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GMM estimation for nonignorable missing data: theory and practice

By Sanha Hemvanich

Abstract

This thesis develops several Generalized Method ofMoments (GMM) estimators for analysing Not Missing at Random (NMAR) data, which is commonly referred to as the self-selection problem in an economic context. We extend the semiparametric estimation procedures of Ramalho and Smith (2003) to include the case where the missing data mechanism (MDM) depends on both a continuous response variable and covariates. Within this framework, it is possible to avoid imposing any assumptions on the missing data mechanism. We also discuss the asymptotic properties of the proposed GMM estimators and establish the connections of them to the GMM estimators of Ramalho and Smith and to the pseudolikelihood estimators of Tang, Little and Raghunathan (2003). All of the aforementioned estimators are then compared to other standard estimators for missing data such as the inverse probability weighted and sample selection model estimators in a number of Monte Carlo experiments. As an empirical application, these estimators are also applied to analyse the UK wage distribution. We found that, in many circumstances, our proposed estimators perform better than the other estimators described; especially when there is no exclusion restriction or other additional information available. Finally, we summarise that, since the true MDM is unlikely to be known, several estimators which impose different assumptions on the MDM should be used together to examine the sensitivity of the outcomes of interest with respect to the assumptions made and the estimation procedures adopted

Topics: QA
OAI identifier: oai:wrap.warwick.ac.uk:1148

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Citations

  1. (2005). A Comparison of Semiparametric Estimators for the Ordered Response Model", doi
  2. (2003). A Pseudoscpre Estimator for Regression Problems With Two-Phase Sampling", doi
  3. (1999). Adjusting for non-ignorable drop-out using serniparametric non-response models",
  4. (1997). Analysis of incomplete Multivariate Data, doi
  5. (2003). Analysis of Multivariate Missing Data with Nonignorable Nonresponse", doi
  6. (1995). Analysis of Serniparametirc Regression models for Repeated Outcomes in the Presence of Missing Data", doi
  7. (1989). Anatomy of the Selection Pronlem",
  8. (2001). Combining Panel Data Sets with Attrition and Refreshment Samples", doi
  9. (2003). Discrete Choice Nonresponse", CENfNMP working paper No.
  10. Distribution Free Estimation of Heteroskedastic Binary Response Models Using Probit/Logit Criterion Functions", doi
  11. (2002). Econometric Analysis of Cross Section and Panel Data. doi
  12. (1998). Estimating Models with Sample Selection Bias: A Surevey", doi
  13. (1985). Estimation of Response Probabilities from Augmented Retrospective Observations", doi
  14. (2003). Estimation of the Distribution of Hourly Pay from Household Survey Data", CEMMAP working paper No.
  15. (2003). GMM and Empirical Likelihood with Incomplete Data",
  16. (2003). Inverse Probability Weighted Estimation for General Missing Data Problems", doi
  17. (1994). Large Sample Estimation and Hypothesis Testing", doi
  18. (1981). Maximum Likelihood Estimation for Choice-Based Samples", doi
  19. (2001). Measuring Low Pay using the New Earnings Survey and the Labour Force Survey", Labour Market Trends,
  20. (1999). Multiple Imputation of Missing Blood Pressure Covariates in Survival Analysis", doi
  21. (2005). Multiple Imputation of Missing Values: Update of Ice",
  22. (2005). Multiple Imputation of Missing Values: Update",
  23. (2004). Multiple Imputation of Missing Values",
  24. (1983). On Least Squares Estimation when the Dependent Variable is Grouped", The Review ofEconomic Studies, doi
  25. (2003). Partial Identification ofProbability Distributions.
  26. (2002). Reducing Bias using Targeted Refreshment Sampling and Matched Imputation",
  27. (2006). Reflections on Fifteen Years of Change in using the Labour Force Survey",
  28. (2000). Regression Analysis under Non-Standard Situations: A Pairwise Pseudolikelihood Approach", doi
  29. (1979). Sample Selection Bias as a Specification Error", doi
  30. (1987). Semi-Nonparametric Maximum Likelihood Estimation", doi
  31. (2006). Semiparametric Theory and Missing Data. doi
  32. (1995). Sernipararnetric Efficiency in Multivariate Regression Models with Missing Data", doi
  33. (2002). Statistical Analysis with Missing Data, 2nd edition. doi
  34. (1976). The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for such Models", Annals ofEconomic and Social Measurement,
  35. (2002). The Measurement of Low Pay in the UK Labour Force Survey", Oxford Bulletin ofEconomics and Statistics, doi
  36. (1994). The Selection Problem. ", doi
  37. (2003). Unified Methods for Censored Longitudinal Data and Causality. doi
  38. (2007). Weighting the Social Surveys ESDS Goverment". Available: http: //www.

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