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Long-Term Effects in Models with Temporal Dependence

Published online by Cambridge University Press:  04 January 2017

Laron K. Williams
Laron K. Williams*
Affiliation:
Department of Political Science, University of Missouri, Columbia, MO, USA, e-mail: [email protected]
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Abstract

A dominant trend in models with binary outcomes is to control for unmodeled duration dependence by including temporal dependence variables. A second, distinct trend is to interpret both the short- and long-term effects of explanatory variables in autoregressive models. While the first trend is nearly ubiquitous in models with binary outcomes, the second trend has yet to be applied consistently beyond models with continuous outcomes. While scholars use temporal splines and cubic polynomials to model the underlying hazard rate, they have neglected the fact that this causes the explanatory variables to have a long-term effect (LTE) by modifying the future values of the temporal dependence variables. In this article, I propose a simple technique that estimates a wide range of probabilistic LTEs in models with temporal dependence variables. These effects can range from simple LTEs for a one-time change in an explanatory variable to more complex scenarios where effects change in magnitude with time and compound across repeated events. I then replicate Clare's (2010, Ideological fractionalization and the international conflict behavior of parliamentary democracies. International Studies Quarterly 54:965–87) examination of the influence of government fractionalization on conflict behavior to show that failing to interpret the results within the context of temporal dependence underestimates the total impact of fractionalization by neglecting LTEs.

Type
Articles
Information
Political Analysis , Volume 24 , Issue 2 , Spring 2016 , pp. 243 - 262
Copyright
Copyright © The Author 2016. Published by Oxford University Press on behalf of the Society for Political Methodology 

