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Monte Carlo simulations are commonly used to test the performance of estimators and models from rival methods, under a range of data-generating processes. This tool improves our understanding of the relative merits of rival methods in different contexts, such as varying sample sizes and violations of assumptions. When used, it is common to report the bias or the root mean squared error of the different methods. It is far less common to report the standard deviation, overconfidence, coverage probability, or power. Each of these six performance statistics provides important, and often differing, information regarding a method's performance. Here, we present a structured way to think about Monte Carlo performance statistics. In replications of three prominent papers, we demonstrate the utility of our approach and provide new substantive results about the performance of rival methods. [ABSTRACT FROM AUTHOR]

Conventional OLS fixed-effects and GLS random-effects estimators of dynamic models that control for individual-effects are known to be biased when applied to short panel data (T ≤ 10). GMM estimators are the most used alternative but are known to have drawbacks. Transformed-likelihood estimators are unused in political science. Of these, orthogonal reparameterization estimators are only tangentially referred to in any discipline. We introduce these estimators and test their performance, demonstrating that the unused orthogonal reparameterization estimator in particular performs very well and is an improvement on the commonly used GMM estimators. When T and/or N are small, it provides efficiency gains and overcomes the issues GMM estimators encounter in the estimation of long-run effects when the coefficient on the lagged dependent variable is close to one. [ABSTRACT FROM AUTHOR]

In majoritarian parliaments, the executive branch typically enjoys an informational advantage over the legislature. In theory, legislators can reduce this asymmetry with information from interest groups. In practice, the government is almost always better informed than the legislature. This article develops a model whereby a politician's access to outside information depends not just on her parliamentary power but on the diffusion of legislative agenda control among political parties—for example, during minority government. Using a new panel data set of 41,619 lobbying communications, it finds interest groups are more likely to communicate with government frontbenchers than with opposition or backbench members. This gap diminishes as agenda control diffuses to opposition parties. It also finds evidence of partisan clustering in lobbying networks during majority government. Strong legislative parties weaken accountability by restricting access to outside information, but this is conditional on the governing party's control over the agenda. [ABSTRACT FROM AUTHOR]

Policy entrepreneurship is often used to explain agenda setting through reference to the behavior of individual 'change agents.' But there are still gaps in our understanding of what motivates entrepreneurs. Rational choice theory emphasizes the importance of material and nonmaterial incentives; however, it remains unclear what role institutions play. This article aims to empirically examine the relationship between incentives and institutional encouragement of innovation. Using a case study of the federal government of Canada, I find evidence that incentives matter. Government agencies with many incentives are more likely to encourage innovation than agencies with few incentives. These results underscore the importance of institutions in determining the motivations of policy entrepreneurs, and suggest future research should focus on the interplay among individual, political, and institutional factors. [ABSTRACT FROM AUTHOR]