IDEAS home Printed from https://ideas.repec.org/e/c/pno189.html
   My authors  Follow this author

Andriy Norets

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Andriy Norets & Xun Tang, 2013. "Semi-Parametric Inference in Dynamic Binary Choice Models," PIER Working Paper Archive 13-054, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

    Cited by:

    1. Xiaohong Chen & Timothy Christensen & Keith O’Hara & Elie Tamer, 2016. "MCMC Confidence sets for Identified Sets," Cowles Foundation Discussion Papers 2037R, Cowles Foundation for Research in Economics, Yale University, revised Jul 2016.
    2. Hanming Fang & Yang Wang, 2010. "Estimating Dynamic Discrete Choice Models with Hyperbolic Discounting, with an Application to Mammography Decisions," PIER Working Paper Archive 10-033, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    3. Victor Aguirregabiria & Arvind Magesan, 2012. "Identification and estimation of dynamic games when players' beliefs are not in equilibrium," Working Papers tecipa-449, University of Toronto, Department of Economics.
    4. Norets, Andriy & Shimizu, Kenichi, 2024. "Semiparametric Bayesian estimation of dynamic discrete choice models," Journal of Econometrics, Elsevier, vol. 238(2).
    5. Jaap H. Abbring & Øystein Daljord, 2020. "Identifying the discount factor in dynamic discrete choice models," Quantitative Economics, Econometric Society, vol. 11(2), pages 471-501, May.
    6. Jiaming Mao & Zhesheng Zheng, 2020. "Structural Regularization," Papers 2004.12601, arXiv.org, revised Jun 2020.
    7. Raffaella Giacomini & Toru Kitagawa, 2018. "Robust Bayesian inference for set-identified models," CeMMAP working papers CWP61/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Merlo, Antonio & Tang, Xun, 2019. "New results on the identification of stochastic bargaining models," Journal of Econometrics, Elsevier, vol. 209(1), pages 79-93.
    9. Francesca Molinari, 2020. "Microeconometrics with Partial Identification," Papers 2004.11751, arXiv.org.
    10. Xiaohong Chen & Timothy M. Christensen & Elie Tamer, 2017. "Monte Carlo confidence sets for identified sets," CeMMAP working papers CWP43/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. Jaap H. Abbring & Øystein Daljord, 2020. "A Comment On “Estimating Dynamic Discrete Choice Models With Hyperbolic Discounting” By Hanming Fang And Yang Wang," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 61(2), pages 565-571, May.
    12. Antonio Merlo & Xun Tang, 2010. "Identification and Estimation of Stochastic Bargaining Models, Third Version," PIER Working Paper Archive 11-008, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 11 Mar 2011.
    13. Abbring, Jaap & Daljord, Øystein, 2016. "Identifying the Discount Factor in Dynamic Discrete Choice Models," CEPR Discussion Papers 11133, C.E.P.R. Discussion Papers.
    14. Aprajit Mahajan & Christian Michel & Alessandro Tarozzi, 2020. "Identification of Time-Inconsistent Models: The Case of Insecticide Treated Nets," NBER Working Papers 27198, National Bureau of Economic Research, Inc.
    15. Higgins, Ayden & Jochmans, Koen, 2021. "Identification Of Mixtures Of Dynamic Discrete Choices," TSE Working Papers 21-1272, Toulouse School of Economics (TSE), revised Jan 2023.
    16. Raffaella Giacomini & Toru Kitagawa & Alessio Volpicella, 2017. "Uncertain identification," CeMMAP working papers 18/17, Institute for Fiscal Studies.
    17. Jean-Pierre Florens & Anna Simoni, 2021. "Revisiting identification concepts in Bayesian analysis," Papers 2110.09954, arXiv.org.
    18. Liu, Xiaobin & Li, Yong & Yu, Jun & Zeng, Tao, 2022. "Posterior-based Wald-type statistics for hypothesis testing," Journal of Econometrics, Elsevier, vol. 230(1), pages 83-113.
    19. Brendan Kline & Elie Tamer, 2024. "Counterfactual Analysis in Empirical Games," Papers 2410.12731, arXiv.org.
    20. Myrto Kalouptsidi & Paul T. Scott & Eduardo Souza‐Rodrigues, 2021. "Identification of counterfactuals in dynamic discrete choice models," Quantitative Economics, Econometric Society, vol. 12(2), pages 351-403, May.
    21. Xiaohong Chen & Timothy M. Christensen & Elie Tamer, 2017. "Monte Carlo confidence sets for identified sets," CeMMAP working papers 43/17, Institute for Fiscal Studies.
    22. Buchholz, Nicholas & Shum, Matthew & Xu, Haiqing, 2021. "Semiparametric estimation of dynamic discrete choice models," Journal of Econometrics, Elsevier, vol. 223(2), pages 312-327.
    23. Brendan Kline & Elie Tamer, 2016. "Bayesian inference in a class of partially identified models," Quantitative Economics, Econometric Society, vol. 7(2), pages 329-366, July.
    24. Taiga Tsubota, 2021. "Identifying Dynamic Discrete Choice Models with Hyperbolic Discounting," Papers 2111.10721, arXiv.org, revised Oct 2024.
    25. Patrick Bajari & Chenghuan Sean Chu & Denis Nekipelov & Minjung Park, 2016. "Identification and semiparametric estimation of a finite horizon dynamic discrete choice model with a terminating action," Quantitative Marketing and Economics (QME), Springer, vol. 14(4), pages 271-323, December.
    26. Myrto Kalouptsidi & Paul T. Scott & Eduardo Souza-Rodrigues, 2015. "Identification of Counterfactuals in Dynamic Discrete Choice Models," NBER Working Papers 21527, National Bureau of Economic Research, Inc.
    27. Jason R. Blevins & Wei Shi & Donald R. Haurin & Stephanie Moulton, 2020. "A Dynamic Discrete Choice Model Of Reverse Mortgage Borrower Behavior," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 61(4), pages 1437-1477, November.
    28. Sasaki, Yuya & Takahashi, Yuya & Xin, Yi & Hu, Yingyao, 2023. "Dynamic discrete choice models with incomplete data: Sharp identification," Journal of Econometrics, Elsevier, vol. 236(1).
    29. Kalouptsidi, Myrto & Scott, Paul & Souza-Rodrigues, Edouardo, 2015. "Identification of Counterfactuals and Payoffs in Dynamic Discrete Choice with an Application to Land Use," TSE Working Papers 15-596, Toulouse School of Economics (TSE).
    30. Schiraldi, Pasquale & Levy, Matthew R., 2020. "Identification of intertemporal preferences in history-dependent dynamic discrete choice models," CEPR Discussion Papers 14447, C.E.P.R. Discussion Papers.
    31. Philipp Eisenhauer & Lena Janys & Christopher Walsh & Janós Gabler, 2023. "Structural Models for Policy-Making," CRC TR 224 Discussion Paper Series crctr224_2023_484, University of Bonn and University of Mannheim, Germany.
    32. Antonio Merlo & Xun Tang, 2011. "Identification and Estimation of Stochastic Bargaining Models, Fourth Version," PIER Working Paper Archive 11-035, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 19 Oct 2011.
    33. Arcidiacono, Peter & Miller, Robert A., 2020. "Identifying dynamic discrete choice models off short panels," Journal of Econometrics, Elsevier, vol. 215(2), pages 473-485.
    34. Khai Chiong & Alfred Galichon & Matt Shum, 2015. "Duality in Dynamic Discrete Choice Models," SciencePo Working papers Main hal-03568184, HAL.
    35. Xiaohong Chen & Timothy M. Christensen & Keith O'Hara & Elie Tamer, 2016. "MCMC confidence sets for identified sets," CeMMAP working papers 28/16, Institute for Fiscal Studies.
    36. Kalouptsidi, Myrto & Scott, Paul T. & Souza-Rodrigues, Eduardo, 2017. "On the non-identification of counterfactuals in dynamic discrete games," International Journal of Industrial Organization, Elsevier, vol. 50(C), pages 362-371.
    37. Yuan Liao & Anna Simoni, 2019. "Bayesian inference for partially identified smooth convex models," Post-Print hal-03089881, HAL.
    38. Kalouptsidi, Myrto & Scott, Paul T. & Souza-Rodrigues, Eduardo, 2021. "Linear IV regression estimators for structural dynamic discrete choice models," Journal of Econometrics, Elsevier, vol. 222(1), pages 778-804.
    39. Kalouptsidi, Myrto & Scott, Paul T. & Souza-Rodrigues, Eduardo, 2018. "Linear IV Regression Estimators for Structural Dynamic Discrete Choice Models," CEPR Discussion Papers 13240, C.E.P.R. Discussion Papers.
    40. Khai Chiong & Alfred Galichon & Matt Shum, 2015. "Duality in Dynamic Discrete Choice Models," Post-Print hal-03568184, HAL.
    41. 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.
    42. Myrto Kalouptsidi & Paul T. Scott & Eduardo Souza-Rodrigues, 2018. "Linear IV Regression Estimators for Structural Dynamic Discrete Choice Models," NBER Working Papers 25134, National Bureau of Economic Research, Inc.
    43. Cheng Chou & Geert Ridder & Ruoyao Shi, 2024. "Identification and Estimation of Nonstationary Dynamic Binary Choice Models," Working Papers 202402, University of California at Riverside, Department of Economics.
    44. Emanuele Bacchiocchi & Toru Kitagawa, 2022. "Locally- but not Globally-identified SVARs," Working Papers wp1171, Dipartimento Scienze Economiche, Universita' di Bologna.
    45. Raffaella Giacomini & Toru Kitagawa & Harald Uhlig, 2019. "Estimation Under Ambiguity," CeMMAP working papers CWP24/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    46. Kalouptsidi, Myrto & Souza-Rodrigues, Eduardo & Scott, Paul, 2017. "Identification of Counterfactuals in Dynamic Discrete Choice Models," CEPR Discussion Papers 12470, C.E.P.R. Discussion Papers.
    47. Khai Xiang Chiong & Alfred Galichon & Matt Shum, 2021. "Duality in dynamic discrete-choice models," Papers 2102.06076, arXiv.org, revised Feb 2021.
    48. Schneider, Ulrich, 2019. "Identification of Time Preferences in Dynamic Discrete Choice Models: Exploiting Choice Restrictions," MPRA Paper 102137, University Library of Munich, Germany, revised 29 Jul 2020.
    49. Yuan Liao & Anna Simoni, 2016. "Bayesian Inference for Partially Identified Convex Models: Is it Valid for Frequentist Inference?," Departmental Working Papers 201607, Rutgers University, Department of Economics.

