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Below results based on the criteria 'event'
Total number of records returned: 36

1
Paper
Analyzing the Dynamics of International Mediation Processess in the Middle East and the former Yugoslavia
Gerner, Deborah J.
Schrodt, Philip A.

Uploaded 06-28-2001
Keywords mediation
event data
cross-correlation
conflict
Middle East
Abstract This paper discusses a new National Science Foundation-funded project that will examine the dynamics of third-party international mediation using statistical time-series analyses of political event data. Third-party mediation was attempted in over half of the conflicts in the post-WWII period and it is likely that the use of mediation has increased following the end of the Cold War. Surprisingly, there have been few systematic studies on mediation. Those that do exist have generally focused on relatively static contextual factors such as the the conflict's attributes and the prior relationship between the mediator and protagonists rather than on dynamic factors' both contextual and process that may contribute to the success or failure of mediation activities. In contrast, the extensive qualitative literature provides numerous hypotheses about dynamic aspects of mediation. This, however, primarily consists of case studies, often by mediation practitioners, that exhibit little cumulation and, when taken as a whole, are rife with contradictory assertions. The project will formally test a number of the hypotheses embedded in the theoretical and qualitative literatures on mediation, using automated coding of event data from news-wire sources and employing time-series and event- history methods. A system of specialized event codes that a sensitive to mediation activities will be developed, then events will be coded from news reports using the TABARI machine coding program. The research will look at the factors that influence (1) whether mediation is accepted by the parties in a conflict, (2) whether formal agreements are reached, and (3) whether the agreements actually reduce the level of conflict. The project will initially focus on conflicts in the Middle East, a region where the principal investigators have substantial field experience. After refining the statistical tests on the Middle East case, the analysis will be extended to event data on conflicts in the former Yugoslavia and West Africa. The paper presents the results of an empirical "plausibility probe" based on existing WEIS-coded event data for the Levant and the former Yugoslavia. It employs a simple measure of third-party mediation efforts as the independent variables and Goldstein-scaled cooperation as the dependent variable. In the Levant, we find a weak but consistent pattern of mediation correlating with past conflictual activity, and resulting in later increases in cooperation. In the former Yugoslavia, the analysis shows strikingly different results for the mediation efforts the UN, European states, and the US. All three respond to increased conflict, but the UN efforts correlate with greater conflict, the US efforts with greater cooperation, and the European efforts have no effect. These results are consistent with many of the qualitative assessments of these efforts, and suggest that the event data approach will produce credible results

2
Paper
The Analysis of Binary Time-Series--Cross-Section Data and/or The Democratic Peace
Beck, Nathaniel
Katz, Jonathan

Uploaded 07-18-1997
Keywords binary dependent variable
time-series--cross-section data
serially correlated errors
event history analysis
Abstract The analysis of binary time-series--cross-section (BTSCS) data almost invariably ignores temporal dependence. Using Monte Carlo we show that ordinary probit standard errors underestimate variability in the presence of serially correlated errors. This underestimate, while severe, is smaller than in a corresponding OLS analysis. The simulations show that the standard errors can be partially corrected using Huber's method. We then discuss a variety of other methods for allowing temporal dependency in BTSCS estimation. The simulations show that using a lagged dependent variable will not be the panacea that it is for continuous data. We briefly examine the ``general estimating equation approach.'' We then note the equivalence of BTSCS and event history data, and thus show that common event history techniques which allow for ``duration dependence'' can be used for temporally dependent BTSCS data. The methods are used to re-analyze Oneal and Russett's study of the role of democracy and trade in facilitating peace. After correcting for temporal dependence we still find support for the democratic peace hypothesis but no longer find support for the liberal trade hypothesis.

