1932

Abstract

This article reviews recent developments in the study of firm and industry dynamics, with a special emphasis on the econometric endogeneity of market structure. The endogeneity of market structure follows from the presence of serially correlated unobservable shocks to the profitability of firms’ dynamic decisions, a feature common to many empirical settings. Methods that ignore endogeneity can lead to misleading parameter estimates and misleading counterfactual results. We pay particular attention to extensions of standard two-step methods that leverage instrumental variables to address endogeneity in both single-agent and oligopoly models. A first step set-identifies dynamic policy functions together with serial correlation parameters, and a second step quickly solves for profit function parameters using an extension of existing forward-simulation methods. We discuss how these new methods provide a general solution to initial-conditions problems and how they can yield practical estimation strategies.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-economics-081720-120019
2021-08-05
2024-12-13
Loading full text...

Full text loading...

/deliver/fulltext/economics/13/1/annurev-economics-081720-120019.html?itemId=/content/journals/10.1146/annurev-economics-081720-120019&mimeType=html&fmt=ahah

Literature Cited

  1. Abbring JH, Campbell JR. 2010.. Last-in first-out oligopoly dynamics. . Econometrica 78::1491527
    [Google Scholar]
  2. Ackerberg D, Benkard L, Berry S, Pakes A. 2007.. Econometric tools for analyzing market outcomes. . In Handbook of Econometrics, Vol. 6A, ed. JJ Heckman, E Leamer , pp. 4171276. Amsterdam:: North-Holland
    [Google Scholar]
  3. Aguirregabiria V, Ho CY. 2012.. A dynamic oligopoly game of the US airline industry: estimation and policy experiments. . J. Econom. 168::15673
    [Google Scholar]
  4. Aguirregabiria V, Mira P. 2007.. Sequential estimation of dynamic discrete games. . Econometrica 75::153
    [Google Scholar]
  5. Aguirregabiria V, Mira P. 2010.. Dynamic discrete choice structural models: a survey. . J. Econom. 156::3867
    [Google Scholar]
  6. Andrews DW, Shi X. 2013.. Inference based on conditional moment inequalities. . Econometrica 81::60966
    [Google Scholar]
  7. Andrews DW, Shi X. 2017.. Inference based on many conditional moment inequalities. . J. Econom. 196::27587
    [Google Scholar]
  8. Andrews DW, Soares G. 2010.. Inference for parameters defined by moment inequalities using generalized moment selection. . Econometrica 78::11957
    [Google Scholar]
  9. Arcidiacono P, Bayer P, Blevins JR, Ellickson PB. 2016.. Estimation of dynamic discrete choice models in continuous time with an application to retail competition. . Rev. Econ. Stud. 83::889931
    [Google Scholar]
  10. Arcidiacono P, Ellickson PB. 2011.. Practical methods for estimation of dynamic discrete choice models. . Annu. Rev. Econ. 3::36394
    [Google Scholar]
  11. Arcidiacono P, Miller R. 2011.. Conditional choice probability estimation of dynamic discrete choice models with unobserved heterogeneity. . Econometrica 7::182368
    [Google Scholar]
  12. Bajari P, Benkard CL, Levin J. 2007.. Estimating dynamic models of imperfect competition. . Econometrica 75::133170
    [Google Scholar]
  13. Beresteanu A, Molinari F, Molchanov I. 2011.. Sharp identification regions in models with convex moment predictions. . Econometrica 79::1785821
    [Google Scholar]
  14. Berry ST. 1994.. Estimating discrete choice models of product differentiation. . RAND J. Econ. 23::24262