  2. Norets, Andriy & Pelenis, Justinas, 2011. "Posterior Consistency in Conditional Density Estimation by Covariate Dependent Mixtures," Economics Series 282, Institute for Advanced Studies.

    Cited by:

    1. Hien Duy Nguyen & TrungTin Nguyen & Faicel Chamroukhi & Geoffrey John McLachlan, 2021. "Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models," Journal of Statistical Distributions and Applications, Springer, vol. 8(1), pages 1-15, December.
    2. Pelenis, Justinas, 2012. "Bayesian Semiparametric Regression," Economics Series 285, Institute for Advanced Studies.
    3. A. R. Linero, 2017. "Bayesian nonparametric analysis of longitudinal studies in the presence of informative missingness," Biometrika, Biometrika Trust, vol. 104(2), pages 327-341.
    4. Federico Bassetti & Roberto Casarin & Francesco Ravazzolo, 2015. "Bayesian nonparametric calibration and combination of predictive distributions," Working Paper 2015/03, Norges Bank.
    5. Taisuke Nakata & Christopher Tonetti, 2015. "Small Sample Properties of Bayesian Estimators of Labor Income Processes," Journal of Applied Economics, Taylor & Francis Journals, vol. 18(1), pages 121-148, May.
    6. Norets, Andriy, 2015. "Bayesian regression with nonparametric heteroskedasticity," Journal of Econometrics, Elsevier, vol. 185(2), pages 409-419.
    7. Norets, Andriy & Pelenis, Justinas, 2012. "Bayesian modeling of joint and conditional distributions," Journal of Econometrics, Elsevier, vol. 168(2), pages 332-346.
    8. Norets, Andriy & Pelenis, Justinas, 2022. "Adaptive Bayesian estimation of conditional discrete-continuous distributions with an application to stock market trading activity," Journal of Econometrics, Elsevier, vol. 230(1), pages 62-82.
    9. Barrientos, Andrés F. & Canale, Antonio, 2021. "A Bayesian goodness-of-fit test for regression," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).
    10. Villani, Mattias & Kohn, Robert & Nott, David J., 2012. "Generalized smooth finite mixtures," Journal of Econometrics, Elsevier, vol. 171(2), pages 121-133.
    11. Debdeep Pati & David Dunson, 2014. "Bayesian nonparametric regression with varying residual density," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(1), pages 1-31, February.