3
Paper
Aggregation Among Binary, Count, and Duration Models
King, Gary
Signorino, Curtis S.
Alt, James E.

Uploaded 08-28-2000
Keywords Duration
event count
binary
renewal process
aggregation
Abstract Binary, count, and duration data all code discrete events occurring at points in time. Although a single data generation process can produce all of these three data types, the statistical literature is not very helpful in providing methods to estimate parameters of the same process from each. In fact, only a single theoretical process exists for which known statistical methods can estimate the same parameters --- and it is generally used only for count and duration data. The result is that seemingly trivial decisions about which level of data to use can have important consequences for substantive interpretations. We describe the theoretical event process for which results exist, based on time-independence. We also derive a set of models for a time-dependent process and compare their predictions to those of a commonly used model. Any hope of understanding and avoiding the more serious problems of aggregation bias in events data is contingent on first deriving a much wider arsenal of statistical models and theoretical processes that are not constrained by the particular forms of data that happen to be available. We discuss these issues and suggest an agenda for political methodologists interested in this very large class of aggregation problems.

4
Paper
Pattern Recognition of International Crises using Hidden Markov Models
Schrodt, Philip A.

Uploaded 06-30-1997
Keywords hidden Markov models
event data
early warning
international crisis
sequence analysis
Middle East
WEIS
BCOW
Abstract Event data are one of the most widely used indicators in quantitative international relations research. To date, most of the models using event data have constructed numerical indicators based on the characteristics of the events measured in isolation and then aggregated. An alternative approach is to use quantitative pattern recognition techniques to compare an existing sequence of behaviors to a set of similar historical cases. This has much in common with human reasoning by historical analogy while providing the advantages of systematic and replicable analysis possible using machine-coded event data and statistical models. This chapter uses "hidden Markov models" —- a recently developed sequence- comparison technique widely used in computational speech recognition —- to measure similarities among international crises. The models are first estimated using the Behavioral Correlates of War data set of historical crises, then applied to an event data set covering political behavior in the contemporary Middle East for the period April 1979 through February 1997. A split-sample test of the hidden Markov models perfectly differentiates crises involving war from those not involving war in the cases used to estimate the models. The models also provide a high level of discrimination in a set of test cases not used in the estimated, and most of the erroneously-classified cases have plausible distinguishing features. The difference between the war and nonwar models also correlates significantly with a scaled measure of conflict in the contemporary Middle East. This suggests that hidden Markov models could be used to develop conflict measures based on event similarities to historical conflicts rather than on aggregated event scores.

5
Paper
Forecasting Conflict in the Balkans using Hidden Markov Models
Schrodt, Philip A.

Uploaded 08-24-2000
Keywords forecasting
event data
hidden Markov models
conflict
Balkans
Yugoslavia
Abstract This study uses hidden Markov models (HMM) to forecast conflict in the former Yugoslavia for the period January 1991 through January 1999. The political and military events reported in the lead sentences of Reuters news service stories were coded into the World Events Interaction Survey (WEIS) event data scheme. The forecasting scheme involved randomly selecting eight 100-event "templates" taken at a 1-, 3- or 6-month forecasting lag for high-conflict and low-conflict weeks. A separate HMM is developed for the high-conflict-week sequences and the low-conflict-week sequences. Forecasting is done by determining whether a sequence of observed events fit the high-conflict or low-conflict model with higher probability. Models were selected to maximize the difference between correct and incorrect predictions, evaluated by week. Three weighting schemes were used: unweighted (U), penalize false positives (P) and penalize false negatives (N). There is a relatively high level of convergence in the estimatesčthe best and worst models of a given type vary in accuracy by only about 15% to 20%. In full-sample tests, the U and P models produce at overall accuracy of around 80%. However, these models correctly forecast only about 25% of the high-conflict weeks, although about 60% of the cases where a high-conflict week has been forecast turn out to have high conflict. In contrast, the N model has an overall accuracy of only about 50% in full-sample tests, but it correctly forecasts high-conflict weeks with 85% accuracy in the 3- and 6-month horizon and 92% accuracy in the 1-month horizon. However, this is achieved by excessive predictions of high-conflict weeks: only about 30% of the cases where a high-conflict week has been forecast are high-conflict. Models that use templates from only the previous year usually do about as well as models based on the entire sample. The models are remarkably insensitive to the length of the forecasting horizončthe drop-off in accuracy at longer forecasting horizons is very small, typically around 2%-4%. There is also no clear difference in the estimated coefficients for the 1-month and 6-month models. An extensive analysis was done of the coefficient estimates in the full-sample model to determine what the model was "looking at" in order to make predictions. While a number of statistically significant differences exist between the high and low conflict models, these do not fall into any neat patterns. This is probably due to a combination of the large number of parameters being estimated, the multiple local maxima in the estimation surface, and the complications introduced by the presence of a number of very low probability event categories. Some experiments with simplified models indicate that it is possible to use models with substantially fewer parameters without markedly decreasing the accuracy of the predictions; in fact predictions of the high conflict periods actually increase in accuracy quite substantially.

