计量经济分析 = Econometric analysis

We investigate conditions sufficient for identification of average treatment effects using instrumental variables. First we show that the existence of valid instruments is not sufficient to identify any meaningful average treatment effect. We then establish that the combination of an instrument and a condition on the relation between the instrument and the participation status is sufficient for identification of a local average treatment effect for those who can be induced to change their participation status by changing the value of the instrument. Finally we derive the probability limit of the standard IV estimator under these conditions. It is seen to be a weighted average of local average treatment effects.

Evolution and Rationality Some Recent Game-Theoretic Results. Identification and Estimation of Local Average Treatment Effects

Series Editor's Preface. Introduction by John Breuilly. Acknowledgements. 1. Definitions. State and nation. The nation. 2. Culture in Agrarian Society. Power and culture in the agro-literature society. The varieties of agrarian rulers. 3. Industrial Society. The society of perpetual growth. Social genetics. The age of universal high culture. 4. The Transition to an Age of Nationalism. A note on the weakness of nationalism. Wild and garden culture. 5. What is a Nation. The course of true nationalism never did run smooth. 6. Social Entropy and Equality in Industrial Society. Obstacles to entropy. Fissures and barriers. A diversity of focus. 7. A Typology of Nationalisms. The varieties of nationalist experience. Diaspora nationalism. 8. The Future of Nationalism. Industrial culture - one or many?. 9. Nationalism and Ideology. Who is for Nuremberg?. One nation, one state. 10. Conclusion. What is not being said. Summary. Select bibliography. Bilbliography of Ernest Gellner's writing: Ian Jarvie. Index

Nations and Nationalism

The course is focused on historical texts, most of them philosophical. Context for understanding the texts and the course of democratic development will be provided in lecture and discussions, and by some background readings (Dunn). We begin with the remarkable Athenian democracy, and its frequent enemy the Spartan oligarchy. In Athens legislation was passed directly by an assembly of all citizens, and executive officials were selected by lot rather than by competitive election. Athenian oligarchs such as Plato more admired Sparta, and their disdain for the democracy became the judgment of the ages, until well after the modern democratic revolutions. Marsilius of Padua in the early Middle Ages argued for popular sovereignty. The Italian citystates of the Middle Ages did without kings, and looked back to Rome and Greece for republican models. During the English Civil War republicans debated whether the few or the many should be full citizens of the regime. The English, French, and American revolutions struggled with justifying and establishing a representative democracy suitable for a large state, and relied on election rather than lot to select officials. The English established a constitutional monarchy, admired in Europe, and adapted by the Americans in their republican constitution. The American Revolution helped inspire the French, and the French inspired republican and democratic revolution throughout Europe during the 19 century.

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Democracy and Its Critics

Therefore, the seeker after the truth is not one who studies the writings of the ancients and, following his natural disposition, puts his trust in them, but rather the one who suspects his faith in them and questions what he gathers from them, the one who submits to argument and demonstration, and not to the sayings of a human being whose nature is fraught with all kinds of imperfection and de‹ciency. Thus the duty of the man who investigates the writings of scientists, if learning the truth is his goal, is to make himself the enemy of all that he reads, and, applying his mind to the core and margins of its content, attack it from every side. He should also suspect himself as he performs his critical examination of it, so that he may avoid falling into either prejudice or leniency. (Ibn al-Haytham)1

국제정치이론 = Theory of international politics

Following older debates in international relations literature concerning the relative importance of domestic versus systemic factors, newer debates emphasize interdependence among states and the complex interactions between systemic and domestic factors. As globalization and democratization advance, theories and empirical models of international politics have become more complicated. We present a systematic theoretical categorization of relationships between domestic and systemic variables. We use this categorization so that scholars can match their theory to the appropriate empirical model and assess the degree to which systemic factors affect their arguments. We also present two advances at the frontier of these empirical models. In one, we combine hierarchical models of moderating relationships with spatial models of interdependence among units within a system. In the other, we provide a model for analyzing spatial interdependence that varies over time. This enables us to examine how the level of interdependence among units has evolved. We illustrate our categorization and new models by revisiting the recent international political economy (IPE) debate over the relationship between trade policy and regime type in developing countries.

