IDEAS home Printed from https://ideas.repec.org/r/anr/reveco/v8y2016p341-377.html
   My bibliography  Save this item

Recent Advances in the Measurement Error Literature

Citations

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


Cited by:

  1. Öberg, Stefan, 2021. "Treatment for natural experiments: How to improve causal estimates using conceptual definitions and substantive interpretations," SocArXiv pkyue, Center for Open Science.
  2. Dong, Hao & Otsu, Taisuke & Taylor, Luke, 2022. "Estimation of varying coefficient models with measurement error," Journal of Econometrics, Elsevier, vol. 230(2), pages 388-415.
  3. De Neve, Jan-Walter & Fink, Günther, 2018. "Children’s education and parental old age survival – Quasi-experimental evidence on the intergenerational effects of human capital investment," Journal of Health Economics, Elsevier, vol. 58(C), pages 76-89.
  4. Aguiar, Victor H. & Serrano, Roberto, 2017. "Slutsky matrix norms: The size, classification, and comparative statics of bounded rationality," Journal of Economic Theory, Elsevier, vol. 172(C), pages 163-201.
  5. Yingyao Hu & Zhongjian Lin, 2018. "Misclassification and the hidden silent rivalry," CeMMAP working papers CWP12/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  6. Kengo Kato & Yuya Sasaki & Takuya Ura, 2021. "Robust inference in deconvolution," Quantitative Economics, Econometric Society, vol. 12(1), pages 109-142, January.
  7. Vitor Possebom, 2021. "Crime and Mismeasured Punishment: Marginal Treatment Effect with Misclassification," Papers 2106.00536, arXiv.org, revised Jul 2023.
  8. Hahn, Jinyong & Hausman, Jerry & Kim, Jeonghwan, 2021. "A small sigma approach to certain problems in errors-in-variables models," Economics Letters, Elsevier, vol. 208(C).
  9. Richard Murphy & Felix Weinhardt, 2020. "Top of the Class: The Importance of Ordinal Rank," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 87(6), pages 2777-2826.
  10. Christian Gourieroux & Joann Jasiak, 2023. "Dynamic deconvolution and identification of independent autoregressive sources," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(2), pages 151-180, March.
  11. Dong, Hao & Millimet, Daniel L., 2023. "Embrace the Noise: It Is OK to Ignore Measurement Error in a Covariate, Sometimes," IZA Discussion Papers 16508, Institute of Labor Economics (IZA).
  12. Peter A.G. van Bergeijk, 2017. "Making Data Measurement Errors Transparent: The Case of the IMF," World Economics, World Economics, 1 Ivory Square, Plantation Wharf, London, United Kingdom, SW11 3UE, vol. 18(3), pages 133-154, July.
  13. Takahide Yanagi, 2019. "Inference on local average treatment effects for misclassified treatment," Econometric Reviews, Taylor & Francis Journals, vol. 38(8), pages 938-960, September.
  14. Zhang, Han, 2021. "How Using Machine Learning Classification as a Variable in Regression Leads to Attenuation Bias and What to Do About It," SocArXiv 453jk, Center for Open Science.
  15. Kato, Kengo & Sasaki, Yuya, 2019. "Uniform confidence bands for nonparametric errors-in-variables regression," Journal of Econometrics, Elsevier, vol. 213(2), pages 516-555.
  16. Dong, Hao & Taylor, Luke, 2022. "Nonparametric Significance Testing In Measurement Error Models," Econometric Theory, Cambridge University Press, vol. 38(3), pages 454-496, June.
  17. Aguiar, Victor H. & Kashaev, Nail & Allen, Roy, 2023. "Prices, profits, proxies, and production," Journal of Econometrics, Elsevier, vol. 235(2), pages 666-693.
  18. Erik Meijer & Edward Oczkowski & Tom Wansbeek, 2021. "How measurement error affects inference in linear regression," Empirical Economics, Springer, vol. 60(1), pages 131-155, January.
  19. Carletto,Calogero & Dillon,Andrew S. & Zezza,Alberto, 2021. "Agricultural Data Collection to Minimize Measurement Error and Maximize Coverage," Policy Research Working Paper Series 9745, The World Bank.
  20. Martin T. Bohl & Nicole Branger & Mark Trede, 2019. "Measurement Errors in Index Trader Positions Data: Is the Price Pressure Hypothesis Still Invalid?," CQE Working Papers 8019, Center for Quantitative Economics (CQE), University of Muenster.
  21. Pablo Mitnik, 2018. "Estimating the Intergenerational Elasticity of Expected Income with Short-Run Income Measures: A Generalized Error-in-Variables Model," Working Papers 2018-045, Human Capital and Economic Opportunity Working Group.
  22. Bastianin, Andrea & Castelnovo, Paolo & Florio, Massimo, 2018. "Evaluating regulatory reform of network industries: a survey of empirical models based on categorical proxies," Utilities Policy, Elsevier, vol. 55(C), pages 115-128.
  23. Tom Boot & Art=uras Juodis, 2023. "Uniform Inference in Linear Error-in-Variables Models: Divide-and-Conquer," Papers 2301.04439, arXiv.org.
  24. Hu, Yingyao & Schennach, Susanne & Shiu, Ji-Liang, 2022. "Identification of nonparametric monotonic regression models with continuous nonclassical measurement errors," Journal of Econometrics, Elsevier, vol. 226(2), pages 269-294.
  25. Li, Siran & Zheng, Xunjie, 2020. "A generalization of Lemma 1 in Kotlarski (1967)," Statistics & Probability Letters, Elsevier, vol. 165(C).
  26. Andrew Chesher & Adam Rosen, 2018. "Generalized instrumental variable models, methods, and applications," CeMMAP working papers CWP43/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  27. Hu, Yingyao, 2021. "Identification of Causal Models with Unobservables: A Self-Report Approach," Economics Working Paper Archive 64330, The Johns Hopkins University,Department of Economics.