    [Google Scholar]
  15. Berry ST, Compiani G. 2020.. An instrumental variable approach to dynamic models. Tech. Rep., Yale Univ., New Haven, CT:
    [Google Scholar]
  16. Berry ST, Gandhi A, Haile PA. 2013.. Connected substitutes and invertibility of demand. . Econometrica 81::2087111
    [Google Scholar]
  17. Berry ST, Haile PA. 2018.. Nonparametric identification of simultaneous equations models with a residual index structure. . Econometrica 86::289315
    [Google Scholar]
  18. Berry ST, Reiss P. 2007.. Empirical models of entry and market structure. . In Handbook of Industrial Organization, Vol. 3, ed. M Armstrong, R Porter , pp. 184586. Amsterdam:: North Holland
    [Google Scholar]
  19. Berry ST, Tamer E. 2007.. Identification in models of oligopoly entry. . In Advances in Economics and Econometrics: Theory and Applications, Vol. 2, ed. R Blundell, W Newey, T Persson , 4685. Cambridge, UK:: Cambridge Univ. Press
    [Google Scholar]
  20. Blundell R, Bond S. 1998.. Initial conditions and moment restrictions in dynamic panel data models. . J. Econom. 87::11543
    [Google Scholar]
  21. Borkovsky R, Doraszelski U, Kryukov Y. 2012.. A dynamic quality ladder model with entry and exit: exploring the equilibrium correspondence using the homotopy method. . Quant. Mark. Econ. 10::197229
    [Google Scholar]
  22. Chamberlain G. 1985.. Heterogeneity, omitted variable bias, and duration dependence. . In Longitudinal Analysis of Labor Market Data, ed. JJ Heckman, B Singer , pp. 338. Cambridge, UK:: Cambridge Univ. Press
    [Google Scholar]
  23. Chernozhukov V, Chetverikov D, Kato K. 2018.. Inference on causal and structural parameters using many moment inequalities. . Rev. Econ. Stud. 86::1867900
    [Google Scholar]
  24. Chernozhukov V, Hansen C. 2005.. An IV model of quantile treatment effects. . Econometrica 73::24561
    [Google Scholar]
  25. Chernozhukov V, Hong H, Tamer E. 2007.. Estimation and confidence regions for parameter sets in econometric models. . Econometrica 75::124384
    [Google Scholar]
  26. Chernozhukov V, Lee S, Rosen A. 2013.. Intersection bounds: estimation and inference. . Econometrica 81::667737
    [Google Scholar]
  27. Chesher A. 2010.. Instrumental variables models for discrete outcomes. . Econometrica 78::575601
    [Google Scholar]
  28. Chesher A, Rosen A. 2017.. Generalized instrumental variable models. . Econometrica 83::95989
    [Google Scholar]
  29. Ciliberto F, Tamer E. 2009.. Market structure and multiple equilibria in airline markets. . Econometrica 77::1791828
    [Google Scholar]
  30. Collard-Wexler A. 2013.. Demand fluctuations in the ready-mix concrete industry. . Econometrica 81::100337
    [Google Scholar]
  31. Collard-Wexler A. 2014.. Mergers and sunk costs: an application to the ready-mix concrete industry. . Am. Econ. J. Microecon. 6::40747
    [Google Scholar]
  32. Dixit A. 1992.. Investment and hysteresis. . J. Econ. Perspect. 6::10732
    [Google Scholar]
  33. Doraszelski U, Judd KL. 2012.. Avoiding the curse of dimensionality in dynamic stochastic games. . Quant. Econ. 3::5393
    [Google Scholar]
  34. Doraszelski U, Pakes A. 2007.. A framework for applied dynamic analysis in IO. . In Handbook of Industrial Organization, Vol. 3, ed. M Armstrong, R Porter , pp. 1887966. Amsterdam:: Elsevier
    [Google Scholar]
  35. Doraszelski U, Satterthwaite M. 2010.. Computable Markov-perfect industry dynamics. . RAND J. Econ. 41::21543
    [Google Scholar]