6
Paper
Estimating the Same Quantities from Different Levels of Data: Time Dependence and Aggregation Bias in Event Process Models
Alt, James E.
King, Gary
Signorino, Curtis S.

Uploaded 06-28-1997
Keywords event process
exponential
gamma
time dependence
aggregation
Abstract Binary, count, and duration data all code for discrete events occurring at points in time. Although a single data generation process can produce any of these three data types, the statistical literature is not very helpful in providing methods to estimate parameters of the same process from each. In fact, only a single theoretical process exists for which known statistical methods can estimate the same parameters --- and it is generally limited to count and duration data. The result is that seemingly trivial decisions about which level of data to use can have important consequences for substantive interpretations. We describe the theoretical event process for which results exist, based on time-independence. We also derive a new set of models for a time-dependent process and compare their predictions to those of a commonly used model. Any hope of avoiding the more serious problems of aggregation bias in events data is contingent on first deriving a much wider arsenal of statistical models and theoretical processes that are not constrained by the particular forms of data that happen to be available.

7
Paper
Bayesian Inference for Heterogeneous Event Counts
Martin, Andrew D.

Uploaded 04-20-2000
Keywords hierarchical models
Poisson
event count
heterogeneity
Abstract This paper presents a handful of Bayesian tools one can use to model heterogeneous event counts. In many political science applications we are interested in modeling the number of times a particular event takes place. While models for event count cross-sections are now widely used in political science (King, 1988, 1989b), little has been written about how to model counts when contextual factors introduce heterogeneity. I begin with a discussion of Bayesian cross-sectional count models and introduce an alternative model for counts with overdispersion. To illustrate the Bayesian framework, I model event counts of the number of discharge petitions from the 61st to the 105th House, and the number of women's rights bills cosponsored by each member in the 92nd House. I then generalize the model to allow for contextual heterogeneity and posit a hierarchical Poisson regression model, fitting this model to the number of women rights cosponsorships for each member of the 83rd to 102nd House. I demonstrate the advantages of this approach over pooled and independent Poisson regressions. The hierarchical model allows one to explicitly model contextual factors and test alternative contextual explanations. Additionally, I discuss software one can use to easily implement these models with little start-up cost.

8
Paper
Covariate Functional Form in Cox Models
Keele, Luke

Uploaded 10-25-2005
Keywords Cox model
event history
survival models
splines
semi-parametric
duration models
Abstract In most event history models, the effect of a covariate on the hazard is assumed to have a log-linear functional form. For continuous covariates, this assumption is often violated as the effect is highly nonlinear. Assuming a log-linear functional form when the nonlinear form applies causes specification errors leading to erroneous statistical conclusions. Scholars can, instead of ignoring the presence of nonlinear effects, test for such nonlinearity and incorporate it into the model. I review methods to test for and model nonlinear functional forms for covariates in the Cox model. Testing for such nonlinear effects is important since such nonlinearity can appear as nonproportional hazards, but time varying terms will not correct the misspecification. I investigate the consequences of nonlinear function forms using data on international conflicts from 1950-1985. I demonstrate that the conclusions drawn from this data depend on fitting the correct functional form for the covariates.

