ReLogit: Rare Events Logistic Regression

By Micahael Tomz, Gary King, Langche Zeng. 2003

370 citations recorded by the source corpus.

We study rare events data, binary dependent variables with dozens to thousands of times fewer ones (events, such as wars, vetoes, cases of political activism, or epidemiological infections) than zeros ("nonevents"). In many literatures, these variables have proven difficult to explain and predict, a problem that seems to have at least two sources. First, popular statistical procedures, such as logistic regression, can shar ply underestimate the probability of rare events. We recommend corrections that outperform existing methods and change the estimates of absolute and relative risks by as much as some estimated effects repor ted in the literature. Second, commonly used data collection strategies are grossly inefficient for rare events data. The fear of collecting data with too few events has led to data collections with huge numbers of obser vations but relatively few, and poorly measured, explanator y variables, such as in international conflict data with more than a quarter-million dyads, only a few of which are at war. As it turns out, more efficient sampling designs exist for making valid inferences, such as sampling all available events (e.g., wars) and a tiny fraction of nonevents (peace). This enables scholars to save as much as 99% of their (nonfixed) data collection costs or to collect much more meaningful explanator y variables. We provide methods that link these two results, enabling both types of corrections to work simultaneously, and software that implements the methods developed.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/7bf3a8dc6e3c9710261647df7e08a6f8bcbb7383

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Citations in the corpus

Which Problems to Solve? Online Knowledge Sharing and Attention Allocation in Organizations (2014, methodology): “…we clustered the standard errors on the provider (Hallen, 2008; Jensen, 2003), since each provider appears in one realized dyad and 10 nonrealized dyads (that is, the provider is constant across 11 observations).4 We used the ReLogit Stata procedure of Tomz (2003) to estimate the logit models.”
The Importance of Trust for Investment: Evidence from Venture Capital (2016, background): “[58] Tomz, Michael, Gary King, and Langche Zeng (2003) ’ReLogit: Rare Events Logistic Regression’, Journal of Statistical Software 8(2).”
Life Satisfaction and Political Participation: Evidence from the United States (2011, neutral): “CLARIFY: Software for interpreting and presenting statistical results.”
Her Support, His Support: Money, Masculinity, and Marital Infidelity (2015, neutral): “There is no precise cutoff for defining an event as rare, but the rare-events problem has to do with the number of cases (not simply the percentage of cases) on the rarer of the two outcomes (Tomz et al. 2003).”
Home Bias in Online Investments: An Empirical Study of an Online Crowdfunding Market (2016, positive): “We estimate the following model using a logistic model as well as a rare events logistic model (Tomz et al. 2003):”