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This paper develops new fundamental models for forecasting presidential, senatorial, and gubernatorial elections at the state level using fundamental data. Despite the fact that our models can be used to make forecasts of elections earlier than existing models and they do not use data from polls on voting intentions, our models have lower out-of-sample forecasting errors than existing models. Our models also provide early and accurate probabilities of victory. We obtain this accuracy by constructing new methods of incorporating various economic and political indicators into forecasting models. We also obtain new results about the relative importance of approval ratings, economic indicators, and midterm effects in the different types of races, how economic data can be most meaningfully incorporated in forecasting models, the effects of different types of candidate experience on election outcomes, and that second quarter data is as predictive of election outcomes as third quarter data. [ABSTRACT FROM AUTHOR]

Sample selection models, variants of which are the Heckman and Heckit models, are increasingly used by political scientists to accommodate data in which censoring of the dependent variable raises concerns of sample selectivity bias. Beyond demonstrating several pitfalls in the calculation of marginal effects and associated levels of statistical significance derived from these models, we argue that many of the empirical questions addressed by political scientists would – for both substantive and statistical reasons – be more appropriately addressed using an alternative but closely related procedure referred to as the two-part model (2 PM). Aside from being simple to estimate, one key advantage of the 2 PM is its less onerous identification requirements. Specifically, the model does not require the specification of so-called exclusion restrictions, variables that are included in the selection equation of the Heckit model but omitted from the outcome equation. Moreover, we argue that the interpretation of the marginal effects from the 2 PM, which are in terms of actual outcomes, are more appropriate for the questions typically addressed by political scientists than the potential outcomes ascribed to the Heckit results. Drawing on data from the Correlates of War database, we present an empirical analysis of conflict intensity illustrating that the choice between the sample selection model and 2 PM can bear fundamentally on the conclusions drawn. [ABSTRACT FROM PUBLISHER]

Most measures of political decentralization seem to capture only specific facets of the concept. In particular, the excessive dependence on fiscal indicators has often been criticized since they seem unable to assess the degree of autonomy exerted by subnational governments. On the other hand, efforts directed at developing more encompassing indexes have had to rely on the aggregation of items developed by experts, a process that is prone to idiosyncratic errors. In this paper I propose the development of a measurement framework using a Bayesian factor analysis model for mixed ordinal and continuous outcomes. This model can efficiently combine multiple measures of decentralization regardless of their level of measurement, and in this way make use of both the rigour of fiscal indicators and the wider coverage of qualitative indicators. Applying this model to a set of 14 indicators I elaborate a more encompassing index of decentralization for 33 OECD countries. In order to illustrate the importance of using non-partial measures of decentralization, I use this index to replicate parts of De Mello and Barenstein (2001) exploratory analysis regarding the relationship between decentralization and corruption, showing that such relationship is practically non-existent. [ABSTRACT FROM AUTHOR]

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Political scientists have increasingly focused on causal processes that operate not solely on mean differences but on other stochastic characteristics of the distribution of a dependent variable. This paper surveys important statistical tools used to assess data in situations where the entire distribution of values is of interest. We first outline three broad conditions under which stochastic process methods are applicable and show that these conditions cover many domains of social inquiry. We discuss a variety of visual and analytical techniques, including distributional analysis, direct parameter estimates of probability density functions, and quantile regression. We illustrate the utility of these statistical tools with an application to budgetary data because strong theoretical expectations at the micro- and macrolevel exist about the distributional characteristics for such data. The expository analysis concentrates on three budget series (total, domestic, and defense outlays) of the U.S. government for 1800–2004. [ABSTRACT FROM PUBLISHER]