9
Paper
Detecting United States Mediation Styles in the Middle East, 1979-1998
Schrodt, Philip A.

Uploaded 03-04-1999
Keywords event data
mediation
Middle East
time series
hidden Markov models
Abstract This research is part of the "Multiple Paths to Knowledge Project" sponsored by the James A. Baker III Institute for Public Policy, Rice University, and the Program in Foreign Policy Decision Making, Texas A&M University. The paper deals with the problem of determining whether the mediation styles used by four U.S. Secretaries of State -- George Schultz, James Baker, Warren Christopher and Madeline Albright -- are sufficiently distinct that they can be detected in event data. The mediation domain is the Israel-Palestinian conflict from April 1979 to December 1998, the event data are coded from the Reuters news service reports using the WEIS event coding scheme, and the classification technique is hidden Markov models. The models are estimated for each of the four Secretaries based on 16 randomly chosen 32-events sequences of USA>ISR and USA>PAL events during the term of the Secretary. Each month in the data set is then assigned to one of the four Secretarial styles based on the best-fitting model. The models differentiate the mediation styles quite distinctly and this method of detecting styles yields quite different results when applied to ISR-PAL data or random data. The "Baker" and "Albright" styles are most distinctive; the "Schultz" style is least; both results are consistent with many qualitative characterizations of these periods. A series of t-tests is then done on Goldstein-scaled scores to determine whether the mediation styles translate into statistically distinct interactions in the ISR>USA, ISR>PAL, PAL>USA and PAL>ISR dyads. While there are a number of statistically-significant differences when the full sample is used, these may be due simply to the overall changes Israel-Palestinian relations over the course of the time series. When tests are done on months that are out-of-term -- in other words, where the style of one Secretary is being employed during the term of another -- few statistically-significant differences are found, though there is someindication of a lag of a month or so between the change in style and the behavioral response. It appears that the effects of the differing styles are not captured by changes in aggregated data, possibly because these scales force behavior into a single conflict-cooperation dimension. Consistent with other papers in the "Multiple Paths to Knowledge" project, the paper contains commentary on how the research project was actually done, as well as the conventional presentation of results. The file includes the papers in Postscript and PDF formats, the event data (Levant, April 1979 to December 1998) used in the analysis, the C source code for estimating the hidden Markov models. This paper was presented at the International Studies Association meetings, Washington, 16-21 February 1999

10
Paper
Inductive Event Data Scaling using Item Response Theory
Schrodt, Philip A.

Uploaded 07-17-2007
Keywords event data
IRT
latent trait
scaling
Rasch model
Goldstein scale
WEIS
CAMEO
Abstract Political event data are frequently converted to an interval-level measurement by assigning a numerical scaled value to each event. All of the existing scaling systems rely on non-replicable expert assessments to determine these numerical scores. This paper uses item response theory (IRT) to derive scales inductively, using event data on Israeli interactions with Lebanon and the Palestinians for 1991-2007. Monthly scores on a latent trait are calculated using three IRT models: the single-parameter Rasch model, and two-parameter models that add discrimination and guessing parameters. The three formulations produce generally comparable scores (correlations of 0.90 or higher). The Rasch scales are less successful than the expert-derived Goldstein scale in reconciling the somewhat divergent sets of events derived from the Agence France Presse and Reuters news services. This is in all likelihood due largely to a low weighting given uses of force by the IRT because such events are common in these two dyads. A factor analysis of the event counts shows that a single cooperation-conflict dimension generally accounts for about two-thirds of the variance in these dyads, but a second case-specific dimension explains another 20%. Finally, moving averages of the derived scores generally correlate well with the Goldstein values, suggesting that IRT may provide a route towards deriving purely inductive, and hence replicable, scales.

