Bandwidth selection for nonparametric regression with errors-in-variables
Author
Abstract
Suggested Citation
Download full text from publisher
Other versions of this item:
- 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.
- Hao Dong & Taisuke Otsu & Luke Taylor, 2021. "Bandwidth Selection for Nonparametric Regression with Errors-in-Variables," Departmental Working Papers 2104, Southern Methodist University, Department of Economics.
- Hao Dong & Taisuke Otsu & Luke Taylor, 2022. "Bandwidth selection for nonparametric regression with errors-in-variables," STICERD - Econometrics Paper Series 620, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
References listed on IDEAS
- Davezies, Laurent & Le Barbanchon, Thomas, 2017.
"Regression discontinuity design with continuous measurement error in the running variable,"
Journal of Econometrics, Elsevier, vol. 200(2), pages 260-281.
- Laurent Davezies & Thomas Le Barbanchon, 2014. "Regression Discontinuity Design with Continuous Measurement Error in the Running Variable," Working Papers 2014-27, Center for Research in Economics and Statistics.
- Davezies, Laurent & Le Barbanchon, Thomas, 2017. "Regression Discontinuity Design with Continuous Measurement Error in the Running Variable," IZA Discussion Papers 10801, Institute of Labor Economics (IZA).
- Le Barbanchon, Thomas & Davezies, Laurent, 2017. "Regression Discontinuity Design with Continuous Measurement Error in the Running Variable," CEPR Discussion Papers 11775, C.E.P.R. Discussion Papers.
- Masry, E., 1993. "Asymptotic Normality for Deconvolution Estimators of Multivariate Densities of Stationary Processes," Journal of Multivariate Analysis, Elsevier, vol. 44(1), pages 47-68, January.
- Blattman, Christopher & Jamison, Julian & Koroknay-Palicz, Tricia & Rodrigues, Katherine & Sheridan, Margaret, 2016.
"Measuring the measurement error: A method to qualitatively validate survey data,"
Journal of Development Economics, Elsevier, vol. 120(C), pages 99-112.
- Christopher Blattman & Julian C. Jamison & Tricia Koroknay-Palicz & Katherine Rodrigues & Margaret Sheridan, 2015. "Measuring the Measurement Error: A Method to Qualitatively Validate Survey Data," NBER Working Papers 21447, National Bureau of Economic Research, Inc.
- Susanne M. Schennach, 2016. "Recent Advances in the Measurement Error Literature," Annual Review of Economics, Annual Reviews, vol. 8(1), pages 341-377, October.
- Delaigle, A. & Gijbels, I., 2004. "Practical bandwidth selection in deconvolution kernel density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 249-267, March.
- Sebastian Calonico & Matias D. Cattaneo & Max H. Farrell, 2018.
"On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference,"
Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 767-779, April.
- Sebastian Calonico & Matias D. Cattaneo & Max H. Farrell, 2015. "On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference," Papers 1508.02973, arXiv.org, revised Mar 2018.
- Yingyao Hu & Susanne M. Schennach, 2008. "Instrumental Variable Treatment of Nonclassical Measurement Error Models," Econometrica, Econometric Society, vol. 76(1), pages 195-216, January.
- Kato, Kengo & Sasaki, Yuya, 2019. "Uniform confidence bands for nonparametric errors-in-variables regression," Journal of Econometrics, Elsevier, vol. 213(2), pages 516-555.
- 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.
- Schennach, Susanne M., 2019.
"Convolution without independence,"
Journal of Econometrics, Elsevier, vol. 211(1), pages 308-318.
- Susanne M. Schennach, 2013. "Convolution without independence," CeMMAP working papers CWP46/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Susanne M. Schennach, 2013. "Convolution without independence," CeMMAP working papers 46/13, Institute for Fiscal Studies.
- Delaigle, Aurore & Hall, Peter, 2008. "Using SIMEX for Smoothing-Parameter Choice in Errors-in-Variables Problems," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 280-287, March.
- Delaigle, Aurore & Meister, Alexander, 2007. "Nonparametric Regression Estimation in the Heteroscedastic Errors-in-Variables Problem," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1416-1426, December.
