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Recent developments in the analysis of long-memoried processes provide important leverage for analysing time-series variables of interest to political scientists. This article provides an accessible exposition of these methods and illustrates their utility for addressing protracted controversies regarding the political economy of party support in Britain. Estimates of the fractionally differencing parameter, d, reveal that governing party support, prime ministerial approval and economic evaluations are long-memoried and non-stationary, and that governing party support and prime ministerial approval are fractionally cointegrated. Pace conventional wisdom that party leader images matter little, if at all, analyses of multivariate fractional error correction models show that prime ministerial approval has important short-run and long-run effects on party support. Prospective and retrospective personal economic evaluations are influential but, contrary to a longstanding claim, national economic evaluations are not significant. The article concludes by suggesting that individual-level heterogeneity is a likely source of the observed aggregate-level fractional integration in governing party support and its determinants. Specifying parsimonious models that incorporate theoretically meaningful heterogeneity is a challenging topic for future research. (British Journal of Political Science / FUB)

AbstractA fundamental challenge facing applied time‐series analysts is how to draw inferences about long‐run relationships (LRR) when we are uncertain whether the data contain unit roots. Unit root tests are notoriously unreliable and often leave analysts uncertain, but popular extant methods hinge on correct classification. Webb, Linn, and Lebo (WLL; 2019) develop a framework for inference based on critical value bounds for hypothesis tests on the long‐run multiplier (LRM) that eschews unit root tests and incorporates the uncertainty inherent in identifying the dynamic properties of the data into inferences about LRRs. We show how the WLL bounds procedure can be applied to any fully specified regression model to solve this fundamental challenge, extend the results of WLL by presenting a general set of critical value bounds to be used in applied work, and demonstrate the empirical relevance of the LRM bounds procedure in two applications.

Time‐varying relationships and volatility are two methodological challenges that are particular to the field of time series. In the case of the former, more comprehensive understanding can emerge when we ask under what circumstances relationships may change. The impact of context—such as the political environment, the state of the economy, the international situation, etc.—is often missing in dynamic analyses that estimate time‐invariant parameters. In addition, time‐varying volatility presents a number of challenges including threats to inference if left unchecked. Among time‐varying parameter models, the Dynamic Conditional Correlation (DCC) model is a creative and useful approach that deals effectively with over‐time variation in both the mean and variance of time series. The DCC model allows us to study the evolution of relationships over time in a multivariate setting by relaxing model assumptions and offers researchers a chance to reinvigorate understandings that are tested using time series data. We demonstrate the method's potential in the first example by showing how the importance of subjective evaluations of the economy are not constant, but vary considerably over time as predictors of presidential approval. A second example using international dyadic time series data shows that the story of movement and comovement is incomplete without an understanding of the dynamics of their variance as well as their means.

MacKuen, Erikson, and Stimson's classic article "Macropartisanship" extended the study of political behavior from static analyses of American elections to the dynamics of partisanship between elections. This launched new frontiers of research, such as studying the effects of presidential approval and economic indices on aggregate party identification. However, the Macropartisanship literature made an important oversight: changes in partisanship between elections are usually from one partisan group to identification as an independent, or vice versa. A single measure of aggregate partisanship, like the original Macropartisanship measure, leaves out independents altogether. This has important theoretical and empirical consequences that are evident in an era of increasingly strong partisanship. We conceive of Macropartisanship as a compositional variable and study how its components are affected by changes in economic sentiment and presidential approval.

We construct a new measure of the aggressiveness of COVID-19 policies in 75 Canadian and American cities and estimate the effect of these policies on mobility patterns in each city. Using a new dataset of five municipal COVID-19 policy indicators for each of our 75 cities, combined with 11 provincial/state policy indicators, we estimate a daily measure of the "aggressiveness" of the provincial/state and municipal COVID-19 policy mix in each city. We then estimate the effects of these policies on subsequent mobility behaviour using dynamic time series models. We find strong evidence of policy effects on subsequent mobility behaviour, but few overall differences between Canadian and American cities. We discuss the significance of our findings both for COVID-19 policy research and for other comparative urban policy research in multilevel policy environments.

Why does the influence of Congressional parties fluctuate over time? Building on prevailing answers, we develop a model, Strategic Party Government, which highlights the electoral motives of legislative parties and the strategic interaction between parties. We test this theory using the entire range of House and Senate party behavior from 1789 to 2000 and find that the strategic behavior of parties complements members' preferences as an explanation for variation in party influence. Specifically, the strongest predictors of one party's voting unity are the unity of the opposing party and the difference between the parties in the preceding year. Moreover, we find strong links between party behavior in Congress and electoral outcomes: an increase in partisan influence on legislative voting has adverse electoral costs, while winning contested votes has electoral benefits.

AbstractGrant and Lebo (2016) and Keele et al. (2016) clarify the conditions under which the popular general error correction model (GECM) can be used and interpreted easily: In a bivariate GECM the data must be integrated in order to rely on the error correction coefficient, $\alpha _1^\ast$, to test cointegration and measure the rate of error correction between a single exogenous x and a dependent variable, y. Here we demonstrate that even if the data are all integrated, the test on $\alpha _1^\ast$ is misunderstood when there is more than a single independent variable. The null hypothesis is that there is no cointegration between y and any x but the correct alternative hypothesis is that y is cointegrated with at least one—but not necessarily more than one—of the x's. A significant $\alpha _1^\ast$ can occur when some I(1) regressors are not cointegrated and the equation is not balanced. Thus, the correct limiting distributions of the right-hand-side long-run coefficients may be unknown. We use simulations to demonstrate the problem and then discuss implications for applied examples.