11
Paper
The Initiative as a Catalyst for Policy Change
Boehmke, Frederick

Uploaded 03-08-1999
Keywords Initiative
political theory
event history analysis
Abstract In this paper I develop and test a theoretical model of the role that the initiative process plays in shaping policy outcomes. My model builds on Gerber (1996) by introducing uncertainty over the median voter's ideal point and by allowing the interest group to lobby the legislature before a potential initiative is proposed. Successful lobbying may occur due to the uncertainty over the outcome of an initiative. Besides the possibility of lobbying, the results differ from Gerber's since proposal of an initiative is an equilibrium outcome for certain parameter values. I then turn to an event history analysis of state lottery adoptions to test the model's prediction that the initiative process should make it more likely that a state adopt a lottery. This is related to work by Berry and Berry (1990). The empirical hypothesis is found to be supported in the post 1980 period, which I believe is a result of the well-documented resurgence in its use after California's Proposition 13 in 1978. An indirect effect of the initiative in non-initiative states is also found through the importance of neighbors' adoptions. This confirms the view that initiative states are often policy leaders, which I argue may lead to less effective policy choices since they have less information about how to implement then.

12
Paper
Modeling Sample Selection for Durations with Time-Varying Covariates
Boehmke, Frederick

Uploaded 07-02-2008
Keywords selection
selection bias
duration
time-vary covariates
event history
exchange rates
Abstract We extend previous estimators for duration data that suffer from non-random sample selection to allow for time-varying covariates. Rather that a continuous-time duration model, we propose a discrete-time alternative that models the (constant) effects of sample selection at the time of selection across all years of the resulting spell. Properties of the estimator are compared to those of a naive discrete duration model through Monte Carlo analysis and indicate that our estimator outperforms the naive model when selection is non-trivial. We then apply this estimator to the question of the duration of monetary regimes.

13
Paper
Signals, Models, and Congressional Overrides of the Supreme Court
Zorn, Christopher
Hettinger, Virginia

Uploaded 04-05-1999
Keywords event history models
split-population duration models
Congress
Supreme Court
statutory decisions
overrides
Abstract Sparked by interest in game-theoretic representations of the separation of powers, empirical work examining congressional overrides of Supreme Court statutory decisions has burgeoned in recent years. Much of this work has been hampered, however, by the relative rarity of such events; as has long been noted, congressional attention to the Court is limited, and most Court decisions represent the last word on statutory interpretation. With this fact foremost in our minds, we examine empirically a number of theories regarding such reversals. We apply a split-population duration model to the survival of Supreme Court statutory interpretation decisions. This approach allows us to separate the factors which lead to the event itself (i.e., the presence or absence of an override in a particular case) from those which influence the timing of the event. We find that case-specific factors relating to the salience of a case are an important influence in the incidence of overrides, while Congress- and Court-specific political influences dominate the timing at which those overrides occur. By separating the incidence and timing of overrides, our results yield a more accurate and nuanced understanding of this aspect of the separation of powers system.

14
Paper
States as Policy Laboratories: Experimenting with the Children's Health Insurance Program
Volden, Craig

Uploaded 07-09-2003
Keywords diffusion
federalism
dyad-year
event
history
learning
Abstract For more than a decade, scholars of policy diffusion across the states have relied on state-year event history analyses. Such work has been limited by: (1) focusing mainly on neighbor-to-neighbor diffusion paths, rather than other similarities across states; (2) neglecting the role of the success or failures of policies in their diffusion; (3) studying singular specific policy adoptions rather than the choice among policy variants; and (4) setting aside questions about how diffusion mechanisms vary across different policies and different political processes. This paper proposes the alternative approach of dyad-year event history analysis, commonly used in international relations, and applies it to the study of policy diffusion in Children's Health Insurance Program from 1998-2001. This approach uncovers strong evidence of the emulation of states with similar political, demographic, and budgetary characteristics, and those with successful policies. Moreover, the diffusion mechanisms differ substantially across different policy types and political processes.