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References

Ai, Chunrong, and Norton, Edward C. 2003. Interaction terms in logit and probit models. Economics Letters 80:123–29.CrossRefGoogle Scholar
Beardsley, Kyle. 2008. Agreement without peace? International mediation and time inconsistency problems. American Journal of Political Science 52:723–40.Google Scholar
Beck, Nathaniel. 2001. Time-series-cross-section data: What have we learned in the past few years? Annual Review of Political Science 4:271–93.CrossRefGoogle Scholar
Beck, Nathaniel. 2010. Time is not a theoretical variable. Political Analysis 18:293–94.Google Scholar
Beck, Nathaniel, Katz, Jonathan, and Tucker, Richard. 1998. Taking time seriously: Time-series-cross-section analysis with a binary dependent variable. American Journal of Political Science 42:1260–88.Google Scholar
Bennett, D. Scott. 1999. Parametric models, duration dependence, and time-varying data revisited. American Journal of Political Science 43:256–70.CrossRefGoogle Scholar
Bennett, D. Scott. 2006. Toward a continuous specification of the democracy-autocracy connection. International Studies Quarterly 50:313–38.Google Scholar
Bennett, D. Scott, and Stam, Allan. 1996. The duration of interstate wars, 1816–1985. American Political Science Review 90:239–57.Google Scholar
Bennett, D. Scott, and Stam, Allan. 2000. Research design and estimator choices in the analysis of interstate dyads: When decisions matter. Journal of Conflict Resolution 44:653–85.Google Scholar
Berry, William D., DeMeritt, Jacqueline H. R., and Esarey, Justin. 2010. Testing for interaction in binary logit and probit models: Is a product term essential? American Journal of Political Science 54:248–66.Google Scholar
Box-Steffensmeier, Janet M., and Jones, Bradford S. 2004. Event history modeling: A guide for social scientists. Cambridge: Cambridge University Press.Google Scholar
Buhaug, Halvard, and Skrede Gleditsch, Kristian. 2008. Contagion or confusion? Why conflicts cluster in space. International Studies Quarterly 52:215–33.Google Scholar
Carter, David B., and Signorino, Curtis S. 2010. Back to the future: Modeling time dependence in binary data. Political Analysis 18:271–92.Google Scholar
Clare, Joe. 2010. Ideological fractionalization and the international conflict behavior of parliamentary democracies. International Studies Quarterly 54:965–87.Google Scholar
Collier, Paul, Elliot, V.L., Hegre, Havard Hoeffler, Anke Reynal-Querol, Marta and Sambanis, Nicholas. 2003. Breaking the conflict trap. Washington, D.C.: Oxford University Press.Google Scholar
Crescenzi, Mark J. C. 2007. Reputation and international conflict. American Journal of Political Science 51:382–96.Google Scholar
de Boef, Suzanna, and Keele, Luke. 2008. Taking time seriously: Dynamic regression. American Journal of Political Science 52:184–200.Google Scholar
Desposato, Scott W. 2006. Parties for rent? Ambition, ideology, and party switching in Brazil's chamber of deputies. American Journal of Political Science 50:62–80.CrossRefGoogle Scholar
Esarey, Justin, and DeMeritt, Jacqueline H. R. 2014. Defining and modeling state-dependent dynamic systems. Political Analysis 22:61–85.Google Scholar
Gandrud, Christopher, and Williams, Laron K. 2016. pltesim: An R package for simulating probabilistic long-term effects in models with temporal dependence. Working Paper, Hertie School of Governance.Google Scholar
Greene, William H. 2008. Econometric analysis. 6th ed. Upper Saddle River, NJ: Pearson Prentice Hall.Google Scholar
Greenhill, Brian, Ward, Michael D., and Sacks, Audrey. 2011. The separation plot: A new visual method for evaluating the fit of binary models. American Journal of Political Science 55:991–1003.Google Scholar
Grieco, Joseph M. 2001. Repetitive military challenges and recurrent international conflicts, 1918–1994. International Studies Quarterly 45:295–316.Google Scholar
Hanmer, Michael J., and Ozan Kalkan, Kerem. 2013. Behind the curve: Clarifying the best approach to calculating predicted probabilities and marginal effects from limited dependent variable models. American Journal of Political Science 57:263–77.Google Scholar
Herron, Michael C. 1999. Post-estimation uncertainty in limited dependent variable models. Political Analysis 8:83–98.Google Scholar
Jackman, Simon. 2000. In and out of war and peace: Transitional models of international conflict. Working Paper, Stanford University.Google Scholar
Keele, Luke. 2008. Semiparametric regression for the social sciences. West Sussex, England: Wiley and Sons.Google Scholar
King, Gary, and Zeng, Langche. 2006. The dangers of extreme counterfactuals. Political Anaysis 14:131–59.Google Scholar
King, Gary, Tomz, Michael, and Wittenberg, Jason. 2000. “Making the most of statistical analyses: Improving interpretation and presentation. American Journal of Political Science 44:347–61.Google Scholar
Li, Quan. 2009. Democracy, autocracy, and expropriation of foreign direct investment. Comparative Political Studies 42:1098–1127.Google Scholar
Licht, Amanda A. 2011. Change comes with time: substantive interpretation of nonproportional hazards in event history analysis. Political Analysis 19:227–43.Google Scholar
Londregan, John B., and Poole, Keith T. 1990. Poverty, the coup trap, and the seizure of executive power. World Politics 42:151–83.Google Scholar
Long, J. Scott. 1997. Regression models for categorical and limited dependent variables. Thousand Oaks, CA: Sage Publications.Google Scholar
McGrath, Liam F. 2015. Estimating oonset of binary events in panel data. Political Analysis 23:534–49.Google Scholar
Melander, Erik. 2005. Gender equality and intrastate armed conflict. International Studies Quarterly 49:695–714.Google Scholar
Mooney, Christopher Z., and Lee, Mei-Hsien. 2000. The influence of values on consensus and contentious morality policy: U.S. death penalty reform, 1956–82. Journal of Politics 62:223–39.Google Scholar
Nagler, Jonathan. 1991. The effect of registration laws and education on United States voter turnout. American Political Science Review 85:1393–1405.Google Scholar
Nagler, Jonathan. 1994. Scobit: An alternative estimator to logit and probit. American Journal of Political Science 38:230–55.Google Scholar
Norton, Edward C., Wang, Hua, and Ai, Chunrong. 2004. Computing interaction effects and standard errors in logit and probit models. The Stata Journal 4:154–67.Google Scholar
Oneal, John R., and Russett, Bruce. 1999. Assessing the liberal peace with alternative specifications: Trade still reduces conflict. Journal of Peace Research 36:423–42.Google Scholar
Pindyck, Robert S., and Rubinfeld, Daniel L. 1991. Econometric models and economic forecasts. 3rd ed. New York: McGraw-Hill.Google Scholar
Przeworski, Adam, and Raymond Vreeland, James. 2002. A statistical model of bilateral cooperation. Political Analysis 10:101–12.Google Scholar
Przeworski, Adam, Alvarez, Michael E., Antonio Cheibub, Jose, and Limongi, Fernando. 2000. Democracy and development: Political institutions and well-being in the world, 1950–1990. Cambridge: Cambridge University Press.Google Scholar
Rainey, Carlisle. 2015. Transformation-induced bias: Unbiased coefficients do not imply unbiased quantities of interest. Working Paper, Texas A&M University.Google Scholar
Senese, Paul D. 2005. Territory, contiguity, and international conflict: Assessing a new joint explanation. American Journal of Political Science 49:769–79.CrossRefGoogle Scholar
Shipan, Charles R., and Volden, Craig. 2008. The mechanisms of policy diffusion. American Journal of Political Science 52:840–57.Google Scholar
Simmons, Beth A. 2000. International law and state behavior: Commitment and compliance in international monetary affairs. American Political Science Review 94:819–35.Google Scholar
Smith, Benjamin. 2004. Oil wealth and regime survival in the developing world, 1960–1999. American Journal of Political Science 48:232–46.Google Scholar
Thompson, William R. 2001. Identifying rivals and rivalries in world politics. International Studies Quarterly 45:557–86.Google Scholar
Train, Kenneth E. 2009. Discrete choice methods with simulation. 2nd ed. Cambridge, England: Cambridge University Press.Google Scholar
Weidmann, Nils B., and Ward, Michael D. 2010. Predicting conflict in space and time. Journal of Conflict Resolution 54:883–901.Google Scholar
Williams, Laron K. 2016. Replication Data for: Long-term effects in models with temporal dependence. http://dx.doi.org/10.7910/DVN/KRKWK8,HarvardDataverse,V1[UNF:6:pwv9cSHI53tZqXlrJ9EDaw¼¼.Google Scholar
Williams, Laron K., and Whitten, Guy D. 2012. But wait, there's more! Maximizing substantive inferences from tscs models. Journal of Politics 74:685–93.CrossRefGoogle Scholar
Zorn, Christopher. 2000. Modeling duration dependence. Political Analysis 8:367–80.CrossRefGoogle Scholar
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