  3. Andriy Norets & Xun Tang, 2010. "Semiparametric Inference in Dynamic Binary Choice Models, Second Version," PIER Working Paper Archive 12-017, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 17 Apr 2012.

    Cited by:

    1. Mira Frick & Ryota Iijima & Tomasz Strzalecki, 2019. "Dynamic Random Utility," Econometrica, Econometric Society, vol. 87(6), pages 1941-2002, November.
    2. Victor Aguirregabiria & Arvind Magesan, 2012. "Identification and estimation of dynamic games when players' beliefs are not in equilibrium," Working Papers tecipa-449, University of Toronto, Department of Economics.
    3. Jiaming Mao & Zhesheng Zheng, 2020. "Structural Regularization," Papers 2004.12601, arXiv.org, revised Jun 2020.
    4. Raffaella Giacomini & Toru Kitagawa, 2018. "Robust Bayesian inference for set-identified models," CeMMAP working papers CWP61/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Jaap H. Abbring & Øystein Daljord, 2020. "A Comment On “Estimating Dynamic Discrete Choice Models With Hyperbolic Discounting” By Hanming Fang And Yang Wang," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 61(2), pages 565-571, May.
    6. Abbring, Jaap & Daljord, Øystein, 2016. "Identifying the Discount Factor in Dynamic Discrete Choice Models," CEPR Discussion Papers 11133, C.E.P.R. Discussion Papers.
    7. Yuan Liao & Anna Simoni, 2012. "Semi-parametric Bayesian Partially Identified Models based on Support Function," Papers 1212.3267, arXiv.org, revised Nov 2013.
    8. Raffaella Giacomini & Toru Kitagawa & Alessio Volpicella, 2017. "Uncertain identification," CeMMAP working papers 18/17, Institute for Fiscal Studies.
    9. George-Levi Gayle & Limor Golan & Mehmet A. Soytas, 2015. "What Accounts for the Racial Gap in Time Allocation and Intergenerational Transmission of Human Capital?," Working Papers 2015-18, Federal Reserve Bank of St. Louis.
    10. Liu, Xiaobin & Li, Yong & Yu, Jun & Zeng, Tao, 2022. "Posterior-based Wald-type statistics for hypothesis testing," Journal of Econometrics, Elsevier, vol. 230(1), pages 83-113.
    11. Victor Aguirregabiria & Junichi Suzuki, 2014. "Identification and counterfactuals in dynamic models of market entry and exit," Quantitative Marketing and Economics (QME), Springer, vol. 12(3), pages 267-304, September.
    12. Patrick Bajari & Chenghuan Sean Chu & Denis Nekipelov & Minjung Park, 2016. "Identification and semiparametric estimation of a finite horizon dynamic discrete choice model with a terminating action," Quantitative Marketing and Economics (QME), Springer, vol. 14(4), pages 271-323, December.
    13. Myrto Kalouptsidi & Paul T. Scott & Eduardo Souza-Rodrigues, 2015. "Identification of Counterfactuals in Dynamic Discrete Choice Models," NBER Working Papers 21527, National Bureau of Economic Research, Inc.
    14. Jason R. Blevins & Wei Shi & Donald R. Haurin & Stephanie Moulton, 2020. "A Dynamic Discrete Choice Model Of Reverse Mortgage Borrower Behavior," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 61(4), pages 1437-1477, November.
    15. Yingyao Hu & Yi Xin, 2019. "Identi?cation and estimation of dynamic structural models with unobserved choices," CeMMAP working papers CWP35/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    16. Kalouptsidi, Myrto & Scott, Paul & Souza-Rodrigues, Edouardo, 2015. "Identification of Counterfactuals and Payoffs in Dynamic Discrete Choice with an Application to Land Use," TSE Working Papers 15-596, Toulouse School of Economics (TSE).
    17. Kalouptsidi, Myrto & Scott, Paul T. & Souza-Rodrigues, Eduardo, 2018. "Linear IV Regression Estimators for Structural Dynamic Discrete Choice Models," CEPR Discussion Papers 13240, C.E.P.R. Discussion Papers.
    18. Myrto Kalouptsidi & Paul T. Scott & Eduardo Souza-Rodrigues, 2018. "Linear IV Regression Estimators for Structural Dynamic Discrete Choice Models," NBER Working Papers 25134, National Bureau of Economic Research, Inc.
    19. Otero, Karina V., 2016. "Nonparametric identification of dynamic multinomial choice games: unknown payoffs and shocks without interchangeability," MPRA Paper 86784, University Library of Munich, Germany.
    20. Raffaella Giacomini & Toru Kitagawa & Harald Uhlig, 2019. "Estimation Under Ambiguity," CeMMAP working papers CWP24/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    21. Rahul Singh & Liyuan Xu & Arthur Gretton, 2020. "Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves," Papers 2010.04855, arXiv.org, revised Oct 2022.
    22. Kalouptsidi, Myrto & Souza-Rodrigues, Eduardo & Scott, Paul, 2017. "Identification of Counterfactuals in Dynamic Discrete Choice Models," CEPR Discussion Papers 12470, C.E.P.R. Discussion Papers.
    23. Khai Xiang Chiong & Alfred Galichon & Matt Shum, 2021. "Duality in dynamic discrete-choice models," Papers 2102.06076, arXiv.org, revised Feb 2021.
    24. Florian Gunsilius, 2019. "A path-sampling method to partially identify causal effects in instrumental variable models," Papers 1910.09502, arXiv.org, revised Jun 2020.

  4. Sam Schulhofer-Wohl & Andriy Norets, 2009. "Heterogeneity in income processes," 2009 Meeting Papers 999, Society for Economic Dynamics.

    Cited by:

    1. Vasia Panousi & Ivan Vidangos & Giovanni Violante & Bradley Heim & Fatih Guvenen, 2010. "Idiosyncratic Income Risk Estimated From IRS Administrative Wage Data," 2010 Meeting Papers 108, Society for Economic Dynamics.

Articles

  1. Norets, Andriy, 2021. "Optimal Auxiliary Priors And Reversible Jump Proposals For A Class Of Variable Dimension Models," Econometric Theory, Cambridge University Press, vol. 37(1), pages 49-81, February.