15
Paper
An Event Data Set for the Arabian/Persian Gulf Region 1979-1997
Schrodt, Philip A.
Gerner, Deborah J.

Uploaded 04-12-1999
Keywords event data
Middle East
Persian Gulf
automated coding
Abstract This paper discusses a WEIS-coded event data set covering the Arabian/Persian Gulf region (Iran, Iraq, Kuwait, Oman, Saudi Arabia, Yemen, and the smaller Gulf states) for the period 15 April 1979 to 10 June 1997. The coded events cover international interactions among these states, as well as interactions with any other states or major international organizations. The data set is generated from Reuters news reports downloaded from the NEXIS data service and coded using the Kansas Event Data System (KEDS) machine-coding program. The paper begins with a review of the process of generating a machine-coded data set, including a discussion of software we have developed to partially automate the development of dictionaries to code new geographical regions. The Gulf data are coded using a standard set of verb phrases (rather than phrases specifically adapted to the Gulf) and an actors dictionary that has been augmented only with the actors identified by a utility program that examines the source texts for actors not already found in the KEDS dictionary. The Reuters reports generate 264,421 events when full stories are coded and 48,721 events when only lead sentences are coded. An examination of the time series that are generated when the events are aggregated by month using the Goldstein scale shows that they capture the major features of the behavior that we know to have occurred in the region. There is generally a high correlation (r > 0.75) between the series generated from lead-sentences and from full stories when the major actors of the region (Iran, Iraq, Saudi Arabia and USA) are studied. An exception to this pattern is found in interactions involving a relatively minor actor, the United Arab Emirates. Here the full-story coding provides far more events than the lead-sentence coding and shows greater variance even for interactions between major actors. We expect this will also be the case for other small Gulf states, suggesting that full-story coding may be necessary for a complete analysis of these actors. Paper was presented a year ago at the International Studies Association, Minneapolis, 18 - 22 March 1998 The file includes the papers in Postscript and PDF formats. The data set has been updated through March, 1999 and is available at the KEDS project web site, http://www.ukans.edu/~keds.

16
Paper
Analyzing the robustness of semi-parametric duration models for the study of repeated events models
Box-Steffensmeier, Janet
Linn, Suzanna
Smidt, Corwin

Uploaded 08-25-2010
Keywords repeated events
event history analysis
Abstract Estimators within the Cox family are often used to estimate models for repeated events. Yet there is much we do not know about the performance of these estimators. In particular, we do not know how they perform given time dependence, different censoring rates, varying number of events experienced, and varying sample sizes. We use Monte Carlo simulations to demonstrate the performance of a variety of popular semi-parametric estimators as these things change and also under conditions of event dependence and heterogeneity, both, or neither. We conclude that the conditional frailty model outperforms other standard estimators under a wide array of data-generating processes and conditions.

17
Paper
Diffusion or Confusion? Modeling Policy Diffusion with Discrete Event History Data
Buckley, Jack

Uploaded 07-24-2002
Keywords policy
diffusion
survival
event
history
Abstract No abstract provided.

18
Paper
Suspension of the Rules in the House Committee Process
Grant, J. Tobin
Hasecke, Edward B.

Uploaded 05-11-1999
Keywords congressional procedures
legislative organizaiton
event history
Abstract Theories of legislative organization offer competing explanations for the existence and practice of committees in Congress. Not only should these theories explain how bills proceed through committees but also why some bills are considered on the floor without formal committee approval. This paper examines the suspension of the rules procedure, the most common means of considering legislation before formal approval by committees. Using data on every public bill that introduced to the House of Representatives during the 105th Congress, we estimate an event history model of the Speaker's use of suspension of the rules. We test the implications of majority dominant and party dominant theories of legislative organization. We find strong support for the party dominant but not for the majority dominant theory. This finding has implications both our understanding of suspension of the rules and of theories of legislative organization.

19
Paper
Automated Production of High-Volume, Near-Real-Time Political Event Data
Schrodt, Philip

Uploaded 08-30-2010
Keywords event data
ICEWS
DARPA
natural language processing
open source
forecasting
prediction
conflict
Abstract This paper summarizes the current state-of-the-art for generating high-volume, near-real-time event data using automated coding methods, based on recent efforts for the DARPA Integrated Crisis Early Warning System (ICEWS) and NSF-funded research. The ICEWS work expanded by more than two orders of magnitude previous automated coding efforts, coding of about 26-million sentences generated from 8-million stories condensed from around 30 gigabytes of text. The actual coding took six minutes. The paper is largely a general ``how-to'' guide to the pragmatic challenges and solutions to various elements of the process of generating event data using automated techniques. It also discusses a number of ways that this could be augmented with existing open-source natural language processing software to generate a third-generation event data coding system.

