Estimating Parameters of Structural Models Using Neural Networks

Published Online:https://doi.org/10.1287/mksc.2022.0360

References

  • Altonji JG, Segal LM (1996) Small-sample bias in GMM estimation of covariance structures. J. Bus. Econom. Statist. 14(3):353–366.CrossrefGoogle Scholar
  • Andersen T, Sorensen B (1996) GMM estimation of a stochastic volatility model: A Monte Carlo study. J. Bus. Econom. Statist. 14(3):328–352.CrossrefGoogle Scholar
  • Athey S (2018) The impact of machine learning on economics. The Economics of Artificial Intelligence: An Agenda (University of Chicago Press, Chicago), 507–547.Google Scholar
  • Bajari P, Benkard CL, Levin J (2007) Estimating dynamic models of imperfect competition. Econometrica 75(5):1331–1370.CrossrefGoogle Scholar
  • Bao W, Ni J (2017) Could good intentions backfire? An empirical analysis of the bank deposit insurance. Marketing Sci. 36(2):301–319.LinkGoogle Scholar
  • Breusch T, Qian H, Schmidt P, Wyhowski D (1999) Redundancy of moment conditions. J. Econometrics 91:89–111.CrossrefGoogle Scholar
  • Bruins M, Duffy JA, Keane MP, Smith AA Jr (2018) Generalized indirect inference for discrete choice models. J. Econometrics 205(1):177–203.CrossrefGoogle Scholar
  • Chen X (2007) Large sample sieve estimation of semi-nonparametric models. Handbook of Econometrics, vol. 6(B) (Elsevier, Amsterdam), 5549–5632.Google Scholar
  • Chen X, Liao Z (2015) Select the valid and relevant moments: An information-based LASSO for GMM with many moments. J. Econometrics 186(2):443–464.Google Scholar
  • Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, Newey W, Robins J (2018) Double/debiased machine learning for treatment and structural parameters. Econom. J. 21(1):C1–C68.CrossrefGoogle Scholar
  • Chiong K, Shum M (2019) Random projection estimation of discrete-choice models with large choice sets. Management Sci. 65(1):256–271.LinkGoogle Scholar
  • Collard-Wexler A (2013) Demand fluctuations in the ready-mix concrete industry. Econometrica 81(3):1003–1037.Google Scholar
  • Dauphin Y, Pascanu R, Gulcehre C, Cho K, Ganguli S, Bengio Y (2014) Identifying and attacking the saddle point problem in high-dimensional non-convex optimization. Adv. Neural Inform. Processing Systems 27:2933–2941.Google Scholar
  • Donald SG, Newey WK (2021) Choosing the number of instruments. Econometrica 69(5):1161–1191.Google Scholar
  • Du S, Lee J, Li H, Wang L, Zhai X (2019) Gradient descent finds global minima of deep neural networks. Chaudhuri K, Salakhutdinov R, eds. Proc. Internat. Conf. Machine Learn., vol. 97 (PMLR, New York), 1675–1685.Google Scholar
  • Farrell M, Liang T, Misra S (2021a) Deep neural networks for estimation and inference. Econometrica 89(1):181–213.CrossrefGoogle Scholar
  • Farrell MH, Liang T, Misra S (2021b) Deep learning for individual heterogeneity: An automatic inference framework. Preprint, submitted October 28, https://arxiv.org/abs/2010.14694.Google Scholar
  • Gelman A, Carlin JB, Stern HS, Rubin DB (2004) Bayesian Data Analysis, 2nd ed. (Chapman and Hall/CRC, Boca Raton, FL).Google Scholar
  • Geweke J, Keane M (2001) Computationally intensive methods for integration in econometrics. Handbook of Econometrics, vol. 5 (Elsevier, New York), 3463–3568.CrossrefGoogle Scholar
  • Gourieroux C, Monfort A, Renault E (1993) Indirect inference. J. Appl. Econometrics 8(S1):S85–S118.CrossrefGoogle Scholar
  • Graham BS (2020) Network data. Durlauf S, Hansen LP, Heckman J, Matzkin R, eds. Handbook of Econometrics, vol. 7 (Elsevier, Amsterdam) 111–218.Google Scholar
  • Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Networks 2(5):359–366.CrossrefGoogle Scholar
  • Kaji T, Manresa E, Pouliot G (2023) An adversarial approach to structural estimation. Econometrica 91(6):2041–2063.CrossrefGoogle Scholar
  • Kim JY (2002) Limited information likelihood and Bayesian analysis. J. Econometrics 107(1–2):175–193.CrossrefGoogle Scholar
  • Lee Y, Ogburn EL (2021) Network dependence can lead to spurious associations and invalid inference. J. Amer. Statist. Assoc. 116(535):1060–1074.CrossrefGoogle Scholar
  • Lewis G, Syrgkanis V (2018) Adversarial generalized method of moments. Preprint, submitted March 19, https://arxiv.org/abs/1803.07164.Google Scholar
  • Li H, Xu Z, Taylor G, Studer C, Goldstein T (2018) Visualizing the loss landscape of neural nets. Bengio S, Wallach H, Larochelle H, Grauman K, Cesa-Bianchi N, Garnett R, eds. Adv. Neural Inform. Processing Systems, vol. 31 (Curran Associates, Inc., Red Hook, NY).Google Scholar
  • Liu X, Lee D, Srinivasan K (2019) Large-scale cross-category analysis of consumer review content on sales conversion leveraging deep learning. J. Marketing Res. 56(6):918–943.CrossrefGoogle Scholar
  • Newey WK (2007) Generalized method of moments. MIT OpenCourseWare: Nonlinear Econometric Analysis, https://ocw.mit.edu/courses/14-385-nonlinear-econometric-analysis-fall-2007/.Google Scholar
  • Pakes A, Ostrovsky M, Berry S (2007) Simple estimators for the parameters of discrete dynamic games (with entry/exit examples). RAND J. Econom. 38(2):373–399.CrossrefGoogle Scholar
  • Smith A (2008) The New Palgrave Dictionary of Economics, 2nd ed. (Palgrave Macmillan, London).Google Scholar
  • Su CL, Judd KL (2012) Constrained optimization approaches to estimation of structural models. Econometrica 80(5):2213–2230.CrossrefGoogle Scholar
  • Timoshenko A, Hauser JR (2019) Identifying customer needs from user-generated content. Marketing Sci. 38(1):1–20.LinkGoogle Scholar
  • Ursu RM (2018) The power of rankings: Quantifying the effect of rankings on online consumer search and purchase decisions. Marketing Sci. 37(4):530–552.LinkGoogle Scholar
  • Wager S, Athey S (2018) Estimation and inference of heterogeneous treatment effects using random forests. J. Amer. Statist. Assoc. 113(523):1228–1242.CrossrefGoogle Scholar
  • Weitzman ML (1979) Optimal search for the best alternative. Econometrica 47(3):641–654.CrossrefGoogle Scholar
  • White H (1982) Maximum likelihood estimation of misspecified models. Econometrica 50(1):1–25.CrossrefGoogle Scholar
  • White H (1989) Learning in artificial neural networks: A statistical perspective. Neural Comput. 1(4):425–464.CrossrefGoogle Scholar
  • White H (1990) Connectionist nonparametric regression: Multilayer feedforward networks can learn arbitrary mappings. Neural Networks 3(5):535–549.CrossrefGoogle Scholar
  • Yoganarasimhan H (2020) Search personalization using machine learning. Management Sci. 66(3):1045–1070.LinkGoogle Scholar
  • Yoganarasimhan H, Barzegary E, Pani A (2023) Design and evaluation of optimal free trials. Management Sci. 69(6):3220–3240.LinkGoogle Scholar
  • Zhang M, Luo L (2023) Can consumer-posted photos serve as a leading indicator of restaurant survival? Evidence from Yelp. Management Sci. 69(1):25–50.LinkGoogle Scholar
  • Zhu Y, Simester D, Parker JA, Schoar A (2020) Dynamic marketing policies: Constructing Markov states for reinforcement learning. Preprint, submitted July 16, https://dx.doi.org/10.2139/ssrn.3633870.Google Scholar
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