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Identification and estimation of statistical functionals using incomplete data

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  • Horowitz, Joel L.
  • Manski, Charles F.

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  • Horowitz, Joel L. & Manski, Charles F., 2006. "Identification and estimation of statistical functionals using incomplete data," Journal of Econometrics, Elsevier, vol. 132(2), pages 445-459, June.
    • Handle: RePEc:eee:econom:v:132:y:2006:i:2:p:445-459
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    References listed on IDEAS

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    1. Charles F. Manski & Elie Tamer, 2002. "Inference on Regressions with Interval Data on a Regressor or Outcome," Econometrica, Econometric Society, vol. 70(2), pages 519-546, March.
    2. Donald W. K. Andrews, 2000. "Inconsistency of the Bootstrap when a Parameter Is on the Boundary of the Parameter Space," Econometrica, Econometric Society, vol. 68(2), pages 399-406, March.
    3. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, December.
    4. Guido W. Imbens & Charles F. Manski, 2004. "Confidence Intervals for Partially Identified Parameters," Econometrica, Econometric Society, vol. 72(6), pages 1845-1857, November.
    5. Horowitz, Joel L. & Manski, Charles F., 1998. "Censoring of outcomes and regressors due to survey nonresponse: Identification and estimation using weights and imputations," Journal of Econometrics, Elsevier, vol. 84(1), pages 37-58, May.
    6. Charles F. Manski & John D. Straub, 2000. "Worker Perceptions of Job Insecurity in the Mid-1990s: Evidence from the Survey of Economic Expectations," Journal of Human Resources, University of Wisconsin Press, vol. 35(3), pages 447-479.
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    Cited by:

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    2. Alfonsi, Livia & Namubiru, Mary & Spaziani, Sara, 2022. "Gender Gaps: Back and Here to Stay? Evidence from Skilled Ugandan Workers during COVID-19," Institute for Research on Labor and Employment, Working Paper Series qt44s4b2dk, Institute of Industrial Relations, UC Berkeley.
    3. Tatiana Komarova & Denis Nekipelov & Evgeny Yakovlev, 2018. "Identification, data combination, and the risk of disclosure," Quantitative Economics, Econometric Society, vol. 9(1), pages 395-440, March.
    4. Patrick Schmidt & Matthias Katzfuss & Tilmann Gneiting, 2021. "Interpretation of point forecasts with unknown directive," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 728-743, September.
    5. Liu, Yu-Hsin, 2011. "Incorporating scatter search and threshold accepting in finding maximum likelihood estimates for the multinomial probit model," European Journal of Operational Research, Elsevier, vol. 211(1), pages 130-138, May.
    6. Montiel Olea, José Luis & Nesbit, James, 2021. "(Machine) learning parameter regions," Journal of Econometrics, Elsevier, vol. 222(1), pages 716-744.
    7. Denis Conniffe & Donal O'Neill, 2011. "Efficient Probit Estimation with Partially Missing Covariates," Advances in Econometrics, in: Missing Data Methods: Cross-sectional Methods and Applications, pages 209-245, Emerald Group Publishing Limited.
    8. Carlos Madeira, 2022. "Partial identification of nonlinear peer effects models with missing data," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 158(1), pages 1-18, December.
    9. Gneiting, Tilmann, 2011. "Making and Evaluating Point Forecasts," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 746-762.
    10. McGovern, Mark E. & Canning, David & Bärnighausen, Till, 2018. "Accounting for non-response bias using participation incentives and survey design: An application using gift vouchers," Economics Letters, Elsevier, vol. 171(C), pages 239-244.
    11. Ana Belén Ramos-Guajardo, 2022. "A hierarchical clustering method for random intervals based on a similarity measure," Computational Statistics, Springer, vol. 37(1), pages 229-261, March.
    12. Lee, Ying-Ying & Bhattacharya, Debopam, 2019. "Applied welfare analysis for discrete choice with interval-data on income," Journal of Econometrics, Elsevier, vol. 211(2), pages 361-387.
    13. Minna Genbäck & Elena Stanghellini & Xavier Luna, 2015. "Uncertainty intervals for regression parameters with non-ignorable missingness in the outcome," Statistical Papers, Springer, vol. 56(3), pages 829-847, August.
    14. Lidan Grossmass, 2014. "Obtaining and Predicting the Bounds of Realized Correlations," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 150(III), pages 191-226, September.
    15. Pudney, Stephen, 2009. "Participation in disability benefit programmes: a partial identification analysis of the British Attendance Allowance system," ISER Working Paper Series 2009-19, Institute for Social and Economic Research.
    16. Cunguara, Benedito & Darnhofer, Ika, 2011. "Assessing the impact of improved agricultural technologies on household income in rural Mozambique," Food Policy, Elsevier, vol. 36(3), pages 378-390, June.
    17. Denis Conniffe & Donal O’Neill, 2008. "An Efficient Estimator for Dealing with Missing Data on Explanatory Variables in a Probit Choice Model," Economics Department Working Paper Series n1960908.pdf, Department of Economics, National University of Ireland - Maynooth.
    18. Juan Carlos Escanciano & Lin Zhu, 2013. "Set inferences and sensitivity analysis in semiparametric conditionally identified models," CeMMAP working papers CWP55/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    19. Mark McGovern & David Canning & Till Bärnighausen, 2018. "Accounting for Non-Response Bias using Participation Incentives and Survey Design," CHaRMS Working Papers 18-02, Centre for HeAlth Research at the Management School (CHaRMS).
    20. Esteban-Bravo, Mercedes & Vidal-Sanz, Jose M., 2007. "Worst-case estimation for econometric models with unobservable components," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3330-3354, April.

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