.
    32. Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-890, July.
    33. Sha Yang & Yuxin Chen & Greg Allenby, 2003. "Bayesian Analysis of Simultaneous Demand and Supply," Quantitative Marketing and Economics (QME), Springer, vol. 1(3), pages 251-275, September.
    34. Kenneth L. Judd & Ben Skrainka, 2011. "High performance quadrature rules: how numerical integration affects a popular model of product differentiation," CeMMAP working papers CWP03/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    35. Andriy Norets, 2009. "Inference in Dynamic Discrete Choice Models With Serially orrelated Unobserved State Variables," Econometrica, Econometric Society, vol. 77(5), pages 1665-1682, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Cheng Chou & Tim Derdenger & Vineet Kumar, 2019. "Linear Estimation of Aggregate Dynamic Discrete Demand for Durable Goods: Overcoming the Curse of Dimensionality," Marketing Science, INFORMS, vol. 38(5), pages 888-909, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andrew Ching & Masakazu Ishihara, 2014. "Dynamic Demand for New and Used Durable Goods without Physical Depreciation: The Case of Japanese Video Games," 2014 Meeting Papers 782, Society for Economic Dynamics.
    2. Peter Davis & Pasquale Schiraldi, 2014. "The flexible coefficient multinomial logit (FC-MNL) model of demand for differentiated products," RAND Journal of Economics, RAND Corporation, vol. 45(1), pages 32-63, March.
    3. Reynaert, Mathias & Verboven, Frank, 2014. "Improving the performance of random coefficients demand models: The role of optimal instruments," Journal of Econometrics, Elsevier, vol. 179(1), pages 83-98.
    4. Donna, Javier D., 2018. "Measuring Long-Run Price Elasticities in Urban Travel Demand," MPRA Paper 90059, University Library of Munich, Germany.
    5. Daniel Ackerberg, 2009. "A new use of importance sampling to reduce computational burden in simulation estimation," Quantitative Marketing and Economics (QME), Springer, vol. 7(4), pages 343-376, December.
    6. Victor Aguirregabiria & Victor Aguirregabiria & Aviv Nevo & Aviv Nevo, 2010. "Recent Developments in Empirical IO: Dynamic Demand and Dynamic Games," Working Papers tecipa-419, University of Toronto, Department of Economics.
    7. Yonezawa, Koichi & Richards, Timothy J., 2016. "Competitive Package Size Decisions," Journal of Retailing, Elsevier, vol. 92(4), pages 445-469.
    8. Timothy Derdenger, 2014. "Technological tying and the intensity of price competition: An empirical analysis of the video game industry," Quantitative Marketing and Economics (QME), Springer, vol. 12(2), pages 127-165, June.
    9. Victor Aguirregabiria & Margaret Slade, 2017. "Empirical models of firms and industries," Canadian Journal of Economics, Canadian Economics Association, vol. 50(5), pages 1445-1488, December.
    10. Liu, Yan & Cirillo, Cinzia, 2018. "A generalized dynamic discrete choice model for green vehicle adoption," Transportation Research Part A: Policy and Practice, Elsevier, vol. 114(PB), pages 288-302.
    11. Junpei Komiyama & Hajime Shimao, 2018. "Cross Validation Based Model Selection via Generalized Method of Moments," Papers 1807.06993, arXiv.org.
    12. Charles J. Romeo, 2016. "Incorporating Prior Information into A GMM Objective For Mixed Logit Demand Systems," Journal of Industrial Economics, Wiley Blackwell, vol. 64(2), pages 336-363, June.
    13. Fernández-Antolín, Anna & Guevara, C. Angelo & de Lapparent, Matthieu & Bierlaire, Michel, 2016. "Correcting for endogeneity due to omitted attitudes: Empirical assessment of a modified MIS method using RP mode choice data," Journal of choice modelling, Elsevier, vol. 20(C), pages 1-15.
    14. Pradeep K. Chintagunta & Marco Shaojun Qin & Maria Ana Vitorino, 2018. "Licensing and Price Competition in Tied-Goods Markets: An Application to the Single-Serve Coffee System Industry," Marketing Science, INFORMS, vol. 37(6), pages 883-911, November.
    15. Andrew Ching & Susumu Imai & Masakazu Ishihara & Neelam Jain, 2012. "A practitioner’s guide to Bayesian estimation of discrete choice dynamic programming models," Quantitative Marketing and Economics (QME), Springer, vol. 10(2), pages 151-196, June.
    16. Susan Athey & Guido W. Imbens, 2007. "Discrete Choice Models With Multiple Unobserved Choice Characteristics," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 48(4), pages 1159-1192, November.
    17. Christopher R. Knittel & Konstantinos Metaxoglou, 2008. "Estimation of Random Coefficient Demand Models: Challenges, Difficulties and Warnings," NBER Working Papers 14080, National Bureau of Economic Research, Inc.
    18. Ketz, Philipp, 2019. "On asymptotic size distortions in the random coefficients logit model," Journal of Econometrics, Elsevier, vol. 212(2), pages 413-432.
    19. Holmes, Thomas J. & Sieg, Holger, 2015. "Structural Estimation in Urban Economics," Handbook of Regional and Urban Economics, in: Gilles Duranton & J. V. Henderson & William C. Strange (ed.), Handbook of Regional and Urban Economics, edition 1, volume 5, chapter 0, pages 69-114, Elsevier.
    20. Victor Aguirregabiria & Gustavo Vicentini, 2006. "Dynamic Spatial Competition Between Multi-Store Firms," Working Papers tecipa-253, University of Toronto, Department of Economics.

    More about this item

    Keywords

    Nested fixed point; BLP; Dynamic; MCMC; Random coefficients logit;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:econom:v:208:y:2019:i:2:p:563-584. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Haili He). General contact details of provider: http://www.elsevier.com/locate/jeconom .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.