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Likelihood approximation by numerical integration on sparse grids

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Cited by:

  1. 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.
  2. M. H. Hof & J. Z. Musoro & R. B. Geskus & G. H. Struijk & I. J. M. ten Berge & A. H. Zwinderman, 2017. "Simulated maximum likelihood estimation in joint models for multiple longitudinal markers and recurrent events of multiple types, in the presence of a terminal event," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(15), pages 2756-2777, November.
  3. Natalia Khorunzhina & Jean-François Richard, 2019. "Finite Gaussian Mixture Approximations to Analytically Intractable Density Kernels," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 991-1017, March.
  4. Kun, Wang & Fu, Chen & Jianyang, Yu & Yanping, Song, 2020. "Nested sparse-grid Stochastic Collocation Method for uncertainty quantification of blade stagger angle," Energy, Elsevier, vol. 201(C).
  5. Bhat, Chandra R., 2011. "The maximum approximate composite marginal likelihood (MACML) estimation of multinomial probit-based unordered response choice models," Transportation Research Part B: Methodological, Elsevier, vol. 45(7), pages 923-939, August.
  6. Hui Chen & Antoine Didisheim & Simon Scheidegger, 2021. "Deep Structural Estimation:With an Application to Option Pricing," Cahiers de Recherches Economiques du Département d'économie 21.14, Université de Lausanne, Faculté des HEC, Département d’économie.
  7. Florian Heiss, 2011. "Dynamics of self-rated health and selective mortality," Empirical Economics, Springer, vol. 40(1), pages 119-140, February.
  8. S. Bogan Aruoba & Pablo Cuba-Borda & Kenji Higa-Flores & Frank Schorfheide & Sergio Villalvazo, 2021. "Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 41, pages 96-120, July.
  9. Maria Polyakova, 2016. "Regulation of Insurance with Adverse Selection and Switching Costs: Evidence from Medicare Part D," American Economic Journal: Applied Economics, American Economic Association, vol. 8(3), pages 165-195, July.
  10. Horowitz, Joel L. & Nesheim, Lars, 2021. "Using penalized likelihood to select parameters in a random coefficients multinomial logit model," Journal of Econometrics, Elsevier, vol. 222(1), pages 44-55.
  11. Fox, Jeremy T. & Kim, Kyoo il & Yang, Chenyu, 2016. "A simple nonparametric approach to estimating the distribution of random coefficients in structural models," Journal of Econometrics, Elsevier, vol. 195(2), pages 236-254.
  12. Felix Tintelnot, 2017. "Global Production with Export Platforms," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 132(1), pages 157-209.
  13. Prateek Bansal & Vahid Keshavarzzadeh & Angelo Guevara & Shanjun Li & Ricardo A Daziano, 2022. "Designed quadrature to approximate integrals in maximum simulated likelihood estimation [Evaluating simulation-based approaches and multivariate quadrature on sparse grids in estimating multivariat," The Econometrics Journal, Royal Economic Society, vol. 25(2), pages 301-321.
  14. Daniel Gerhard & Melanie Bremer & Christian Ritz, 2014. "Estimating marginal properties of quantitative real-time PCR data using nonlinear mixed models," Biometrics, The International Biometric Society, vol. 70(1), pages 247-254, March.
  15. Alexanderian, Alen & Gremaud, Pierre A. & Smith, Ralph C., 2020. "Variance-based sensitivity analysis for time-dependent processes," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
  16. Florian Heiss, 2016. "Discrete Choice Methods with Simulation," Econometric Reviews, Taylor & Francis Journals, vol. 35(4), pages 688-692, April.
  17. Paleti, Rajesh & Bhat, Chandra R., 2013. "The composite marginal likelihood (CML) estimation of panel ordered-response models," Journal of choice modelling, Elsevier, vol. 7(C), pages 24-43.
  18. Joel L. Horowitz & Lars Nesheim, 2018. "Using penalized likelihood to select parameters in a random coefficients multinomial logit model," CeMMAP working papers CWP29/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  19. Sándor Zsolt, 2013. "Monte Carlo Simulation in Random Coefficient Logit Models Involving Large Sums," Acta Universitatis Sapientiae, Economics and Business, Sciendo, vol. 1(1), pages 85-108, July.
  20. Xuan Teng, 2022. "Self-Preferencing, Quality Provision, and Welfare in Mobile Application Markets," CESifo Working Paper Series 10042, CESifo.
  21. Matthew Gentzkow & Jesse M. Shapiro & Michael Sinkinson, 2014. "Competition and Ideological Diversity: Historical Evidence from US Newspapers," American Economic Review, American Economic Association, vol. 104(10), pages 3073-3114, October.
  22. Nicolas Legrand & Christophe Gouel, 2022. "The Role of Storage in Commodity Markets: Indirect Inference Based on Grains Data," Working Papers hal-03809825, HAL.
  23. Gaurav Khemka & Adam Butt, 2017. "Non-Parametric Integral Estimation Using Data Clustering in Stochastic dynamic Programming: An Introduction Using Lifetime Financial Modelling," Risks, MDPI, vol. 5(4), pages 1-17, October.
  24. Junko Koeda & Yoichi Ueno, 2022. "A Preferred Habitat View of Yield Curve Control," Bank of Japan Working Paper Series 22-E-7, Bank of Japan.
  25. Jean-Jacques Forneron, 2019. "A Sieve-SMM Estimator for Dynamic Models," Papers 1902.01456, arXiv.org, revised Jan 2023.
  26. Gerstner, Thomas & Griebel, Michael & Holtz, Markus, 2009. "Efficient deterministic numerical simulation of stochastic asset-liability management models in life insurance," Insurance: Mathematics and Economics, Elsevier, vol. 44(3), pages 434-446, June.
