Search Results - subject:"missing observations"

Interpolation and Prewar-Postwar Output Volatility and Shock-Persistence Debate: A Closer Look and New Results

Dezhbakhsh, Hashem; Levy, Daniel - 2026

It is well established that the U.S. prewar output was more volatile and less shock persistent than the postwar output. This is often attributed to the data interpolation employed to construct the prewar series. Our analytical results, however, indicate that commonly used linear interpolation...

Persistent link: https://www.econbiz.de/10015596091

An optimal imputation algorithm for reducing bias and errors in missing data handling for AI models

Sebastian, Anu Maria; Peter, David; Sebastian, Rinu Ann - 2025

Data is an essential fuel for artificial intelligence (AI) to power the underlying machine learning (ML) algorithms. Missing data is common in most real-world datasets due to measurement errors, non-responses, and human errors during the data collection, which can ultimately lead to reduced...

Persistent link: https://www.econbiz.de/10015506770

A nonparametric conditional copula-based imputation method

Di Lascio, F. Marta L.; Gatto, Aurora - 2025

Persistent link: https://www.econbiz.de/10015437084

Statistical imputation and validation of consumption microdata for EUROMOD

Dreoni, Ilda; Serruys, Hannes; Manso, Luis; … - 2025

Consumption taxes are a crucial revenue source for EU Member States, yet they also potentially have non-negligible impact on income distribution. The EU's tax-benefit microsimulation model, EUROMOD, has recently been extended to simulate consumption taxes (CT) across all 27 EU countries allowing...

Persistent link: https://www.econbiz.de/10015427352

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

Effective and efficient handling of missing data in supervised machine learning

Popoola, Peter Ayokunle; Tapamo, Jules-Raymond; … - In: Data science and management : DSM 8 (2025) 3, pp. 361-373

The prevailing consensus in statistical literature is that multiple imputation is generally the most suitable method for addressing missing data in statistical analyses, whereas a complete case analysis is deemed appropriate only when the rate of missingness is negligible or when the missingness...

Persistent link: https://www.econbiz.de/10015550343

Missing value imputation in environmental, social, and governance data : an impact on emissions scores

Downing, Nicholas Joseph - In: Finance research letters 85 (2025) 1, pp. 1-12

Persistent link: https://www.econbiz.de/10015557225

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

Interpolation and prewar-postwar output volatility and shock-persistence debate : a closer look and new results

Dezhbakhsh, Hashem; Levy, Daniel C. - 2026

Persistent link: https://www.econbiz.de/10015633699

Bootstrapping trending timevarying coefficient panel models with missing observations

Lin, Yicong; Sluis, Bernhard van der; 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/10014442008