- Kato, Kengo & Sasaki, Yuya, 2018. "Uniform confidence bands in deconvolution with unknown error distribution," Journal of Econometrics, Elsevier, vol. 207(1), pages 129-161.
- A. Delaigle & I. Gijbels, 2004. "Bootstrap bandwidth selection in kernel density estimation from a contaminated sample," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 56(1), pages 19-47, March.
- Otávio Bartalotti & Quentin Brummet & Steven Dieterle, 2021.
"A Correction for Regression Discontinuity Designs With Group-Specific Mismeasurement of the Running Variable,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(3), pages 833-848, July.
- Bartalotti, Otávio & Brummet, Quentin & Dieterle, Steven, 2019. "A Correction for Regression Discontinuity Designs with Group-Specific Mismeasurement of the Running Variable," ISU General Staff Papers 201905170700001045, Iowa State University, Department of Economics.
- Bartalotti, Otávio & Brummet, Quentin & Dieterle, Steven G., 2019. "A Correction for Regression Discontinuity Designs with Group-Specific Mismeasurement of the Running Variable," IZA Discussion Papers 12366, Institute of Labor Economics (IZA).
- Bartalotti, Otávio & Brummet, Quentin & Dieterle, Steven, 2020. "A Correction for Regression Discontinuity Designs With Group-Specific Mismeasurement of the Running Variable," ISU General Staff Papers 202004020700001701, Iowa State University, Department of Economics.
- Aurore Delaigle & Peter Hall & Farshid Jamshidi, 2015. "Confidence bands in non-parametric errors-in-variables regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(1), pages 149-169, January.
- Li, Tong & Vuong, Quang, 1998. "Nonparametric Estimation of the Measurement Error Model Using Multiple Indicators," Journal of Multivariate Analysis, Elsevier, vol. 65(2), pages 139-165, May.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Kato, Kengo & Sasaki, Yuya, 2019. "Uniform confidence bands for nonparametric errors-in-variables regression," Journal of Econometrics, Elsevier, vol. 213(2), pages 516-555.
- 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.
- Dong, Hao & Millimet, Daniel L., 2020. "Propensity Score Weighting with Mismeasured Covariates: An Application to Two Financial Literacy Interventions," IZA Discussion Papers 13893, Institute of Labor Economics (IZA).
- Hao Dong & Daniel L. Millimet, 2020. "Propensity Score Weighting with Mismeasured Covariates: An Application to Two Financial Literacy Interventions," Departmental Working Papers 2013, Southern Methodist University, Department of Economics.
- Kato, Kengo & Sasaki, Yuya, 2018. "Uniform confidence bands in deconvolution with unknown error distribution," Journal of Econometrics, Elsevier, vol. 207(1), pages 129-161.
- 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.
- Hao Dong & Yuya Sasaki, 2022. "Estimation of Average Derivatives of Latent Regressors: With an Application to Inference on Buffer-Stock Saving," Papers 2209.05914, arXiv.org.
- Adusumilli, Karun & Kurisu, Daisuke & Otsu, Taisuke & Whang, Yoon-Jae, 2020.
"Inference on distribution functions under measurement error,"
Journal of Econometrics, Elsevier, vol. 215(1), pages 131-164.
- Karun Adusumilli & Taisuke Otsu & Yoon-Jae Whang, "undated". "Inference On Distribution Functions Under Measurement Error," Working Paper Series no108, Institute of Economic Research, Seoul National University.
- Karun Adusumilli & Taisuke Otsu & Yoon-Jae Whang, 2017. "Inference on distribution functions under measurement error," STICERD - Econometrics Paper Series 594, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
- Julie McIntyre & Brent A. Johnson & Stephen M. Rappaport, 2018. "Monte Carlo methods for nonparametric regression with heteroscedastic measurement error," Biometrics, The International Biometric Society, vol. 74(2), pages 498-505, June.
- Kengo Kato & Yuya Sasaki & Takuya Ura, 2021. "Robust inference in deconvolution," Quantitative Economics, Econometric Society, vol. 12(1), pages 109-142, January.
- Adusumilli, Karun & Kurisu, Daisies & Otsu, Taisuke & Whang, Yoon-Jae, 2020. "Inference on distribution functions under measurement error," LSE Research Online Documents on Economics 102692, London School of Economics and Political Science, LSE Library.