    Cited by:

    1. Norets, Andriy & Shimizu, Kenichi, 2024. "Semiparametric Bayesian estimation of dynamic discrete choice models," Journal of Econometrics, Elsevier, vol. 238(2).
    2. Andriy Norets & Justinas Pelenis, 2022. "Adaptive Bayesian Estimation of Discrete‐Continuous Distributions Under Smoothness and Sparsity," Econometrica, Econometric Society, vol. 90(3), pages 1355-1377, May.
    3. Norets, Andriy & Pelenis, Justinas, 2022. "Adaptive Bayesian estimation of conditional discrete-continuous distributions with an application to stock market trading activity," Journal of Econometrics, Elsevier, vol. 230(1), pages 62-82.

  2. Norets, Andriy & Pati, Debdeep, 2017. "Adaptive Bayesian Estimation Of Conditional Densities," Econometric Theory, Cambridge University Press, vol. 33(4), pages 980-1012, August.

    Cited by:

    1. Boyarchenko, Nina & Adrian, Tobias & Giannone, Domenico, 2020. "Multimodality in Macro-Financial Dynamics," CEPR Discussion Papers 15088, C.E.P.R. Discussion Papers.
    2. Laura Liu, 2018. "Density Forecasts in Panel Data Models: A Semiparametric Bayesian Perspective," Papers 1805.04178, arXiv.org, revised Oct 2021.
    3. Kalli, Maria & Griffin, Jim E., 2018. "Bayesian nonparametric vector autoregressive models," Journal of Econometrics, Elsevier, vol. 203(2), pages 267-282.
    4. Norets, Andriy & Pelenis, Justinas, 2022. "Adaptive Bayesian estimation of conditional discrete-continuous distributions with an application to stock market trading activity," Journal of Econometrics, Elsevier, vol. 230(1), pages 62-82.

  3. Ulrich K. Müller & Andriy Norets, 2016. "Credibility of Confidence Sets in Nonstandard Econometric Problems," Econometrica, Econometric Society, vol. 84, pages 2183-2213, November.

    Cited by:

    1. Gregory Fletcher Cox, 2024. "A Simple and Adaptive Confidence Interval when Nuisance Parameters Satisfy an Inequality," Papers 2409.09962, arXiv.org.
    2. Khalaf, Lynda & Lin, Zhenjiang, 2021. "Projection-based inference with particle swarm optimization," Journal of Economic Dynamics and Control, Elsevier, vol. 128(C).
    3. Chenchuan (Mark) Li & Ulrich K. Müller, 2021. "Linear regression with many controls of limited explanatory power," Quantitative Economics, Econometric Society, vol. 12(2), pages 405-442, May.
    4. Ulrich K. Müller & Mark W. Watson, 2018. "Long†Run Covariability," Econometrica, Econometric Society, vol. 86(3), pages 775-804, May.
    5. Kaplan, David M. & Zhuo, Longhao, 2021. "Frequentist properties of Bayesian inequality tests," Journal of Econometrics, Elsevier, vol. 221(1), pages 312-336.
    6. Zhou, Bo, 2017. "Semiparametric inference for non-LAN models," Other publications TiSEM 0ea4fd8a-937d-4c19-8f77-f, Tilburg University, School of Economics and Management.
    7. David M. Kaplan & Longhao Zhuo, 2017. "Frequentist size of Bayesian inequality tests," Working Papers 1709, Department of Economics, University of Missouri, revised 14 Jul 2019.
    8. Roy Allen & John Rehbeck, 2020. "Counterfactual and Welfare Analysis with an Approximate Model," Papers 2009.03379, arXiv.org.
    9. Philipp Ketz, 2019. "On asymptotic size distortions in the random coefficients logit model," Post-Print halshs-02302067, HAL.
    10. Sebastian Calonico & Matias D. Cattaneo & Max H. Farrell, 2018. "Coverage Error Optimal Confidence Intervals for Local Polynomial Regression," Papers 1808.01398, arXiv.org, revised Jul 2021.
    11. Christian Gourieroux & Joann Jasiak, 2022. "Long Run Risk in Stationary Structural Vector Autoregressive Models," Papers 2202.09473, arXiv.org.
    12. Ulrich K. Muller & Mark W. Watson, 2021. "Spatial Correlation Robust Inference," Papers 2102.09353, arXiv.org.

  4. Ulrich K. Müller & Andriy Norets, 2016. "Coverage Inducing Priors in Nonstandard Inference Problems," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1233-1241, July.

    Cited by:

    1. Alvarez, Luis Antonio, 2023. "Approximate Bayesian Computation for Partially Identified Models," MPRA Paper 117339, University Library of Munich, Germany.

  5. Norets, Andriy, 2015. "Bayesian regression with nonparametric heteroskedasticity," Journal of Econometrics, Elsevier, vol. 185(2), pages 409-419.

    Cited by:

    1. Matthew J. Salois & Kelvin G. Balcombe, 2015. "A Generalized Bayesian Instrumental Variable Approach under Student t-distributed Errors with Application," Manchester School, University of Manchester, vol. 83(5), pages 499-522, September.
    2. Kaplan, David M. & Zhuo, Longhao, 2021. "Frequentist properties of Bayesian inequality tests," Journal of Econometrics, Elsevier, vol. 221(1), pages 312-336.
    3. David M. Kaplan & Longhao Zhuo, 2017. "Frequentist size of Bayesian inequality tests," Working Papers 1709, Department of Economics, University of Missouri, revised 14 Jul 2019.
    4. Yuan Liao & Anna Simoni, 2019. "Bayesian inference for partially identified smooth convex models," Post-Print hal-03089881, HAL.
    5. Kline, Brendan, 2024. "Classical p-values and the Bayesian posterior probability that the hypothesis is approximately true," Journal of Econometrics, Elsevier, vol. 240(1).
    6. Yuan Liao & Anna Simoni, 2016. "Bayesian Inference for Partially Identified Convex Models: Is it Valid for Frequentist Inference?," Departmental Working Papers 201607, Rutgers University, Department of Economics.
    7. Nguyen Ngoc Thach & Bui Hoang Ngoc, 2021. "Impact of Economic Freedom on Corruption Revisited in ASEAN Countries: A Bayesian Hierarchical Mixed-Effects Analysis," Economies, MDPI, vol. 9(1), pages 1-16, January.
    8. Christoph Breunig & Ruixuan Liu & Zhengfei Yu, 2022. "Double Robust Bayesian Inference on Average Treatment Effects," Papers 2211.16298, arXiv.org, revised Oct 2024.
    9. Christopher D. Walker, 2023. "Parametrization, Prior Independence, and the Semiparametric Bernstein-von Mises Theorem for the Partially Linear Model," Papers 2306.03816, arXiv.org, revised Feb 2024.
    10. Lewis, Gabriel, 2022. "Heteroskedasticity and Clustered Covariances from a Bayesian Perspective," MPRA Paper 116662, University Library of Munich, Germany.

