Banner, Katharine M. and Higgs, Megan D. 2017. Considerations for assessing model averaging of regression coefficients. Ecological Applications, Vol. 27, Issue. 1, p. 78.
Kappe, Roland 2016. The Effect of the Religious Environment on Teenage Birth Rates in the United States. Sexuality Research and Social Policy, Vol. 13, Issue. 3, p. 241.
Olson, Roman Fan, Yanan and Evans, Jason P. 2016. A simple method for Bayesian model averaging of regional climate model projections: Application to southeast Australian temperatures. Geophysical Research Letters, Vol. 43, Issue. 14, p. 7661.
Rainey, Carlisle 2016. Does district magnitude matter? The case of Taiwan. Electoral Studies, Vol. 41, p. 202.
Zhukov, Yuri M. 2016. Trading hard hats for combat helmets: The economics of rebellion in eastern Ukraine. Journal of Comparative Economics, Vol. 44, Issue. 1, p. 1.
Aizenman, Joshua Cheung, Yin-Wong and Ito, Hiro 2015. International reserves before and after the global crisis: Is there no end to hoarding?. Journal of International Money and Finance, Vol. 52, p. 102.
Cranmer, Skyler J. Rice, Douglas R. and Siverson, Randolph M. 2015. What To Do About Atheoretic Lags. Political Science Research and Methods, p. 1.
Hassan, Saima Khosravi, Abbas and Jaafar, Jafreezal 2015. Examining performance of aggregation algorithms for neural network-based electricity demand forecasting. International Journal of Electrical Power & Energy Systems, Vol. 64, p. 1098.
Lauderdale, Benjamin E. and Linzer, Drew 2015. Under-performing, over-performing, or just performing? The limitations of fundamentals-based presidential election forecasting. International Journal of Forecasting, Vol. 31, Issue. 3, p. 965.
Montgomery, Jacob M. Hollenbach, Florian M. and Ward, Michael D. 2015. Calibrating ensemble forecasting models with sparse data in the social sciences. International Journal of Forecasting, Vol. 31, Issue. 3, p. 930.
Morozova, Olga Levina, Olga Uusküla, Anneli and Heimer, Robert 2015. Comparison of subset selection methods in linear regression in the context of health-related quality of life and substance abuse in Russia. BMC Medical Research Methodology, Vol. 15, Issue. 1,
Nyhan, Brendan 2015. Increasing the Credibility of Political Science Research: A Proposal for Journal Reforms. PS: Political Science & Politics, Vol. 48, Issue. S1, p. 78.
Sinha, Ankur Malo, Pekka and Kuosmanen, Timo 2015. A Multiobjective Exploratory Procedure for Regression Model Selection. Journal of Computational and Graphical Statistics, Vol. 24, Issue. 1, p. 154.
Yom, Sean 2015. From Methodology to Practice. Comparative Political Studies, Vol. 48, Issue. 5, p. 616.
Böhmelt, Tobias and Bove, Vincenzo 2014. Forecasting military expenditure. Research & Politics, Vol. 1, Issue. 1, p. 205316801453590.
Debray, Thomas P.A. Koffijberg, Hendrik Nieboer, Daan Vergouwe, Yvonne Steyerberg, Ewout W. and Moons, Karel G.M. 2014. Meta-analysis and aggregation of multiple published prediction models. Statistics in Medicine, Vol. 33, Issue. 14, p. 2341.
Horvath, Roman Rusnak, Marek Smidkova, Katerina and Zapal, Jan 2014. The dissent voting behaviour of central bankers: what do we really know?. Applied Economics, Vol. 46, Issue. 4, p. 450.
Pedraza, Francisco I. 2014. The Two-Way Street of Acculturation, Discrimination, and Latino Immigration Restrictionism. Political Research Quarterly, Vol. 67, Issue. 4, p. 889.
Pepinsky, Thomas B. 2014. The Politics of Capital Flight in the Global Economic Crisis. Economics & Politics, Vol. 26, Issue. 3, p. 431.
Schrodt, Philip A 2014. Seven deadly sins of contemporary quantitative political analysis. Journal of Peace Research, Vol. 51, Issue. 2, p. 287.
Political science researchers typically conduct an idiosyncratic search of possible model configurations and then present a single specification to readers. This approach systematically understates the uncertainty of our results, generates fragile model specifications, and leads to the estimation of bloated models with too many control variables. Bayesian model averaging (BMA) offers a systematic method for analyzing specification uncertainty and checking the robustness of one's results to alternative model specifications, but it has not come into wide usage within the discipline. In this paper, we introduce important recent developments in BMA and show how they enable a different approach to using the technique in applied social science research. We illustrate the methodology by reanalyzing data from three recent studies using BMA software we have modified to respect statistical conventions within political science.
Authors' note: A poster based on an earlier version of this paper was presented at the Society for Political Methodology Summer Conference, State College, PA, July 18–21, 2007. We thank James Adams, Benjamin G. Bishin, David W. Brady, Brandice Canes-Wrone, John F. Cogan, Jay K. Dow, James D. Fearon, and David D. Laitin for sharing their data and providing assistance with our replications of their work. We also thank John H. Aldrich, Michael C. Brady, Merlise Clyde, Josh Cutler, Scott de Marchi, Andrew Gelman, Daniel J. Lee, Efrén O. Pérez, Jill Rickershauser, David Sparks, Michael W. Tofias, T. Camber Warren, the editors, and two anonymous reviewers for helpful comments. All remaining errors are, of course, our own. Replication materials are available on the Political Analysis Web site.
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