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Conventional statistical methods for panel data are based on the assumption that unobserved heterogeneity is time constant. Despite the central importance of this assumption for panel data methods, few studies have developed statistical methods for testing this assumption and modeling time‐varying unobserved heterogeneity. In this article, I introduce a formal test to check the assumption of time‐constant unobserved heterogeneity using Bayesian model comparison. Then, I present two panel data methods that account for time‐varying unobserved heterogeneity in the context of the random‐effects model and the fixed‐effects model, respectively. I illustrate the utility of the introduced methods using both simulated data and examples drawn from two important debates in the political economy literature: (1) the identification of shifting relationships between income inequality and economic development in capitalist countries and (2) the effects of the GATT/WTO on bilateral trade volumes.

In this paper, I introduce changepoint models for binary and ordered time series data based on Chib's hidden Markov model. The extension of the changepoint model to a binary probit model is straightforward in a Bayesian setting. However, detecting parameter breaks from ordered regression models is difficult because ordered time series data often have clustering along the break points. To address this issue, I propose an estimation method that uses the linear regression likelihood function for the sampling of hidden states of the ordinal probit changepoint model. The marginal likelihood method is used to detect the number of hidden regimes. I evaluate the performance of the introduced methods using simulated data and apply the ordinal probit changepoint model to the study of Eichengreen, Watson, and Grossman on violations of the "rules of the game" of the gold standard by the Bank of England during the interwar period.

Has there been a structural change in the way U.S. presidents use force abroad since the nineteenth century? In this article, I investigate historical changes in the use of force by U.S. presidents using Bayesian changepoint analysis. In doing so, I present an integrated Bayesian approach for analyzing changepoint problems in a Poisson regression model. To find the nature of the breaks, I estimate parameters of the Poisson regression changepoint model using Chib's (1998) hidden Markov model algorithm and Fruhwirth-Schnatter and Wagner's (2006) data augmentation method. Then, I utilize transdimensional Markov chain Monte Carlo methods to detect the number of breaks. Analyzing yearly use of force data from 1890 to 1995, I find that, controlling for the effects of the Great Depression and the two world wars, the relationship between domestic conditions and the frequency of the use of force abroad fundamentally shifted in the 1940s. Adapted from the source document.

What explains the foreign policy gap between elites and the general public on nuclear proliferation? We investigate specific contexts in which experts' nonproliferation information changes public attitudes toward nuclear weapons development using a novel attitude change experiment. By randomly assigning seven categories of nonproliferation information to pro-armament survey participants, we examine how different types of nonproliferation information affect pro-armament respondents' opinions and behavioral choices. The results of our experiment demonstrate the enlightening effect of economic sanctions information. After learning about the economic costs and consequences of nuclear weapons development, pro-armament respondents substantially changed their opinion as well as behaviors toward nuclear proliferation. In comparison to economic sanctions information, other types of nonproliferation information (e.g. conditional military punishment, normative sanctions, nuclear technology sanctions, elite or public opposition to proliferation) have limited effects on pro-armament subjects' attitude changes. These findings are the first to identify the relative explanatory powers of previous explanations for nuclear nonproliferation at the individual level.

AbstractResearchers of time series cross-sectional data regularly face the change-point problem, which requires them to discern between significant parametric shifts that can be deemed structural changes and minor parametric shifts that must be considered noise. In this paper, we develop a general Bayesian method for change-point detection in high-dimensional data and present its application in the context of the fixed-effect model. Our proposed method, hidden Markov Bayesian bridge model, jointly estimates high-dimensional regime-specific parameters and hidden regime transitions in a unified way. We apply our method to Alvarez, Garrett, and Lange's (1991, American Political Science Review 85, 539–556) study of the relationship between government partisanship and economic growth and Allee and Scalera's (2012, International Organization 66, 243–276) study of membership effects in international organizations. In both applications, we found that the proposed method successfully identify substantively meaningful temporal heterogeneity in parameters of regression models.

What explains the foreign policy gap between elites and the general public on nuclear proliferation? We investigate specific contexts in which experts' nonproliferation information changes public attitudes toward nuclear weapons development using a novel attitude change experiment. By randomly assigning seven categories of nonproliferation information to pro-armament survey participants, we examine how different types of nonproliferation information affect pro-armament respondents' opinions and behavioral choices. The results of our experiment demonstrate the enlightening effect of economic sanctions information. After learning about the economic costs and consequences of nuclear weapons development, pro-armament respondents substantially changed their opinion as well as behaviors toward nuclear proliferation. In comparison to economic sanctions information, other types of nonproliferation information (e.g. conditional military punishment, normative sanctions, nuclear technology sanctions, elite or public opposition to proliferation) have limited effects on pro-armament subjects' attitude changes. These findings are the first to identify the relative explanatory powers of previous explanations for nuclear nonproliferation at the individual level.

In this article, we study social mobility across multiple generations in premodern Korea. Using two extant oldest family records, jokbo, we construct a prospective genealogical microdata containing the entire records of public offices and reproduction over five generations of the two elite family lineages in premodern Korea. We argue that the confluence of an ambiguous stratification system with a limited number of high-ranking offices generated a trade-off for parents between the quantity and quality of positions attained by their offspring. The result of the trade-off was unequal distributions of mobility-related family resources to maximize the lineage's collective goal, rather than to maximize individual children's social ranks. Using a novel empirical strategy to consider the heterogeneous resource-allocation within elite families, we present empirical evidence on associations between parents' and grandparents' social ranks and quality of offices achieved by children of elite Korean families.

AbstractNetwork scholars commonly encounter multiple networks, each of which is possibly governed by distinct generation rules while sharing a node group structure. Although the stochastic blockmodeling—detecting such latent group structures with group-specific connection profiles—has been a major topic of recent research, the focus has been given to the assortative group discovery of a single network. Despite its universality, concepts, and techniques for simultaneous characterization of node traits of multilayer networks, constructed by stacking multiple networks into layers, have been limited. Here, we propose a Bayesian multilayer stochastic blockmodeling framework that uncovers layer-common node traits and factors associated with layer-specific network generating functions. Without assuming a priori layer-specific generation rules, our fully Bayesian treatment allows probabilistic inference of latent traits. We extend the approach to detect changes in block structures embedded in temporal layers of network time series. We demonstrate the method using synthetic multilayer networks with assortative, disassortative, core-periphery, and overlapping community structures. Finally, we apply the method to empirical social network datasets, and find that it detects significant latent traits and structural changepoints. In particular, we uncover endogenous historical regimes associated with distinct constellations of states in United States Senate roll call vote similarity patterns.

The argument that reputational concerns promote compliance is at the center of the literature of international cooperation. In this paper, we study how reputational sanctions affect compliance when domestic parties carry their own reputations in international negotiations. We showed that the prospect of international cooperation varies a lot depending on who sits at the negotiation table, how partisan preferences for compliance are different, and how much international audiences discriminate between different types of noncompliance. We illustrate implications of our model using episodes from the negotiations between the United States and North Korea over North Korea's nuclear weapons program.