  6. A. Norets & X. Tang, 2014. "Semiparametric Inference in Dynamic Binary Choice Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(3), pages 1229-1262.
    See citations under working paper version above.
  7. Norets, Andriy & Pelenis, Justinas, 2014. "Posterior Consistency In Conditional Density Estimation By Covariate Dependent Mixtures," Econometric Theory, Cambridge University Press, vol. 30(3), pages 606-646, June.
    See citations under working paper version above.
  8. Andriy Norets & Satoru Takahashi, 2013. "On the surjectivity of the mapping between utilities and choice probabilities," Quantitative Economics, Econometric Society, vol. 4(1), pages 149-155, March.

    Cited by:

    1. Mogens Fosgerau & Julien Monardo & André de Palma, 2024. "The Inverse Product Differentiation Logit Model," American Economic Journal: Microeconomics, American Economic Association, vol. 16(4), pages 329-370, November.
    2. Odran Bonnet & Alfred Galichon & Yu-Wei Hsieh & Keith O'Hara & Matt Shum, 2021. "Yogurts Choose Consumers? Estimation of Random-Utility Models via Two-Sided Matching," Papers 2111.13744, arXiv.org.
    3. Jesper R.-V. Soerensen & Mogens Fosgerau, 2020. "How McFadden met Rockafellar and learnt to do more with less," Discussion Papers 20-01, University of Copenhagen. Department of Economics.
    4. Mogens Fosgerau & Emerson Melo & André de Palma & Matthew Shum, 2017. "Discrete Choice and Rational Inattention: a General Equivalence Result," Discussion Papers 17-26, University of Copenhagen. Department of Economics.
    5. Vinit Kumar Mishra & Karthik Natarajan & Dhanesh Padmanabhan & Chung-Piaw Teo & Xiaobo Li, 2014. "On Theoretical and Empirical Aspects of Marginal Distribution Choice Models," Management Science, INFORMS, vol. 60(6), pages 1511-1531, June.
    6. Erhao Xie, 2022. "Nonparametric Identification of Incomplete Information Discrete Games with Non-equilibrium Behaviors," Staff Working Papers 22-22, Bank of Canada.
    7. Batram, Manuel & Bauer, Dietmar, 2019. "On consistency of the MACML approach to discrete choice modelling," Journal of choice modelling, Elsevier, vol. 30(C), pages 1-16.
    8. Haoge Chang & Yusuke Narita & Kota Saito, 2022. "Approximating Choice Data by Discrete Choice Models," Papers 2205.01882, arXiv.org, revised Dec 2023.
    9. Patrick Bajari & Chenghuan Sean Chu & Denis Nekipelov & Minjung Park, 2016. "Identification and semiparametric estimation of a finite horizon dynamic discrete choice model with a terminating action," Quantitative Marketing and Economics (QME), Springer, vol. 14(4), pages 271-323, December.
    10. Fosgerau, Mogens & Lindberg, Per Olov & Mattsson, Lars-Göran & Weibull, Jörgen, 2015. "Invariance of the distribution of the maximum," MPRA Paper 63538, University Library of Munich, Germany.
    11. Jason R. Blevins & Wei Shi & Donald R. Haurin & Stephanie Moulton, 2020. "A Dynamic Discrete Choice Model Of Reverse Mortgage Borrower Behavior," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 61(4), pages 1437-1477, November.
    12. Komarova, Tatiana & Sanches, Fábio Adriano & Silva Junior, Daniel & Srisuma, Sorawoot, 2018. "Joint analysis of the discount factor and payoff parameters in dynamic discrete choice games," LSE Research Online Documents on Economics 86858, London School of Economics and Political Science, LSE Library.
    13. Øystein Daljord & Denis Nekipelov & Minjung Park, 2019. "Comments on “identification and semiparametric estimation of a finite horizon dynamic discrete choice model with a terminating action”," Quantitative Marketing and Economics (QME), Springer, vol. 17(4), pages 439-449, December.
    14. David Muller & Yurii Nesterov & Vladimir Shikhman, 2019. "Discrete choice prox-functions on the simplex," Papers 1909.05591, arXiv.org.
    15. Jean-Pierre H. Dube & Günter J. Hitsch & Pranav Jindal, 2012. "The Joint Identification of Utility and Discount Functions From Stated Choice Data: An Application to Durable Goods Adoption," NBER Working Papers 18393, National Bureau of Economic Research, Inc.
    16. Roy Allen, 2019. "Injectivity and the Law of Demand," Papers 1908.05714, arXiv.org.
    17. Mogens Fosgerau & Emerson Melo & Matthew Shum & Jesper R.-V. Sørensen, 2021. "Some Remarks on CCP-based Estimators of Dynamic Models," Discussion Papers 21-03, University of Copenhagen. Department of Economics.
    18. Guiyun Feng & Xiaobo Li & Zizhuo Wang, 2017. "Technical Note—On the Relation Between Several Discrete Choice Models," Operations Research, INFORMS, vol. 65(6), pages 1516-1525, December.
    19. Scott, Paul, 2014. "Dynamic Discrete Choice Estimation of Agricultural Land Use," TSE Working Papers 14-526, Toulouse School of Economics (TSE).
    20. Jean-Pierre Dubé & Günter Hitsch & Pranav Jindal, 2014. "The Joint identification of utility and discount functions from stated choice data: An application to durable goods adoption," Quantitative Marketing and Economics (QME), Springer, vol. 12(4), pages 331-377, December.
    21. Fabio A. Miessi Sanches & Daniel Silva Junior, Sorawoot Srisuma, 2014. "Ordinary Least Squares Estimation for a Dynamic Game," Working Papers, Department of Economics 2014_19, University of São Paulo (FEA-USP), revised 23 Feb 2015.
    22. Khai Xiang Chiong & Alfred Galichon & Matt Shum, 2021. "Duality in dynamic discrete-choice models," Papers 2102.06076, arXiv.org, revised Feb 2021.

