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Below results based on the criteria 'dynamics'
Total number of records returned: 7
Spatio-Temporal Models for Political-Science Panel and Time-Series-Cross-Section Data
Spatio-Temporal Steady-State Effects
Building from our broader project exploring spatial-econometric models for political science, this paper discusses estimation, interpretation, and presentation of spatio-temporal models. We first present a generic spatio-temporal-lag model and two methods, OLS and ML, for estimating parameters in such models. We briefly consider those estimators’ properties analytically before showing next how to calculate and to present the spatio-temporal dynamic and long-run steady-state equilibrium effects—i.e., the spatio-temporal substance of the model—implied by the coefficient estimates. Then, we conduct Monte Carlo experiments to explore the properties of the OLS and ML estimators, and, finally, we conclude with a reanalysis of Beck, Gleditsch, and Beardsley’s (2006) state-of-the-art study of directed export flows among major powers.
Modeling Dynamics in Time-Series-Cross-Section Political Economy Data
lagged dependent variable
This paper deals with a variety of dynamic issues in the analysis of time-series-cross-section (TSCS) data. While the issues raised are more general, we focus on applications to political economy. We begin with a discussion of specification and lay out the theoretical differences implied by the various types of time series models that can be estimated. It is shown that there is nothing pernicious in using a lagged dependent variable and that all dynamic models either implicitly or explicitly have such a variable; the differences between the models relate to assumptions about the speeds of adjustment of measured and unmeasured variables. When adjustment is quick it is hard to differentiate between the various models; with slower speeds of adjustment the various models make sufficiently different predictions that they can be tested against each other. As the speed of adjustment gets slower and slower, specification (and estimation) gets more and more tricky. We then turn to a discussion of estimation. It is noted that models with both a lagged dependent variable and serially correlated errors can easily be estimated; it is only OLS that is inconsistent in this situation. We then show, via Monte Carlo analysis shows that for typical TSCS data that fixed effects with a lagged dependent variable performs about as well as the much more complicated Kiviet estimator, and better than the Anderson-Hsiao estimator (both designed for panels).
Estimation of Evolutionary Processes
Evolutionary game theory has accumulated an enormous body of theoretical work and even some proposed substantive applications. However, current empirical work has shown little evidence of evolutionary models matching field or experimental data. We argue that this is in part because estimation has been of overly restrictive models that make unwarranted assumptions either on the matrix of fitnesses in the evolutionary game, or more often, on the rule mapping the selection process. These heavy assumptions facilitate easy estimation procedures, but cripple the ability for evolutionary models to describe the data and for the researcher to reveal from the data the true quantities of interest in the evolutionary model. We demonstrate an EM based algorithm capable of estimating both the matrix of fitnesses and the selection mechanism, and apply this to experimental data. We show that the evolutionary model fits the experimental data progressively better as the assumptions of the evolutionary model are incorporated into the experiment. We also show that the model we propose can be used as a flexible estimator for deducing flows over compositional variables across time, and compare it to the more typical compositional model of Aitchison (1986).
A Dynamic Panel Analysis of Campaign Contributions in Elections for the U.S. House of Representatives
Himmelberg, Charles P.
panel data methods
Political scientists have recognized the importance of dynamics in understanding the role of campaign finance in congressional elections. Yet for the most part, researchers have not exploited available data to its fullest or used appropriate methods to answer questions of interest. Though the Federal Election Commission's reporting and disclosure requirements enable us to use panel data models, researchers have ignored these powerful tools. One of the main advantages of panel data methods is that they enable us to account for unobserved individual and temporal effects that, if not accounted for, might lead us to incorrect inferences. In this paper we describe the problems with estimating dynamic panel models and discuss techniques that correct for these problems. We apply recently developed panel data methods to estimate a dynamic model of campaign finance and assess the usefulness of these methods by examining the robustness of results obtained with more traditional methods. We examine the relationship between past and current campaign contributions to incumbents and challengers during the 1986 through 1992 election cycles. Dynamic panel estimators give results that differ in substantively interesting ways from those given by standard estimators. In particular, the estimates obtained from dynamic panel methods suggest that challengers who are successful fundraisers can cut into the fundraising efforts of incumbents.
Congressional Campaign Contributions, District Service and Electoral Outcomes in the United States: Statistical Tests of a Formal Game Model with Nonlinear Dynamics
Mebane, Walter R.
Whitney embedding theorem
multivariate normal distribution
Using a two-stage game model of congressional campaigns, the second stage being a system of ordinary differential equations, I argue that candidates, political parties and financial contributors interact strategically in American congressional elections in a way that is inherently nonlinear. Congressional races in which the incumbent faces a challenge are generated by dynamical systems that have Hopf bifurcations: a small change in the challenger's quality or in the type of district service can change a stable incumbent advantage into an oscillating race in which the incumbent's chances are uncertain. The normal form equations for such a system inspire a statistical model that can recover qualitative features of the dynamics from cross-sectional data. I estimate and test the model using data from the 1984 and 1986 election periods for political action committee campaign contributions, intergovernmental transfers and general election vote shares.
Why Study Only Presidential Campaigns? Statewide Races as a Laboratory for Campaign Analysis
Alvarez, R. Michael
Political campaigns play a central role in democratic politics since they are an important source of contact between citizens and voters. But the literature has been quite pessimistic about whether political campaigns can influence the preferences and behavior of voters. In this paper I argue that one of the primary reasons for this pessimism stems from the consistent and lasting focus on presidential campaigns. While presidential campaigns are an important aspect of the American political process, they make poor laboratories for the study of campaigns. Instead I argue that statewide political campaigns provide a much better laboratory for the study of campaigns. The paper presents a series of empirical analyses of statewide campaigns and concludes with a discussion of different designs for the study of statewide campaigns.
Revisiting Dynamic Specification
De Boef, Suzanna
auto-distributed lag models
Dramatic change in the world around us has stimulated a wealth of interest in research questions about the dynamics of political processes. At the same time we have seen increases in the number of time series data sets and the length of typical time series. Parallel advances have occurred in time series econometrics. These events have turned more political scientists into time series analysts and motivated more political methodologists to delve further into the annals of time series econometrics. But before taking the next advanced time series course, we recommend that time series analysts devote more time to issues of specification and interpretation. While advances in time series methods have helped us to change how we think about the process of political change in important ways, too often analysts have failed to recognize the wide number of general models available for stationary time series data, have estimated restricted models without testing the implied restrictions, and have done a poor job of drawing interpretations from their results. The consequences, at best, are poor connections between theory and tests and thus a limited cumulation of knowledge. More likely, the costs include biased results as well. We identify a number of general dynamic specifications, each a linear parameterization of the basic autoregressive distributed lag model and each highlighting different types of information. We then discuss the consequences of imposing restrictions on any of them. We recommend that analysts start with one or a combination of these general models and test for restrictions before adopting them. We illustrate this strategy with data on support for the Supreme Court and on presidential approval. Finally, we recommend that analysts make use of the wide array of information that can be gleaned from dynamic specifications. Such a practice will help us to better equate dynamic econometrics with dynamic theory.