Search Results - subject:"missing observations"

Observation-driven hierarchical density models for missing data imputation

Khanna, Yonas; Lucas, André - 2025

We propose an observation-driven dynamic common factor model for missing value imputation in high-dimensional panel data. The model exploits both serial and cross-sectional information in the data and can easily cope with time-variation in conditional means and variances, as well as with either...

Persistent link: https://www.econbiz.de/10015373862

Marriage penalties and bonuses by race and ethnicity : an application of race and ethnicity imputation

Costello, Rachel; DeFilippes, Portia; Fisher, Robin; … - 2024

Persistent link: https://www.econbiz.de/10014468791

Bootstrapping trending timevarying coefficient panel models with missing observations

Lin, Yicong; van der Sluis, Bernhard; Friedrich, Marina - 2023

-sectional and serial dependence, as well as heteroskedasticity. Our models also allow for missing observations in the dependent … variable. We introduce a local linear dummy variable estimator capable of handling missing observations and derive its … intervals and bands. The AWB accommodates missing observations and automatically replicates all the nuisance parameters …

Persistent link: https://www.econbiz.de/10014469349

Bootstrapping trending timevarying coefficient panel models with missing observations

Lin, Yicong; Sluis, Bernhard van der; Friedrich, Marina - 2023

Persistent link: https://www.econbiz.de/10014442008

The handling of missing data in trial-based economic evaluations : should data be multiply imputed prior to longitudinal linear mixed-model analyses?

Ben, Ângela Jornada; Dongen, Johanna M. van; El Alili, … - In: The European journal of health economics 24 (2023) 6, pp. 951-965

Persistent link: https://www.econbiz.de/10014328152

Missing Data in Asset Pricing Panels

Freyberger, Joachim; Höppner, Björn; Neuhierl, Andreas; … - 2023

Missing data for return predictors is a common problem in cross sectional asset pricing. Most papers do not explicitly discuss how they deal with missing data but conventional treatments focus on the subset of firms with no missing data for any predictor or impute the unconditional mean. Both...

Persistent link: https://www.econbiz.de/10014254015

Estimating poverty for refugees in data-scarce contexts : an application of cross-survey imputation

Dang, Hai-Anh; Verme, Paolo - In: Journal of population economics : international … 36 (2023) 2, pp. 653-679

Persistent link: https://www.econbiz.de/10014226423

Imputing Missing Values in the Us Census Bureau's County Business Patterns

Eckert, Fabian; Fort, Teresa; Schott, Peter K.; Yang, … - 2022

The County Business Patterns data published by the US Census Bureau track employment by county and industry from 1946 to the present. Two features of the data limit their usefulness to researchers: (1) employment for the majority of county-industry cells is suppressed to protect confidentiality,...

Persistent link: https://www.econbiz.de/10013324539

On Missing Data Imputation for IRB Models

Liu, Yang - 2022

Often, model development starts with missing data treatment. For regulatory internal rating based (IRB) models, missing data raise data quality concerns around system and process whereas the randomness nature of missing is sometimes overlooked, resulting inappropriate choice of imputation...

Persistent link: https://www.econbiz.de/10013305872

Jackknife winsorized variance estimator under imputed data

Sohil, Fariha; Sohail, Muhammad Umair; Shabbir, Javid; … - In: Statistics in transition : an international journal of … 23 (2022) 2, pp. 17-32

In the present study, we consider the problem of missing and extreme values for the estimation of population variance. The presence of extreme values either in the study variable, or the auxiliary variable, or in both of them, can adversely affect the performance of the estimation procedure. We...

Persistent link: https://www.econbiz.de/10013419424