  9. Andriy Norets, 2012. "Estimation of Dynamic Discrete Choice Models Using Artificial Neural Network Approximations," Econometric Reviews, Taylor & Francis Journals, vol. 31(1), pages 84-106.

    Cited by:

    1. 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.
    2. Dennis Kristensen & Bernard Salanie, 2013. "Higher-order properties of approximate estimators," CeMMAP working papers 45/13, Institute for Fiscal Studies.
    3. Aguirregabiria, Victor & Magesan, Arvind, 2013. "Euler Equations for the Estimation of Dynamic Discrete Choice Structural," MPRA Paper 46056, University Library of Munich, Germany.
    4. Haoying Wang & Guohui Wu, 2022. "Modeling discrete choices with large fine-scale spatial data: opportunities and challenges," Journal of Geographical Systems, Springer, vol. 24(3), pages 325-351, July.
    5. Kristensen, Dennis & Mogensen, Patrick K. & Moon, Jong Myun & Schjerning, Bertel, 2021. "Solving dynamic discrete choice models using smoothing and sieve methods," Journal of Econometrics, Elsevier, vol. 223(2), pages 328-360.
    6. Marlon Azinovic & Luca Gaegauf & Simon Scheidegger, 2022. "Deep Equilibrium Nets," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(4), pages 1471-1525, November.
    7. Kase, Hanno & Melosi, Leonardo & Rottner, Matthias, 2024. "Estimating Nonlinear Heterogeneous Agent Models with Neural Networks," The Warwick Economics Research Paper Series (TWERPS) 1499, University of Warwick, Department of Economics.
    8. Victor Duarte & Diogo Duarte & Dejanir H. Silva, 2024. "Machine Learning for Continuous-Time Finance," CESifo Working Paper Series 10909, CESifo.
    9. Matthew Osborne, 2011. "Consumer learning, switching costs, and heterogeneity: A structural examination," Quantitative Marketing and Economics (QME), Springer, vol. 9(1), pages 25-70, March.
    10. Ben Deaner, 2020. "Approximation-Robust Inference in Dynamic Discrete Choice," Papers 2010.11482, arXiv.org.

  10. Norets, Andriy & Pelenis, Justinas, 2012. "Bayesian modeling of joint and conditional distributions," Journal of Econometrics, Elsevier, vol. 168(2), pages 332-346.

    Cited by:

    1. Laura Liu, 2018. "Density Forecasts in Panel Data Models: A Semiparametric Bayesian Perspective," Papers 1805.04178, arXiv.org, revised Oct 2021.
    2. Laura Liu & Hyungsik Roger Moon & Frank Schorfheide, 2018. "Forecasting with Dynamic Panel Data Models," NBER Working Papers 25102, National Bureau of Economic Research, Inc.
    3. Celso Brunetti & Jeffrey H. Harris & Shawn Mankad, 2018. "Bank Holdings and Systemic Risk," Finance and Economics Discussion Series 2018-063, Board of Governors of the Federal Reserve System (U.S.).
    4. Mike G. Tsionas, 2017. "“When, Where, and How” of Efficiency Estimation: Improved Procedures for Stochastic Frontier Modeling," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 948-965, July.
    5. Federico Bassetti & Roberto Casarin & Francesco Ravazzolo, 2015. "Bayesian nonparametric calibration and combination of predictive distributions," Working Paper 2015/03, Norges Bank.
    6. Topaloglou, Nikolas & Tsionas, Mike G., 2020. "Stochastic dominance tests," Journal of Economic Dynamics and Control, Elsevier, vol. 112(C).
    7. Christian Carmona & Luis Nieto-Barajas & Antonio Canale, 2019. "Model-based approach for household clustering with mixed scale variables," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(2), pages 559-583, June.
    8. Fukuyama, Hirofumi & Tsionas, Mike & Tan, Yong, 2023. "Dynamic network data envelopment analysis with a sequential structure and behavioural-causal analysis: Application to the Chinese banking industry," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1360-1373.
    9. Taisuke Nakata & Christopher Tonetti, 2015. "Small Sample Properties of Bayesian Estimators of Labor Income Processes," Journal of Applied Economics, Taylor & Francis Journals, vol. 18(1), pages 121-148, May.
    10. Andriy Norets & Justinas Pelenis, 2022. "Adaptive Bayesian Estimation of Discrete‐Continuous Distributions Under Smoothness and Sparsity," Econometrica, Econometric Society, vol. 90(3), pages 1355-1377, May.
    11. Pelenis, Justinas, 2014. "Bayesian regression with heteroscedastic error density and parametric mean function," Journal of Econometrics, Elsevier, vol. 178(P3), pages 624-638.
    12. Norets, Andriy & Pelenis, Justinas, 2022. "Adaptive Bayesian estimation of conditional discrete-continuous distributions with an application to stock market trading activity," Journal of Econometrics, Elsevier, vol. 230(1), pages 62-82.
    13. Federico Bassetti & Roberto Casarin & Marco Del Negro, 2022. "A Bayesian Approach to Inference on Probabilistic Surveys," Staff Reports 1025, Federal Reserve Bank of New York.
    14. Sam Schulhofer-Wohl & Andriy Norets, 2009. "Heterogeneity in income processes," 2009 Meeting Papers 999, Society for Economic Dynamics.

  11. 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.

    Cited by:

    1. Patrick Kofod Mogensen, 2018. "Solving Dynamic Discrete Choice Models: Integrated or Expected Value Function?," Papers 1801.03978, arXiv.org.
    2. Norets, Andriy & Shimizu, Kenichi, 2024. "Semiparametric Bayesian estimation of dynamic discrete choice models," Journal of Econometrics, Elsevier, vol. 238(2).
    3. Timothy M. Christensen, 2018. "Dynamic Models with Robust Decision Makers: Identification and Estimation," Papers 1812.11246, arXiv.org, revised Jan 2019.
    4. Alphonse Hakizimana & Joseph K. Scott, 2017. "Differentiability Conditions for Stochastic Hybrid Systems with Application to the Optimal Design of Microgrids," Journal of Optimization Theory and Applications, Springer, vol. 173(2), pages 658-682, May.
    5. Jackson Bunting, 2022. "Continuous permanent unobserved heterogeneity in dynamic discrete choice models," Papers 2202.03960, arXiv.org, revised Feb 2024.
    6. Kristensen, Dennis & Mogensen, Patrick K. & Moon, Jong Myun & Schjerning, Bertel, 2021. "Solving dynamic discrete choice models using smoothing and sieve methods," Journal of Econometrics, Elsevier, vol. 223(2), pages 328-360.
    7. 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.
    8. 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.
    9. Thomas J. Sargent & John Stachurski, 2024. "Dynamic Programming: Finite States," Papers 2401.10473, arXiv.org.
    10. Armstrong, Timothy B. & Bertanha, Marinho & Hong, Han, 2014. "A fast resample method for parametric and semiparametric models," Journal of Econometrics, Elsevier, vol. 179(2), pages 128-133.
    11. Fabio Blasutto & David de la Croix, 2023. "Catholic Censorship and the Demise of Knowledge Production in Early Modern Italy," The Economic Journal, Royal Economic Society, vol. 133(656), pages 2899-2924.
    12. Otero, Karina V., 2016. "Nonparametric identification of dynamic multinomial choice games: unknown payoffs and shocks without interchangeability," MPRA Paper 86784, University Library of Munich, Germany.
    13. 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.
    14. Timothy M. Christensen, 2020. "Existence and uniqueness of recursive utilities without boundedness," Papers 2008.00963, arXiv.org, revised Aug 2021.

