Time Series Analysis

How a Deep Neural Network Contributes to Learning Causal Graph and Forecasting Political Dynamics

Seo Eun Yang (Ohio State University)

Abstract: Nonlinearity has been considerably interested in time series analysis of conflict/opinion dynamics. However, handling unknown nonlinear interactions on time series data is a methodologically challenging task because traditional models such as VAR Granger analysis or B-SVAR...

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Multiplicative Interactions in Error Correction Models

Flávio Souza (Texas A&M University)

Abstract: Error correction modeling (ECM) is a common time-series strategy when both dependent and independent variables contain a unit root and are cointegrated. But one of its principal drawbacks is its inflexibility—since it requires that every independent variable enter the right-hand...

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Donation Dynamics: Do Critical Campaign Events Influence Contributions?

Seo-young Silvia Kim (California Institute of Technology)

Abstract: What events motivate individual campaign contributions? Using the 2016 campaign finance data from the Federal Election Commission as a daily time-series, I test the hypothesis that if presidential donors are either instrumental or momentum-driven, they...

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New Frontiers in Dynamic Pie Modeling

Andrea Junqueira (Texas A&M University), Ali Kagalwala (Texas A&M University), Andrew Philips (University of Colorado Boulder) and Guy Whitten (Texas A&M University)

Abstract: In this paper, we...

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Learning From Likes: The Effect of Social Engagement Feedback on Politicians' Social Media Communications

Ryden Butler (Washington University in St. Louis)

Abstract: Considerable research indicates that politicians adapt their rhetoric in order to appeal to the preferences of their constituents. However scant research has examined this phenomenon in the context of politicians' social media communications. In this study I use...

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