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International Systems and Domestic Politics: Linking Complex Interactions with Empirical Models in International Relations

This paper proposes a Bayesian generalized linear multilevel model with a pth-order autoregressive error process to analyze unbalanced binary time-series cross-sectional (TSCS) data. The model specification is motivated by the generic TSCS data structure and is intended to handle the associated inefficiency and endogeneity problems. It accommodates heterogeneity across units and between time periods in the form of random intercepts and random-effect coefficients. At the same time, its pth-order autoregressive error process, employed either by itself or in concert with other dynamic methods, adequately corrects serial correlation and improves statistical inference and forecasting. With a stationarity restriction on the error process, the model can also be used as a residual-based cointegration test on discrete TSCS data. This is especially valuable because cointegration testing on discrete TSCS data is methodologically challenging and rarely conducted in practice. To handle the estimation difficulties, I developed an efficient Markov chain Monte Carlo (MCMC) algorithm by orthogonalizing the error term with the Cholesky decomposition and adding an auxiliary variable. The parameter expansion method, that is, partial group move-multigrid Monte Carlo updating (PGM-MGMC), is employed to further improve MCMC mixing and speed up convergence. The paper also provides a computational scheme to approximate the Bayes's factor for the purposes of serial correlation diagnostics, lag order determination, and variable selection. Simulated and empirical examples are used to assess the model and techniques.

Modeling Heterogeneity and Serial Correlation in Binary Time-Series Cross-sectional Data: A Bayesian Multilevel Model with AR(p) Errors

This paper proposes a Bayesian alternative to the synthetic control method for comparative case studies with a single or multiple treated units. We adopt a Bayesian posterior predictive approach to Rubin's causal model, which allows researchers to make inferences about both individual and average treatment effects on treated observations based on the empirical posterior distributions of their counterfactuals. The prediction model we develop is a dynamic multilevel model with a latent factor term to correct biases induced by unit-specific time trends. It also considers heterogeneous and dynamic relationships between covariates and the outcome, thus improving precision of the causal estimates. To reduce model dependency, we adopt a Bayesian shrinkage method for model searching and factor selection. Monte Carlo exercises demonstrate that our method produces more precise causal estimates than existing approaches and achieves correct frequentist coverage rates even when sample sizes are small and rich heterogeneities are present in the data. We illustrate the method with two empirical examples from political economy.

A Bayesian Alternative to Synthetic Control for Comparative Case Studies

Jurisprudential regime theory is a legal explanation of decision-making on the U.S. Supreme Court that asserts that a key precedent in an area of law fundamentally restructures the relationship between case characteristics and the outcomes of future cases. In this article, we offer a multivariate multiple change-point probit model that can be used to endogenously test for the existence of jurisprudential regimes. Unlike the previously employed methods, our model does so by estimating the locations of many possible changepoints along with structural parameters. We estimate the model using Markov chain Monte Carlo methods, and use Bayesian model comparison to determine the number of change-points. Our findings are consistent with jurisprudential regimes in the Establishment Clause and administrative law contexts. We find little support for hypothesized regimes in the areas of free speech and search-and-seizure. The Bayesian multivariate change-point model we propose has broad potential applications to studying structural breaks in either regular or irregular time-series data about political institutions or processes.

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Xun Pang

Papers

International Systems and Domestic Politics: Linking Complex Interactions with Empirical Models in International Relations

Modeling Heterogeneity and Serial Correlation in Binary Time-Series Cross-sectional Data: A Bayesian Multilevel Model with AR(p) Errors

A Bayesian Alternative to Synthetic Control for Comparative Case Studies

Endogenous Jurisprudential Regimes

A Bayesian Alternative to Synthetic Control for Comparative Case Studies