20
Paper
A Monte Carlo Analysis for Recurrent Events Data
Box-Steffensmeier, Janet M.
De Boef, Suzanna

Uploaded 07-13-2002
Keywords survival analysis
repeated events
heterogeneity
event dependence
simulations
Abstract Scholars have long known that multiple events data, which occur when subjects experience more than one event, cause a problem when analyzed without taking into consideration the correlation among the events. In particular there has not been a solution about the best way to model the common occurrence of repeated events, where the subject experiences the same type of event more than once. Many event history model variations based on the Cox proportional hazards model have been proposed for the analysis of repeated events and it is well known that these models give different results (Clayton 1994; Lin 1994; Gao and Zhou 1997; Klein and Moeschberger 1997; Therneau and Hamilton 1997; Wei and Glidden 1997; Box-Steffensmeier and Zorn 1999; Hosmer and Lemeshow 1999; Kelly and Lim 2000). Our paper focuses on the two main alternatives for modeling repeated events data, variance corrected and frailty (also referred to as random effects) approaches, and examines the consequences these different choices have for understanding the interrelationship between dynamic processes in multivariate models, which will be useful across disciplines. Within political science, the statistical work resulting from this project will help resolve some important theoretical and policy debates about political dynamics, such as the liberal peace, by commenting on the reliability of the different modeling strategies used to test those theories and applying those models. Specifically, the results of the project will help assess whether one of the two primary approaches is better able to account for within-subject correlation. We evaluate the various modeling strategies using Monte Carlo evidence to determine whether and under what conditions alternative modeling strategies for repeated events are appropriate. The question as to the best modeling strategy for repeated events data is an important one. Our understanding of political processes, as in all studies, depends on the quality of the inferences we can draw from our models. There is currently little guidance about which approach or model is appropriate and so, not surprisingly, we see analysts unsure of the best way to analyze their data. Given the dramatic substantive differences that result from using the different models and approaches, this is a problem that will be of interest across research communities.

21
Paper
Getting the Odds Right: Casino Gaming Diffusion, the Initiative Process and Expected Voter Support
Boehmke, Frederick

Uploaded 10-27-1999
Keywords Initiative
direct democracy
interest groups
lobbying
event history
casino gaming
formal model
Abstract In this paper I develop and test a formal model of the role that the initiative process plays in shaping policy outcomes. By introducing uncertainty over the median voter's ideal point, I show that the possibility of proposing an initiative makes it cheaper for an interest group to change policy, both through the initiative process and by using contributions to convince the legislature to change policy. By also allowing groups to use information drawn from neighboring states' adoptions to estimate the probability an initiative would pass, I show that policy diffusion should function primarily as information diffusion between initiative states. The predictions of the model are then tested through an event history analysis of state casino gaming legalizations. The initiative process is found to be a positive and significant predictor of adoption and its effect is increased by voter liberalism. There is strong support for the informational approach to diffusion with a positive flow between initiative states and none from or to non-initiative states.

22
Paper
Conflict and Mediation Event Observations (CAMEO): A New Event Data Framework for the Analysis of Foreign Policy Interactions
Schrodt, Philip A.
Gerner, Deborah J.
Abu-Jabr, Rajaa
Yilmaz, Omur

