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ABSTRACTA researcher's "pipeline" refers to the progression of papers from the idea stage to the publication stage. Tenure-track faculty and graduate students often are advised that a busy pipeline is the way to tenure. However, beyond the simple encouragement to "work harder," "write more," and "publish more," not much direction is given to junior scholars regarding how they should think about and manage their research pipeline. How does one develop a large number of papers from their first stages to the pages of respected journals? This article discusses the author's system for pipeline management. The system works (1) by separating the difficult exercise of publishing a paper into eight manageable stages; and (2) by organizing a set of papers using these steps according to a practicable timeline.

Intro -- Contents -- Preface -- Chapter 1. Worst. Congress. Ever. Why? -- Chapter 2. A Theory of Strategic Parties -- Chapter 3. Microlevel Foundations: Do Citizens Dislike Partisanship or Extremism? / Coauthored with Everett Young -- Chapter 4. The Electoral Costs of Party Loyalty in Congress / Coauthored with Jamie Carson and Ellen Key -- Chapter 5. The Effects of Legislative Behavior on Aggregate Election Outcomes -- Chapter 6. The Dynamics of Partisan Power -- Chapter 7. Party Competition in Legislative Voting -- Chapter 8. Conclusion -- Notes -- References -- Index.

While traditionally considered for non-stationary and cointegrated data, DeBoef and Keele suggest applying a General Error Correction Model (GECM) to stationary data with or without cointegration. The GECM has since become extremely popular in political science but practitioners have confused essential points. For one, the model is treated as perfectly flexible when, in fact, the opposite is true. Time series of various orders of integration–stationary, non-stationary, explosive, near- and fractionally integrated–should not be analyzed together but researchers consistently make this mistake. That is, withoutequation balancethe model is misspecified and hypothesis tests and long-run-multipliers are unreliable. Another problem is that the error correction term's sampling distribution moves dramatically depending upon the order of integration, sample size, number of covariates, and theboundednessofYt.This means that practitioners are likely to overstate evidence of error correction, especially when using a traditionalt-test. We evaluate common GECM practices with six types of data, 746 simulations, and five paper replications.

The papers in this symposium agree on several points. In this article, we sort through some remaining areas of disagreement and discuss some of the practical issues of time series modeling we think deserve further explanation. In particular, we have five points: (1) clarifying our stance on the general error correction model in light of the comments in this issue; (2) clarifying equation balance and discussing how bounded series affects our thinking about stationarity, balance, and modeling choices; (3) answering lingering questions about our Monte Carlo simulations and exploring potential problems in the inferences drawn from long-run multipliers; (4) reviewing and defending fractional integration methods in light of the questions raised in this symposium and elsewhere; and (5) providing a short practical guide to estimating a multivariate autoregressive fractionally integrated moving average model with or without an error correction term.

Repeated cross-sectional (RCS) designs are distinguishable from true panels and pooled cross-sectional time series (PCSTS) since cross-sectional units (e.g., individual survey respondents) appear but once in the data. This poses two serious challenges. First, as with PCSTS, autocorrelation threatens inferences. However, common solutions like differencing and using a lagged dependent variable are not possible with RCS since lags for i cannot be used. Second, although RCS designs contain information that allows both aggregate- and individual-level analyses, available methods-from pooled ordinary least squares to PCSTS to time series-force researchers to choose one level of analysis. The PCSTS tool kit does not provide an appropriate solution, and we offer one here: double filtering with ARFIMA methods to account for autocorrelation in longer RCS followed by the use of multilevel modeling to estimate both aggregate- and individual-level parameters simultaneously. We use Monte Carlo experiments and three applied examples to explore the advantages of our framework. Adapted from the source document.

Various approaches have been taken to forecasting the outcomes of elections. The "PM and the Pendulum" model uses Prime Ministerial Approval as the sole short-term predictor of the vote in British General Elections. Here we test the forecasting power of this predictor against a close rival -- Government Approval. Using a time-varying parameter estimator, we demonstrate that between elections and especially in the months preceding a General Election, PM Approval is the most accurate predictor of both governing party vote intentions and the actual vote on Election Day, as measured in governing party vote share. The autoregressive model for translating votes into seats, based on elections from 1910 onward, provides an almost exact prediction of the outcome of the 2010 contest. Adapted from the source document.

This article applies the strategic parties framework to the 111th Congress and 2010 election results that followed. In 2009-2010, the Democrats pursued an ambitious agenda over the nearly unanimous opposition of Congressional Republicans, leading to a high level of partisanship on both sides. This partisanship was costly in the 2010 elections. Like other papers on this election, we find some evidence that key roll calls were linked to decreased electoral vote share. However, the clearer pattern is that overall patterns of partisanship had a consistent detrimental effect on incumbents running for reelection.