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Missing data, imputation, and endogeneity

McDonough, Ian K.; Millimet, Daniel L. - 2016

when data on the endogenous covariate is missing for some observations. Many such imputation techniques have been proposed … decreasing in the degree of measurement accuracy, the most accurate imputation method is not necessarily the method that … minimizes the bias of 2SLS. Instead, we explore imputation methods designed to increase the first-stage strength of the …

Persistent link: https://www.econbiz.de/10011594466

Missing data, imputation, and endogeneity

McDonough, Ian K.; Millimet, Daniel L. - In: Journal of econometrics 199 (2017) 2, pp. 141-155

Persistent link: https://www.econbiz.de/10011897665

Educational Impacts and Rising Inequality in the Us

Millimet, Daniel L.; Slottje, Daniel J.; Yitzhaki, Shlomo; … - 2003

This study integrates two bodies of work to explore how changes in human capital have impacted the distribution of household permanent income. Introducing a human capital accumulation model, a framework is established - utilizing the special properties of the Gini coefficient - which maps...

Persistent link: https://www.econbiz.de/10014084869

Missing Data, Imputation, and Endogeneity

McDonough, Ian K.; Millimet, Daniel L. - 2016

Persistent link: https://www.econbiz.de/10011653130

Measuring economic mobility in India using noisy data : a partial identification approach

Li, Hao; Millimet, Daniel L.; Roychowdhury, Punarjit - 2019

We examine economic mobility in India while rigorously accounting for measurement error. Such an analysis is imperative to fully understand the welfare effects of the rise in inequality that has occurred in India over the past few decades. To proceed, we extend recently developed methods on the...

Persistent link: https://www.econbiz.de/10012057331

Accounting for skewed or one-sided measurement error in the dependent variable

Millimet, Daniel L.; Parmeter, Christopher F. - 2019

While classical measurement error in the dependent variable in a linear regression framework results only in a loss of precision, non-classical measurement error can lead to estimates which are biased and inference which lacks power. Here, we consider a particular type of non-classical...

Persistent link: https://www.econbiz.de/10012100859

Partial identification of economic mobility : with an application to the United States

Millimet, Daniel L.; Li, Hao; Roychowdhury, Punarjit - 2019

The economic mobility of individuals and households is of fundamental interest. While many measures of economic mobility exist, reliance on transition matrices remains pervasive due to simplicity and ease of interpretation. However, estimation of transition matrices is complicated by the...

Persistent link: https://www.econbiz.de/10011972716

Bounding the joint distribution of disability and employment with contaminated data

Liu, Ding; Millimet, Daniel L. - 2020

Understanding the relationship between disability and employment is critical and has long been the subject of study. However, estimating this relationship is difficult, particularly with survey data, since both disability and employment status are known to be misreported. Here, we use a partial...

Persistent link: https://www.econbiz.de/10012180343

Propensity score weighting with mismeasured covariates: an application to two financial literacy interventions

Dong, Hao; Millimet, Daniel L. - 2020

Estimation of the causal effect of a binary treatment on outcomes often requires conditioning on covariates to address selection on observed variables. This is not straightforward when one or more of the covariates are measured with error. Here, we present a new semi-parametric estimator that...

Persistent link: https://www.econbiz.de/10012316918

Measuring economic mobility in India using noisy data : a partial identification approach

Li, Hao; Millimet, Daniel L.; Roychowdhury, Punarjit - 2023

We examine economic mobility in India while accounting for misclassification to better understand the welfare e§ects of the rise in inequality. To proceed, we extend recently developed methods on the partial identification of transition matrices. Allowing for modest misclassification, we find...

Persistent link: https://www.econbiz.de/10013545844