1 to 8 of 8 Results
Beck, Nathaniel; King, Gary; Zeng, Langche, 2007, "Replication data for: Improving Quantitative Studies of International Conflict: A Conjecture", https://doi.org/10.7910/DVN/ZGDYNQ, Harvard Dataverse, V4, UNF:3:rYRDzT8dCJ/BR7V9u8fObA== [fileUNF]
We address a well-known but infrequently discussed problem in the quantitative study of international conflict: Despite immense data collections, prestigious journals, and sophisticated analyses, empirical findings in the literature on international conflict are often unsatisfying. Many statistical results change from article to article and specifi... |
King, Gary; Zeng, Langche, 2007, "Replication data for: Explaining Rare Events in International Relations", https://doi.org/10.7910/DVN/RNSU7V, Harvard Dataverse, V4, UNF:3:vyct3c8fMCdWOdp03NUhaA== [fileUNF]
Some of the most important phenomena in international conflict are coded s "rare events data," binary dependent variables with dozens to thousands of times fewer events, such as wars, coups, etc., than "nonevents". Unfortunately, rare events data are difficult to explain and predict, a problem that seems to have at least two sources. First, and mos... |
King, Gary; Zeng, Langche, 2007, "Replication data for: The Dangers of Extreme Counterfactuals", https://doi.org/10.7910/DVN/MJ1YCL, Harvard Dataverse, V5, UNF:3:ytKKNjK+yR8Pq3H0RcV6eg== [fileUNF]
We address the problem that occurs when inferences about counterfactuals -- predictions, "what if" questions, and causal effects -- are attempted far from the available data. The danger of these extreme counterfactuals is that substantive conclusions drawn from statistical models that fit the data well turn out to be based largely on speculation hi... |
Beck, Nathaniel; King, Gary; Zeng, Langche, 2007, "Replication data for: Theory and Evidence in International Conflict: A Response to de Marchi, Gelpi, and Grynaviski", https://doi.org/10.7910/DVN/S7JLEL, Harvard Dataverse, V4, UNF:3:N0bEAswAlPPVXCxPOZYyqw== [fileUNF]
We thank Scott de Marchi, Christopher Gelpi, and Jeffrey Grynaviski (2003; hereinafter dGG) for their careful attention to our work (Beck, King, and Zeng, 2000; hereinafter BKZ) and for raising some important methodological issues that we agree deserve readers' attention. We are pleased that dGG's analyses are consistent with the theoretical conjec... |
King, Gary; Zeng, Langche, 2007, "Replication data for: When Can History be Our Guide? The Pitfalls of Counterfactual Inference", https://doi.org/10.7910/DVN/EK886K, Harvard Dataverse, V4, UNF:3:DaYlT6QSX9r0D50ye+tXpA== [fileUNF]
Inferences about counterfactuals are essential for prediction, answering "what if" questions, and estimating causal effects. However, when the counterfactuals posed are too far from the data at hand, conclusions drawn from well-specified statistical analyses become based on speculation and convenient but indefensible model assumptions rather than e... |
King, Gary; Zeng, Langche, 2007, "Replication data for: Detecting Model Dependence in Statistical Inference: A Response", https://doi.org/10.7910/DVN/O2NXPE, Harvard Dataverse, V5, UNF:3:K4/CgnMYDMV6izc5RVOZTA== [fileUNF]
Inferences about counterfactuals are essential for prediction, answering "what if" questions, and estimating causal effects. However, when the counterfactuals posed are too far from the data at hand, conclusions drawn from well-specified statistical analyses become based on speculation and convenient but indefensible model assumptions rather than e... |
King, Gary; Zeng, Langche, 2009, "Replication data for: Empirical vs. Theoretical Claims about Extreme Counterfactuals: A Response", https://doi.org/10.7910/DVN/VL7QMO, Harvard Dataverse, V5
A response to Sambanis and Michaelides, "A Comment on Diagnostic Tools for Counterfactual Inference", which was a comment on: Gary King and Langche Zeng. 2006. " The Dangers of Extreme Counterfactuals," Political Analysis, 14, 2, Pp. 131-159. In response to the data-based measures of model dependence proposed in King and Zeng (2006), Sambanis and M... |
King, Gary; Zeng, Langche, 2007, "Replication data for: Improving Forecasts of State Failure", https://doi.org/10.7910/DVN/BS4236, Harvard Dataverse, V4, UNF:3:CEsbEgPxbxExfYuh2NWwWQ== [fileUNF]
We offer the first independent scholarly evaluation of the claims, forecasts, and causal inferences of the State Failure Task Force and their efforts to forecast when states will fail. State failure refers to the collapse of the authority of the central government to impose order, as in civil wars, revolutionary wars, genocides, politicides, and ad... |