- Delaigle, Aurore & Fan, Jianqing & Carroll, Raymond J., 2009. "A Design-Adaptive Local Polynomial Estimator for the Errors-in-Variables Problem," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 348-359.
- 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.
- Hao Dong & Taisuke Otsu & Luke Taylor, 2019. "Estimation of Varying Coefficient Models with Measurement Error," Departmental Working Papers 1905, Southern Methodist University, Department of Economics.
- Dong, Hao & Otsu, Taisuke & Taylor, Luke, 2022. "Estimation of varying coefficient models with measurement error," LSE Research Online Documents on Economics 108147, London School of Economics and Political Science, LSE Library.
- Hao Dong & Taisuke Otsu & Luke Taylor, 2019. "Estimation of Varying Coefficient Models with Measurement Error," STICERD - Econometrics Paper Series 607, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
- Dong, Hao & Taylor, Luke, 2022.
"Nonparametric Significance Testing In Measurement Error Models,"
Econometric Theory, Cambridge University Press, vol. 38(3), pages 454-496, June.
- Hao Dong & Luke Taylor, 2020. "Nonparametric Significance Testing in Measurement Error Models," Departmental Working Papers 2003, Southern Methodist University, Department of Economics.
- Julie McIntyre & Leonard Stefanski, 2011. "Density Estimation with Replicate Heteroscedastic Measurements," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 63(1), pages 81-99, February.
- Ben-Moshe, Dan, 2018. "Identification Of Joint Distributions In Dependent Factor Models," Econometric Theory, Cambridge University Press, vol. 34(1), pages 134-165, February.
- Kengo Kato & Yuya Sasaki & Takuya Ura, 2018. "Inference based on Kotlarski's Identity," Papers 1808.09375, arXiv.org, revised Sep 2019.
- 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.
- Gong, Xiaodong & Gao, Jiti, 2015.
"Nonparametric Kernel Estimation of the Impact of Tax Policy on the Demand for Private Health Insurance in Australia,"
IZA Discussion Papers
9265, Institute of Labor Economics (IZA).
- Xiaodong Gong & Jiti Gao, 2015. "Nonparametric Kernel Estimation of the Impact of Tax Policy on the Demand for Private Health Insurance in Australia," Monash Econometrics and Business Statistics Working Papers 6/15, Monash University, Department of Econometrics and Business Statistics.
- Xiaodong Gong & Jiti Gao, 2017. "Nonparametric kernel estimation of the impact of tax policy on the demand for private health insurance in Australia," Monash Econometrics and Business Statistics Working Papers 7/17, Monash University, Department of Econometrics and Business Statistics.
- Guillermo Basulto-Elias & Alicia L. Carriquiry & Kris Brabanter & Daniel J. Nordman, 2021. "Bivariate Kernel Deconvolution with Panel Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 122-151, May.
- Schennach, Susanne M., 2019.
"Convolution without independence,"
Journal of Econometrics, Elsevier, vol. 211(1), pages 308-318.
- Susanne M. Schennach, 2013. "Convolution without independence," CeMMAP working papers CWP46/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Susanne M. Schennach, 2013. "Convolution without independence," CeMMAP working papers 46/13, Institute for Fiscal Studies.
- Hao Dong & Taisuke Otsu & Luke Taylor, 2022.
"Nonparametric estimation of additive models with errors-in-variables,"
Econometric Reviews, Taylor & Francis Journals, vol. 41(10), pages 1164-1204, November.
- Dong, Hao & Otsu, Taisuke & Taylor, Luke, 2022. "Nonparametric estimation of additive models with errors-in-variables," LSE Research Online Documents on Economics 116007, London School of Economics and Political Science, LSE Library.
- Carroll, Raymond J. & Delaigle, Aurore & Hall, Peter, 2009. "Nonparametric Prediction in Measurement Error Models," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 993-1003.
More about this item
Keywords
measurement error models; deconvolution; nonparametric regression; bandwidth selection;All these keywords.
JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ehl:lserod:115551. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: LSERO Manager (email available below). General contact details of provider: https://edirc.repec.org/data/lsepsuk.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.