  28. Chen, Linkun & Clarke, Philip M. & Petrie, Dennis J. & Staub, Kevin E., 2021. "The effects of self-assessed health: Dealing with and understanding misclassification bias," Journal of Health Economics, Elsevier, vol. 78(C).
  29. Schennach, Susanne M., 2019. "Convolution without independence," Journal of Econometrics, Elsevier, vol. 211(1), pages 308-318.
  30. Mochen Yang & Edward McFowland & Gordon Burtch & Gediminas Adomavicius, 2022. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem," INFORMS Joural on Data Science, INFORMS, vol. 1(2), pages 138-155, October.
  31. Mochen Yang & Edward McFowland III & Gordon Burtch & Gediminas Adomavicius, 2020. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem," Papers 2012.10790, arXiv.org.
  32. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Papers 2202.04146, arXiv.org.
  33. Bertrand, Aurelie & Van Keilegom, Ingrid & Legrand, Catherine, 2017. "Flexible parametric approach to classical measurement error variance estimation without auxiliary data," LIDAM Discussion Papers ISBA 2017025, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
  34. Flavio Cunha & Irma Elo & Jennifer Culhane, 2021. "Maternal Subjective Expectations about the Technology of Skill Formation Predict Investments in Children One Year Later," Working Papers 2021-018, Human Capital and Economic Opportunity Working Group.
  35. van Bergeijk, P.A.G., 2017. "Measurement error of global production," ISS Working Papers - General Series 632, International Institute of Social Studies of Erasmus University Rotterdam (ISS), The Hague.
  36. Daniel Wilhelm, 2018. "Testing for the presence of measurement error," CeMMAP working papers CWP45/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  37. Gilles E. Gignac & Elizabeth Ooi, 2022. "Measurement error in research on financial literacy: How much error is there and how does it influence effect size estimates?," Journal of Consumer Affairs, Wiley Blackwell, vol. 56(2), pages 938-956, June.
  38. Eric Blankmeyer, 2018. "Measurement Errors as Bad Leverage Points," Papers 1807.02814, arXiv.org, revised Mar 2020.
  39. Naqib Ullah Khan & Peng Zhongyi & Heesup Han & Antonio Ariza-Montes, 2023. "Linking public leadership and public project success: the mediating role of team building," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-10, December.
  40. Nagasawa, Kenichi, 2020. "Identification and Estimation of Group-Level Partial Effects," The Warwick Economics Research Paper Series (TWERPS) 1243, University of Warwick, Department of Economics.
  41. Cunha, Flávio & Elo, Irma & Culhane, Jennifer, 2022. "Maternal subjective expectations about the technology of skill formation predict investments in children one year later," Journal of Econometrics, Elsevier, vol. 231(1), pages 3-32.
  42. Kato, Kengo & Sasaki, Yuya, 2018. "Uniform confidence bands in deconvolution with unknown error distribution," Journal of Econometrics, Elsevier, vol. 207(1), pages 129-161.
  43. Hao Dong & Taisuke Otsu & Luke Taylor, 2023. "Bandwidth selection for nonparametric regression with errors-in-variables," Econometric Reviews, Taylor & Francis Journals, vol. 42(4), pages 393-419, April.
  44. Hu, Yingyao, 2017. "The econometrics of unobservables: Applications of measurement error models in empirical industrial organization and labor economics," Journal of Econometrics, Elsevier, vol. 200(2), pages 154-168.
  45. Aurélie Bertrand & Ingrid Van Keilegom & Catherine Legrand, 2019. "Flexible parametric approach to classical measurement error variance estimation without auxiliary data," Biometrics, The International Biometric Society, vol. 75(1), pages 297-307, March.
  46. Hao Dong & Yuya Sasaki, 2022. "Estimation of average derivatives of latent regressors: with an application to inference on buffer-stock saving," Departmental Working Papers 2204, Southern Methodist University, Department of Economics.
  47. JoonHwan Cho & Yao Luo & Ruli Xiao, 2022. "Deconvolution from Two Order Statistics," Working Papers tecipa-739, University of Toronto, Department of Economics.
  48. Hao Dong & Daniel L. Millimet, 2020. "Propensity Score Weighting with Mismeasured Covariates: An Application to Two Financial Literacy Interventions," JRFM, MDPI, vol. 13(11), pages 1-24, November.
  49. Mohamed Doukali & Xiaojun Song & Abderrahim Taamouti, 2022. "Value-at Risk under Measurement Error," Working Papers 202209, University of Liverpool, Department of Economics.
  50. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Working Papers 22-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
  51. Kyle L Marquardt, 2020. "How and how much does expert error matter? Implications for quantitative peace research," Journal of Peace Research, Peace Research Institute Oslo, vol. 57(6), pages 692-700, November.
  52. Marica Valente & Timm Gries & Lorenzo Trapani, 2023. "Informal employment from migration shocks," Working Papers 2023-09, Faculty of Economics and Statistics, Universität Innsbruck.
  53. Young Jun Lee & Daniel Wilhelm, 2020. "Testing for the presence of measurement error in Stata," Stata Journal, StataCorp LP, vol. 20(2), pages 382-404, June.
  54. Hong Li & Qifan Song & Jianxi Su, 2021. "Robust estimates of insurance misrepresentation through kernel quantile regression mixtures," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(3), pages 625-663, September.
  55. Andrea Bastianin & Paolo Castelnovo & Massimo Florio, 2017. "The Empirics of Regulatory Reforms Proxied by Categorical Variables: Recent Findings and Methodological Issues," Working Papers 2017.22, Fondazione Eni Enrico Mattei.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.