  12. 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.

    Cited by:

    1. Christian Bayer & Falko Juessen, 2006. "On the Dynamics of Interstate Migration: Migration Costs and Self-Selection," Discussion Papers in Economics 06_03, University of Dortmund, Department of Economics.
    2. Aguirregabiria, Victor & Nevo, Aviv, 2010. "Recent developments in empirical IO: dynamic demand and dynamic games," MPRA Paper 27814, University Library of Munich, Germany.
    3. Mira Frick & Ryota Iijima & Tomasz Strzalecki, 2019. "Dynamic Random Utility," Econometrica, Econometric Society, vol. 87(6), pages 1941-2002, November.
    4. Norets, Andriy & Shimizu, Kenichi, 2024. "Semiparametric Bayesian estimation of dynamic discrete choice models," Journal of Econometrics, Elsevier, vol. 238(2).
    5. Andreas Lanz & Gregor Reich & Ole Wilms, 2022. "Adaptive grids for the estimation of dynamic models," Quantitative Marketing and Economics (QME), Springer, vol. 20(2), pages 179-238, June.
    6. Steven T Berry & Giovanni Compiani, 2023. "An Instrumental Variable Approach to Dynamic Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(4), pages 1724-1758.
    7. Andrew Ching & Susumu Imai & Neelam Jain, 2006. "Bayesian Estimation Of Dynamic Discrete Choice Models," Working Paper 1118, Economics Department, Queen's University.
    8. Dennis Kristensen & Bernard Salanie, 2013. "Higher-order properties of approximate estimators," CeMMAP working papers 45/13, Institute for Fiscal Studies.
    9. 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.
    10. Nina Neubecker & Marcel Smolka & Anne Steinbacher, 2017. "Networks And Selection In International Migration To Spain," Economic Inquiry, Western Economic Association International, vol. 55(3), pages 1265-1286, July.
    11. Hiroyuki Kasahara & Katsumi Shimotsu, 2018. "Estimation of Discrete Choice Dynamic Programming Models," The Japanese Economic Review, Japanese Economic Association, vol. 69(1), pages 28-58, March.
    12. Yingyao Hu & Matthew Shum, 2008. "Nonparametric Identification of Dynamic Models with Unobserved State Variables," Economics Working Paper Archive 543, The Johns Hopkins University,Department of Economics.
    13. Hu Yingyao & Shum Matthew & Tan Wei & Xiao Ruli, 2017. "A Simple Estimator for Dynamic Models with Serially Correlated Unobservables," Journal of Econometric Methods, De Gruyter, vol. 6(1), pages 1-16, January.
    14. Liu, Xiaobin & Li, Yong & Yu, Jun & Zeng, Tao, 2022. "Posterior-based Wald-type statistics for hypothesis testing," Journal of Econometrics, Elsevier, vol. 230(1), pages 83-113.
    15. Amoroso, S., 2013. "Heterogeneity of innovative, collaborative, and productive firm-level processes," Other publications TiSEM f5784a49-7053-401d-855d-1, Tilburg University, School of Economics and Management.
    16. Fowler, Stuart J. & Fowler, Jennifer J. & Seagraves, Philip A. & Beauchamp, Charles F., 2018. "A fundamentalist theory of real estate market outcomes," Economic Modelling, Elsevier, vol. 73(C), pages 295-305.
    17. Lucija Muehlenbachs, 2015. "A Dynamic Model Of Cleanup: Estimating Sunk Costs In Oil And Gas Production," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56(1), pages 155-185, February.
    18. Han Hong & Ahmed Khwaja & A. Ronald Gallant, 2008. "Estimating Dynamic Games of Complete Information with an Application to the Generic Pharmaceutical Industry," 2008 Meeting Papers 1050, Society for Economic Dynamics.
    19. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Learning Models: An Assessment of Progress, Challenges and New Developments," Economics Papers 2013-W07, Economics Group, Nuffield College, University of Oxford.
    20. Li, Yong & Yu, Jun & Zeng, Tao, 2017. "A Specification Test based on the MCMC Output," Economics and Statistics Working Papers 9-2017, Singapore Management University, School of Economics.
    21. Myrto Kalouptsidi & Paul T. Scott & Eduardo Souza-Rodrigues, 2015. "Identification of Counterfactuals in Dynamic Discrete Choice Models," NBER Working Papers 21527, National Bureau of Economic Research, Inc.
    22. Hanming Fang & Edward Kung, 2012. "Why Do Life Insurance Policyholders Lapse? The Roles of Income, Health and Bequest Motive Shocks," NBER Working Papers 17899, National Bureau of Economic Research, Inc.
    23. 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.
    24. Matthew Osborne, 2018. "Frequency Versus Depth: How Changing the Temporal Process of Promotions Impacts Demand for a Storable Good," The Japanese Economic Review, Springer, vol. 69(3), pages 258-283, September.
    25. 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.
    26. Kalouptsidi, Myrto & Scott, Paul & Souza-Rodrigues, Edouardo, 2015. "Identification of Counterfactuals and Payoffs in Dynamic Discrete Choice with an Application to Land Use," TSE Working Papers 15-596, Toulouse School of Economics (TSE).
    27. Yu Zheng & Juan Pantano, 2012. "Using Subjective Expectations Data to Allow for Unobserved Heterogeneity in Hotz-Miller Estimation Strategies," 2012 Meeting Papers 940, Society for Economic Dynamics.
    28. Steve Berry & Ahmed Khwaja & Vineet Kumar & Andres Musalem & Kenneth Wilbur & Greg Allenby & Bharat Anand & Pradeep Chintagunta & W. Hanemann & Przemek Jeziorski & Angelo Mele, 2014. "Structural models of complementary choices," Marketing Letters, Springer, vol. 25(3), pages 245-256, September.
    29. Philipp Eisenhauer & Lena Janys & Christopher Walsh & Janós Gabler, 2023. "Structural Models for Policy-Making," CRC TR 224 Discussion Paper Series crctr224_2023_484, University of Bonn and University of Mannheim, Germany.