  36. Dubé J, Fox JT, Su C. 2012.. Improving the numerical performance of static and dynamic aggregate discrete choice random coefficients demand estimation. . Econometrica 80::223167
    [Google Scholar]
  37. Dunne T, Klimek SD, Roberts MJ, Xu DY. 2013.. Entry, exit, and the determinants of market structure. . RAND J. Econ. 44::46287
    [Google Scholar]
  38. Ericson R, Pakes A. 1995.. Markov perfect industry dynamics: a framework for empirical work. . Rev. Econ. Stud. 62::5382
    [Google Scholar]
  39. Fowlie M, Reguant M, Ryan SP. 2016.. Market-based emissions regulation and industry dynamics. . J. Political Econ. 124::249302
    [Google Scholar]
  40. Galichon A, Henry M. 2011.. Set identification in models with multiple equilibria. . Rev. Econ. Stud. 78::126498
    [Google Scholar]
  41. Gowrisankaran G, Town RJ. 1997.. Dynamic equilibrium in the hospital industry. . J. Econ. Manag. Strategy 6::4574
    [Google Scholar]
  42. Heckman J. 1981.. The incidental parameters problem and the problem of initial conditions in estimating a discrete time–discrete data stochastic process. . In Structural Analysis of Discrete Data with Econometric Applications, ed. CF Manski, DL McFadden , pp. 17995. Cambridge, MA:: MIT Press
    [Google Scholar]
  43. Heckman J, Singer B. 1984.. A method for minimizing the impact of distributional assumptions in econometric models for duration data. . Econometrica 52::271320
    [Google Scholar]
  44. Hodgson C. 2019.. Information externalities, free riding, and optimal exploration in the UK oil industry. Tech. Rep., Yale Univ., New Haven, CT:
    [Google Scholar]
  45. Holmes TJ. 2011.. The diffusion of Wal-Mart and economies of density. . Econometrica 79::253302
    [Google Scholar]
  46. Honoré BE, Tamer E. 2006.. Bounds on parameters in panel dynamic discrete choice models. . Econometrica 74::61129
    [Google Scholar]
  47. Hotz VJ, Miller RA. 1993.. Conditional choice probabilities and the estimation of dynamic models. . Rev. Econ. Stud. 60::497529
    [Google Scholar]
  48. Hotz VJ, Miller RA, Sanders S, Smith J. 1994.. A simulation estimator for dynamic models of discrete choice. . Rev. Econ. Stud. 61::26589
    [Google Scholar]
  49. Ichimura H, Thompson TS. 1998.. Maximum likelihood estimation of a binary choice model with random coefficients of unknown distribution. . J. Econometrics 86::26995
    [Google Scholar]
  50. Igami M. 2017.. Estimating the innovator's dilemma: structural analysis of creative destruction in the hard disk drive industry, 1981–98. . J. Political Econ. 125::798847
    [Google Scholar]
  51. Igami M. 2018.. Industry dynamics of offshoring: the case of hard disk drives. . Am. Econ. J. Microecon. 10::67101
    [Google Scholar]
  52. Igami M, Uetake K. 2020.. Mergers, innovation, and entry-exit dynamics: consolidation of the hard disk drive industry, 1996–2016. . Rev. Econ. Stud. 87::2672702
    [Google Scholar]
  53. Igami M, Yang N. 2016.. Unobserved heterogeneity in dynamic games: cannibalization and preemptive entry of hamburger chains in Canada. . Quant. Econ. 7::483521
    [Google Scholar]
  54. Jeziorski P. 2014.. Estimation of cost efficiencies from mergers: application to US radio. . RAND J. Econ. 45::81646
    [Google Scholar]
  55. Jofre-Bonet M, Pesendorfer M. 2003.. Estimation of a dynamic auction game. . Econometrica 71::144389
    [Google Scholar]
  56. Kalouptsidi M, Scott P, Souza-Rodrigues E. 2020.. Linear IV regression estimators for single-agent dynamic discrete choice models. Tech. Rep., Harvard Univ., Cambridge, MA:
    [Google Scholar]