An important disconnect exists between the current use of formal modeling and applied statistical analysis. In general, a lack of linkage between the two can produce statistically significant parameters of ambiguous origin that, in turn, fail to assist in falsifying theories and hypotheses. To address this scientific challenge, a framework for unification is proposed. Methodological unification leverages the mutually reinforcing properties of formal and applied statistical analysis to produce greater transparency in relating theory to test. This framework for methodological unification, or what has been referred to as the empirical implications of theoretical models (EITM), includes (1) connecting behavioral (formal) and applied statistical concepts, (2) developing behavioral (formal) and applied statistical analogues of these concepts, and (3) linking and evaluating the behavioral (formal) and applied statistical analogues. The elements of this EITM framework are illustrated with examples from voting behavior, macroeconomic policy and outcomes, and political turnout. [ABSTRACT FROM AUTHOR]

The Cox proportional hazards model is widely used to model durations in the social sciences. Although this model allows analysts to forgo choices about the form of the hazard, it demands careful attention to the proportional hazards assumption. To this end, a standard diagnostic method has been developed to test this assumption. I argue that the standard test for nonproportional hazards has been misunderstood in current practice. This test detects a variety of specification errors, and these specification errors must be corrected before one can correctly diagnose nonproportionality. In particular, unmodeled nonlinearity can appear as a violation of the proportional hazard assumption for the Cox model. Using both simulation and empirical examples, I demonstrate how an analyst might be led astray by incorrectly applying the nonproportionality test. [ABSTRACT FROM PUBLISHER]

We look at conventional methods for removing endogeneity bias in regression models, including the linear model and the probit model. It is known that the usual Heckman two-step procedure should not be used in the probit model: from a theoretical perspective, it is unsatisfactory, and likelihood methods are superior. However, serious numerical problems occur when standard software packages try to maximize the biprobit likelihood function, even if the number of covariates is small. We draw conclusions for statistical practice. Finally, we prove the conditions under which parameters in the model are identifiable. The conditions for identification are delicate; we believe these results are new. [ABSTRACT FROM PUBLISHER]

Abstract: The underlying motivation of this collection is the concern that the failure to understand the relationship between our measure of a concept and the concept itself has real consequences. It may cause us to overlook model specification problems, to unnecessarily and inappropriately change our theories, and to miss out on information that could be usefully applied to the specification and estimation of our model. In order to take modelling seriously, we first must take measurement seriously. This is most effectively done when multiple measures of the underlying concept are utilised, allowing for the application of more appropriate models with more precise estimates. The contributions to this symposium demonstrate a wide range of such modelling approaches, each of which is tailored to match the theoretical expectations for the statistical characteristics of the data being used. By way of introduction, I highlight the measurement problems that these approaches are designed to tackle. [Copyright &y& Elsevier]

I examine a fundamental problem of politics in authoritarian regimes: the dictator and the ruling coalition must share power and govern in an environment where political influence must be backed by a credible threat of violence. I develop a model of authoritarian politics in which power sharing is complicated by this conflict of interest: by exploiting his position, the dictator may acquire more power at the expense of the ruling coalition, which may attempt to deter such opportunism by threatening to stage a coup. Two power-sharing regimes, contested and established dictatorships, may emerge as a result of strategic behavior by the dictator and the ruling coalition. This theory accounts for the large variation in the duration of dictators' tenures and the concentration of power in dictatorships over time, and it contributes to our understanding of the dynamics of power sharing and accountability in authoritarian regimes. [ABSTRACT FROM AUTHOR]

Election forecasting has experienced considerable growth as a subfield within political science. Research work on United Kingdom elections has been cutting edge here. The recent 2010 general election afforded the opportunity for a trial of different forecasting methodologies. These efforts are showcased in this volume, and include standard, and not-so-standard, statistical models. Overall, these models perform well, foreseeing the unprecedented outcome of a “hung parliament”, as most pollsters and pundits did not. Moreover, they achieved this accuracy with forecasts well in advance of the election itself. [Copyright &y& Elsevier]

The article is a reply to Neal Beck who commented on the authors' article in his own article titled "Time is Not A Theoretical Variable" both of which are in this issue. Beck's views on modeling duration dependence with a cubic polynomial are discussed. Topics in the discussion include statistical theories about cubic splines, approximation theory, knot selection, and binary data. The use of time-series analysis, game theory, and dynamic choice models in political analysis research are also discussed.