  27. S. Bogan Aruoba & Pablo Cuba-Borda & Kenji Higa-Flores & Frank Schorfheide & Sergio Villalvazo, 2021. "Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 41, pages 96-120, July.
  28. Santiago Pereda-Fernández, 2021. "Copula-Based Random Effects Models for Clustered Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(2), pages 575-588, March.
  29. Franco Peracchi & Claudio Rossetti, 2022. "A nonlinear dynamic factor model of health and medical treatment," Health Economics, John Wiley & Sons, Ltd., vol. 31(6), pages 1046-1066, June.
  30. Christian Bayer & Markus Siebenmorgen & Raul Tempone, 2016. "Smoothing the payoff for efficient computation of Basket option prices," Papers 1607.05572, arXiv.org, revised Feb 2017.
  31. Michael Chen & Sanjay Mehrotra & Dávid Papp, 2015. "Scenario generation for stochastic optimization problems via the sparse grid method," Computational Optimization and Applications, Springer, vol. 62(3), pages 669-692, December.
  32. Zsolt Sándor, 2019. "Further evidence on sparse grids-based numerical integration in the mixed logit model," Economics Bulletin, AccessEcon, vol. 39(4), pages 2726-2731.
  33. Li, Luxin & Chen, Guohai & Fang, Mingxuan & Yang, Dixiong, 2021. "Reliability analysis of structures with multimodal distributions based on direct probability integral method," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
  34. Silvia Cagnone & Francesco Bartolucci, 2017. "Adaptive Quadrature for Maximum Likelihood Estimation of a Class of Dynamic Latent Variable Models," Computational Economics, Springer;Society for Computational Economics, vol. 49(4), pages 599-622, April.
  35. Øystein Daljord, 2022. "Durable Goods Adoption and the Consumer Discount Factor: A Case Study of the Norwegian Book Market," Management Science, INFORMS, vol. 68(9), pages 6783-6796, September.
  36. Patil, Priyadarshan N. & Dubey, Subodh K. & Pinjari, Abdul R. & Cherchi, Elisabetta & Daziano, Ricardo & Bhat, Chandra R., 2017. "Simulation evaluation of emerging estimation techniques for multinomial probit models," Journal of choice modelling, Elsevier, vol. 23(C), pages 9-20.
  37. Brownlees, Christian T., 2019. "Hierarchical GARCH," Journal of Empirical Finance, Elsevier, vol. 51(C), pages 17-27.
  38. Hui Chen & Antoine Didisheim & Simon Scheidegger, 2021. "Deep Structural Estimation: With an Application to Option Pricing," Papers 2102.09209, arXiv.org.
  39. Florian Heiss & Daniel McFadden & Joachim Winter & Amelie Wuppermann & Bo Zhou, 2016. "Inattention and Switching Costs as Sources of Inertia in Medicare Part D," NBER Working Papers 22765, National Bureau of Economic Research, Inc.
  40. Sarah Gelper & Ralf van der Lans & Gerrit van Bruggen, 2021. "Competition for Attention in Online Social Networks: Implications for Seeding Strategies," Management Science, INFORMS, vol. 67(2), pages 1026-1047, February.
  41. Donald Ngwe, 2017. "Why Outlet Stores Exist: Averting Cannibalization in Product Line Extensions," Marketing Science, INFORMS, vol. 36(4), pages 523-541, July.
  42. Eggleston, Jonathan, 2016. "An efficient decomposition of the expectation of the maximum for the multivariate normal and related distributions," Journal of Econometrics, Elsevier, vol. 195(1), pages 120-133.
  43. Paul Piveteau & gabriel smagghue, 2018. "The Impact of Chinese Competition along the Quality Ladder," 2018 Meeting Papers 509, Society for Economic Dynamics.
  44. Amaresh K Tiwari, 2021. "A Control Function Approach to Estimate Panel Data Binary Response Model," Papers 2102.12927, arXiv.org, revised Sep 2021.
  45. Abay, Kibrom A., 2015. "Evaluating simulation-based approaches and multivariate quadrature on sparse grids in estimating multivariate binary probit models," Economics Letters, Elsevier, vol. 126(C), pages 51-56.
  46. Justine Hastings & Jesse M. Shapiro, 2012. "Mental Accounting and Consumer Choice: Evidence from Commodity Price Shocks," NBER Working Papers 18248, National Bureau of Economic Research, Inc.
  47. Bhat, Chandra R., 2018. "New matrix-based methods for the analytic evaluation of the multivariate cumulative normal distribution function," Transportation Research Part B: Methodological, Elsevier, vol. 109(C), pages 238-256.
  48. Christopher Conlon & Jeff Gortmaker, 2020. "Best practices for differentiated products demand estimation with PyBLP," RAND Journal of Economics, RAND Corporation, vol. 51(4), pages 1108-1161, December.
  49. Xie, Junfei & Wan, Yan & Mills, Kevin & Filliben, James J. & Lei, Yu & Lin, Zongli, 2019. "M-PCM-OFFD: An effective output statistics estimation method for systems of high dimensional uncertainties subject to low-order parameter interactions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 159(C), pages 93-118.
  50. Florian Heiss & Steven F. Venti & David A. Wise, 2014. "The Persistence and Heterogeneity of Health among Older Americans," NBER Working Papers 20306, National Bureau of Economic Research, Inc.
  51. Xuming Tong & Jinghang Chen & Hongyu Miao & Tingting Li & Le Zhang, 2015. "Development of an Agent-Based Model (ABM) to Simulate the Immune System and Integration of a Regression Method to Estimate the Key ABM Parameters by Fitting the Experimental Data," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-14, November.
  52. 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.
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