  57. Kasahara H, Shimotsu K. 2009.. Nonparametric identification of finite mixture models of dynamic discrete choices. . Econometrica 77::13575
    [Google Scholar]
  58. Magnac T, Thesmar D. 2002.. Identifying dynamic discrete decision processes. . Econometrica 70::80116
    [Google Scholar]
  59. Manski CF. 2003.. Partial Identification of Probability Distributions. New York:: Springer
    [Google Scholar]
  60. Manski CF, Tamer E. 2002.. Inference on regressions with interval data on a regressor or outcome. . Econometrica 70::51946
    [Google Scholar]
  61. Matzkin RL. 2003.. Nonparametric estimation of nonadditive random functions. . Econometrica 71::133975
    [Google Scholar]
  62. Menzel K. 2014.. Consistent estimation with many moment inequalities. . J. Econom. 182::32950
    [Google Scholar]
  63. Olley SG, Pakes A. 1996.. The dynamics of productivity in the telecommunications equipment industry. . Econometrica 64::126397
    [Google Scholar]
  64. Pakes A, Ericson R. 1998.. Empirical implications of alternative models of firm dynamics. . J. Econ. Theory 79::145
    [Google Scholar]
  65. Pakes A, McGuire P. 1994.. Computing Markov-perfect Nash equilibria: numerical implications of a dynamic differentiated product model. . RAND J. Econ. 25::55589
    [Google Scholar]
  66. Pakes A, Ostrovsky M, Berry S. 2007.. Simple estimators for the parameters of dynamic games, with entry/exit examples. . RAND J. Econ. 38::37399
    [Google Scholar]
  67. Pesendorfer M, Schmidt-Dengler P. 2008.. Asymptotic least squares estimators for dynamic games. . Rev. Econ. Stud. 75::90128
    [Google Scholar]
  68. Pesendorfer M, Schmidt-Dengler P. 2010.. Sequential estimation of dynamic discrete games: a comment. . Econometrica 78::83342
    [Google Scholar]
  69. Rust J. 1987.. Optimal replacement of GMC bus engines: an empirical model of Harold Zurcher. . Econometrica 55::9991033
    [Google Scholar]
  70. Rust J. 1994.. Estimation of dynamic structural models, problems and prospects: discrete decision processes. . In Advances in Econometrics, ed. C Sims , pp. 11970. Cambridge, UK:: Cambridge Univ. Press
    [Google Scholar]
  71. Ryan SP. 2012.. The costs of environmental regulation in a concentrated industry. . Econometrica 80::101961
    [Google Scholar]
  72. Stokey NL, Lucas RE, Prescott EC. 1989.. Recursive Methods in Economic Dynamics. Cambridge, MA:: Harvard Univ. Press
    [Google Scholar]
  73. Sweeting A. 2013.. Dynamic product positioning in differentiated product markets: the effect of fees for musical performance rights on the commercial radio industry. . Econometrica 81::1763803
    [Google Scholar]
  74. Tamer E. 2003.. Incomplete simultaneous discrete response model with multiple equilibria. . Rev. Econ. Stud. 70::14765
    [Google Scholar]
  75. Toivanen O, Waterson M. 2005.. Market structure and entry: Where's the beef?. RAND J. Econ. 36::68099
    [Google Scholar]
  76. Weintraub GY, Benkard CL, Van Roy B. 2008.. Markov perfect industry dynamics with many firms. . Econometrica 76::1375411
    [Google Scholar]
  77. Wolpin K, Keane M. 1994.. The solution and estimation of discrete choice dynamic programming models by simulation and interpolation: Monte Carlo evidence. . Rev. Econ. Stat. 76::64872
    [Google Scholar]
  78. Wooldridge JM. 2005.. Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity. . J. Appl. Econ. 20::3954
    [Google Scholar]
/content/journals/10.1146/annurev-economics-081720-120019
Loading
/content/journals/10.1146/annurev-economics-081720-120019
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error