    30. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Invited Paper ---Learning Models: An Assessment of Progress, Challenges, and New Developments," Marketing Science, INFORMS, vol. 32(6), pages 913-938, November.
    31. Matthew Osborne, 2018. "Approximating the Cost-of-Living Index for a Storable Good," American Economic Journal: Microeconomics, American Economic Association, vol. 10(2), pages 286-314, May.
    32. Jason R. Blevins & Ahmed Khwaja & Nathan Yang, 2018. "Firm Expansion, Size Spillovers, and Market Dominance in Retail Chain Dynamics," Management Science, INFORMS, vol. 64(9), pages 4070-4093.
    33. A. Ronald Gallant & Han Hong & Ahmed Khwaja, 2012. "Bayesian Estimation of a Dynamic Game with Endogenous, Partially Observed, Serially Correlated State," Working Papers 12-01, Duke University, Department of Economics.
    34. Zhou, Yiyi, 2012. "Failure to Launch in Two-Sided Markets: A Study of the U.S. Video Game Market," MPRA Paper 42002, University Library of Munich, Germany.
    35. Jonathan Leightner & Tomoo Inoue & Pierre Lafaye de Micheaux, 2021. "Variable Slope Forecasting Methods and COVID-19 Risk," JRFM, MDPI, vol. 14(10), pages 1-22, October.
    36. 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.
    37. A. Ronald Gallant & Han Hong & Ahmed Khwaja, 2018. "The Dynamic Spillovers of Entry: An Application to the Generic Drug Industry," Management Science, INFORMS, vol. 64(3), pages 1189-1211, March.
    38. Hong, Han & Li, Weiming & Wang, Boyu, 2015. "Estimation of dynamic discrete models from time aggregated data," Journal of Econometrics, Elsevier, vol. 188(2), pages 435-446.
    39. 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.
    40. Yiyi Zhou, 2017. "Bayesian Estimation of a Dynamic Model of Two-Sided Markets: Application to the U.S. Video Game Industry," Management Science, INFORMS, vol. 63(11), pages 3874-3894, November.
    41. Li, Yong & Yu, Jun & Zeng, Tao, 2020. "Deviance information criterion for latent variable models and misspecified models," Journal of Econometrics, Elsevier, vol. 216(2), pages 450-493.
    42. Andriy Norets & Xun Tang, 2013. "Semi-Parametric Inference in Dynamic Binary Choice Models," PIER Working Paper Archive 13-054, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    43. Dennis Kristensen & Bernard Salanié, 2010. "Higher Order Improvements for Approximate Estimators," CAM Working Papers 2010-04, University of Copenhagen. Department of Economics. Centre for Applied Microeconometrics.
    44. Keane, Michael P. & Todd, Petra E. & Wolpin, Kenneth I., 2011. "The Structural Estimation of Behavioral Models: Discrete Choice Dynamic Programming Methods and Applications," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 4, chapter 4, pages 331-461, Elsevier.
    45. Panle Jia Barwick & Parag A. Pathak, 2015. "The costs of free entry: an empirical study of real estate agents in Greater Boston," RAND Journal of Economics, RAND Corporation, vol. 46(1), pages 103-145, March.
    46. Otero, Karina V., 2016. "Nonparametric identification of dynamic multinomial choice games: unknown payoffs and shocks without interchangeability," MPRA Paper 86784, University Library of Munich, Germany.
    47. Dan Bernhardt & Chi Wan & Zhijie Xiao, 2016. "The Reluctant Analyst," Journal of Accounting Research, Wiley Blackwell, vol. 54(4), pages 987-1040, September.
    48. Hu, Yingyao, 2017. "The Econometrics of Unobservables -- Latent Variable and Measurement Error Models and Their Applications in Empirical Industrial Organization and Labor Economics [The Econometrics of Unobservables]," Economics Working Paper Archive 64578, The Johns Hopkins University,Department of Economics, revised 2021.
    49. Sara Amoroso, 2014. "The hidden costs of R&D collaboration," JRC Working Papers on Corporate R&D and Innovation 2014-02, Joint Research Centre.
    50. Khai Xiang Chiong & Alfred Galichon & Matt Shum, 2021. "Duality in dynamic discrete-choice models," Papers 2102.06076, arXiv.org, revised Feb 2021.
    51. Peter Arcidiacono & Robert A. Miller, 2011. "Conditional Choice Probability Estimation of Dynamic Discrete Choice Models With Unobserved Heterogeneity," Econometrica, Econometric Society, vol. 79(6), pages 1823-1867, November.
    52. Matthew Osborne, 2011. "Consumer learning, switching costs, and heterogeneity: A structural examination," Quantitative Marketing and Economics (QME), Springer, vol. 9(1), pages 25-70, March.
    53. Daniel dos Santos, "undated". "The choice of major: effects on wages and an evaluatio of the no-switching majors rule," Working Papers 09_06, Universidade de São Paulo, Faculdade de Economia, Administração e Contabilidade de Ribeirão Preto.
    54. Olivier Toubia & Andrew T. Stephen, 2013. "Intrinsic vs. Image-Related Utility in Social Media: Why Do People Contribute Content to Twitter?," Marketing Science, INFORMS, vol. 32(3), pages 368-392, May.
    55. Li, Yong & Yu, Jun & Zeng, Tao, 2018. "Integrated Deviance Information Criterion for Latent Variable Models," Economics and Statistics Working Papers 6-2018, Singapore Management University, School of Economics.
    56. 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.
    57. Gallant, A. Ronald & Hong, Han & Khwaja, Ahmed, 2018. "A Bayesian approach to estimation of dynamic models with small and large number of heterogeneous players and latent serially correlated states," Journal of Econometrics, Elsevier, vol. 203(1), pages 19-32.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.