What to do about missing values in time series cross- section data

Honaker, James; King, Gary

doi:10.1111/j.1540-5907.2010.00447.x

Cited by 966Statistical Methods and Bayesian InferenceSpatial and Panel Data AnalysisPolitical Science Research and Education

What to do about missing values in time series cross- section data

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Honaker, James, King, Gary·2010·American Journal of Political Science

DOI 10.1111/j.1540-5907.2010.00447.x

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Abstract

Applications of modern methods for analyzing data with missing values, based primarily on multiple imputation, have in the last half‐decade become common in American politics and political behavior. Scholars in this subset of political science have thus increasingly avoided the biases and inefficiencies caused by ad hoc methods like listwise deletion and best guess imputation. However, researchers in much of comparative politics and international relations, and others with similar data, have been unable to do the same because the best available imputation methods work poorly with the time‐series cross‐section data structures common in these fields. We attempt to rectify this situation with three related developments. First, we build a multiple imputation model that allows smooth time trends, shifts across cross‐sectional units, and correlations over time and space, resulting in far more accurate imputations. Second, we enable analysts to incorporate knowledge from area studies experts via priors on individual missing cell values, rather than on difficult‐to‐interpret model parameters. Third, because these tasks could not be accomplished within existing imputation algorithms, in that they cannot handle as many variables as needed even in the simpler cross‐sectional data for which they were designed, we also develop a new algorithm that substantially expands the range of computationally feasible data types and sizes for which multiple imputation can be used. These developments also make it possible to implement the methods introduced here in freely available open source software that is considerably more reliable than existing algorithms.

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The Amelia II R package was selected due to the statistical validity of its approach in handling time-series, cross-sectional data.
“The Amelia II R package was selected due to the statistical validity of its approach in handling time-series, cross-sectional data (Honaker & King, 2010;Honaker et al., 2011).”
From A single-case experimental evaluation of a new group-based intervention to enhance adjustment to life with acquired brain injury: VaLiANT (valued living after neurological trauma) (2022) · cites this paper as Honaker & King, 2010
Multiple imputation was performed to handle missing values.
“Given that the bootstrapping method does not allow the sample to include missing values, multiple imputation was performed (Honaker & King, 2010).”
From The reciprocal and indirect relationships between passive Facebook use, comparison on Facebook, and adolescents' body dissatisfaction (2017) · cites this paper as Honaker & King, 2010

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Authors: Honaker, J., & King, G.
Affiliations: Pennsylvania State University (US)
Harvard University (US)
ORCID: King, Gary
Venue: American Journal of Political Science
Publisher: Wiley
Published: 2010-04
Volume: 54
Issue: 2
Pages: 561-581
ISSN: 0092-5853 (ISSN-L)
1540-5907
DOI: 10.1111/j.1540-5907.2010.00447.x
OpenAlex: W2125291718
Cited by: 966
References: 71
Citations by year: 2026: 44 · 2025: 41 · 2024: 27 · 2023: 41 · 2022: 45 · 2021: 60 · 2020: 64 · 2019: 77 · 2018: 77 · 2017: 54 · 2016: 55 · 2015: 90 · 2014: 83 · 2013: 74 · 2012: 68
FWCI: 48.73
Type: journal article
Language: en
Open access: green
nrs.harvard.edu
Licence: cc-by
onlinelibrary.wiley.com/termsAndConditions#vor (vor, 8 d)
Locations: American Journal of Political Science (publishedVersion)
Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
Social Sciences › Political Science and International Relations › Political Science Research and Education
Keywords: missing data, multiple imputation, time-series cross-section data, imputation algorithm, temporal trends, spatial correlation, listwise deletion, open source software

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What to do about missing values in time series cross- section data

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