Abstract
PurposeThis study proposes a Bayesian approach to analyze structural breaks and examines whether structural changes have occurred, at the onset of civil war, with respect to economic development and population during the period from 1945 to 1999.Design/methodology/approachIn the Bayesian logit regression changepoint model, parameters of covariates are allowed to shift individually, regime transitions can move back and forth, and the model is applicable to cross-sectional, time-series data.FindingsContrary to popular belief that the causal process of civil war changed with the end of the Cold War, the empirical analysis shows that the regression relationships between civil war and economic development, as well as between civil war and population, remain quite stable during the study period.Originality/valueThis is the first to develop a Bayesian logit regression changepoint model and to apply it to studies of economic development and civil war.
Highlights
When suspecting unusual changes in a political or economic phenomenon, researchers are eager to employ changepoint analysis that may enable them to identify such structural changes as well as their causes
Upon fitting a Bayesian changepoint model to Fearon and Laitin’s civil war data for 156 countries during the period from 1945 to 1999, this study finds that there was no structural break in the relationship between civil war and its economic determinants
Since changepoint analysis is instrumental in determining whether important changes have taken place in the mechanism of politico-economic phenomena, several existing studies across disciplines have striven to develop various types of structural break models
Summary
When suspecting unusual changes in a political or economic phenomenon, researchers are eager to employ changepoint analysis that may enable them to identify such structural changes as well as their causes. Existing changepoint models are designed to mainly deal with a series of time ordered data within a country (e.g., Quandt, 1960; Brown et al, 1975; Andrews, 1993; Andrews et al, 1996; Bai and Perron, 1998; Chib, 1998; Spirling, 2007). Most existing changepoint models are not suitable for more than one changepoint (e.g., Quandt, 1960; Andrews, 1993; Spirling, 2007), incorporating covariates (e.g., Chib, 1998), or modification of programming codes for a new research project. The rationale for the use of logit is that it is one of the most
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