Uploaded 04-01-2002
Keywords event data
mediation
WEIS
Middle East
Balkans
West Africa
Abstract The Conflict and Mediation Events Observations (CAMEO) framework is a new event data coding scheme optimized for the study of third-party mediation in international disputes. We have developed and implemented this system using the TABARI automated coding program, and have generated data sets for the Balkans (1989-2002; N=69,620), Levant (1979-2002; N=146,283), and West Africa (1989-2002; N=17,468) from Reuters and Agence France Presse reports. We describe why we decided to develop a new coding system, rather than continuing to use the World Events Interaction Survey (WEIS) framework that we have used in earlier work. Our decision involved both known weaknesses in the WEIS system, and some additional problems that we have found occur when WEIS is coded using automated methods. We have addressed these problems in constructing CAMEO, as well as producing much more completed documentation than has been available for WEIS. In this paper, we make several statistical comparisons of CAMEO-coded and WEIS-coded data in the three geographical regions. When the data are aggregated to a general behavioral level—verbal cooperation, material cooperation, verbal conflict and material conflict—most of the data sets show a high correlation (r>0.90) in the number of WEIS and CAMEO events coded per month. However, as we expected, CAMEO consistently picks up a greater number of events involving material cooperation. CAMEO and WEIS show similar irregularities in the distribution of events by category. Finally, there is a very significant correlation (r>0.57) between the count of CAMEO events specifically dealing with mediation and negotiation, and a pattern-based measure of mediation we developed earlier from WEIS data. Appendices in the paper show the CAMEO coding framework and examples from the codebook.

23
Paper
The In-and-Outers Revisited: Duration Analysis and Presidential Appointee Tenure
Tomlinson, Andrew R.
Anderson, William D.

Uploaded 11-09-1999
Keywords presidency
staff
event history
duration
competing risks
Abstract Much has been written about the "fraying" of the Presidential appointments system (NAPA, 1985), but little research has been conducted which takes advantage of recent advancements in event history modeling. The questions posed by many authors in this field focus on two variables -- the time someone spends in office (Joyce, 1990), and the reasons they give for leaving their job (Bonafede, 1987). Not only do event history models avoid the common pitfalls of using OLS to model temporal data (Box-Steffensmeier and Jones, 2000), but certain models allow the researcher to combine temporal data with categorical choice models. We run a simple version of a competing risks model to test the effects of theoretically-chosen covariates on the likelihood of presidential appointees leaving office at a given time. We find support for some hypotheses, specifically those dealing with stress related factors and financial pressures on staffer tenure.

24
Paper
First Do No Harm: The Risks of Modeling Temporal Dependence
Dafoe, Allan

Uploaded 07-17-2013
Keywords temporal dependence
repeated events
event history models
lagged dependent variable
war
Abstract Scholars analyzing repeated-events time-series cross-sectional (TSCS) data for causal inference routinely employ event history models or temporal control variables to address temporal dependence. These methods condition on functions of lags of the outcome, f(LY), to improve causal inference. The appropriate use of such techniques for causal inference rely on assumptions about the data-generating process. The first set of assumptions concerns how one should control for temporal dependence: the functional form, f(LY), must be correctly specified. I examine the study of interstate conflict, showing some ways in which f(LY) is misspecified, and offer suggestions for improving it. The second, deeper, set of assumptions concerns whether one should control for temporal dependence: depending on the cause of temporal dependence, conditioning on f(LY) can worsen estimates. Through the analysis of non-parametric causal graphs and simulations I show that conditioning on f(LY) can reduce bias when temporal dependence arises solely from event dependence, can induce bias when there are unobserved persistent causes of the outcome, and will have ambiguous effects when both causes are present. I present results about the direction of the expected bias, including a bounding result specifying the conditions under which estimates with and without temporal controls will bound the truth. Outside of the experimental ideal, clear causal inference depends on substantive assumptions. Absent strong beliefs about the DGP which justify temporal controls, or a measurable exhaustive un-confounded mechanism generating the temporal dependence, I recommend that scholars routinely report estimates with and without temporal controls; sensitivity to temporal specification implies that a result can not be understood without a better understanding of the temporal dynamics of the phenomenon of interest.

25
Paper
State and American Indian Negotiation of Gaming Compacts: An Event Count Analysis
Boehmke, Frederick
Witner, Richard

Uploaded 01-23-2002
Keywords american indian
gaming
event count
policy adoption
Abstract There has been a proliferation of casino-style Indian gaming in the years since the passage of the Indian Gaming Regulatory Act in 1988. Yet little is known about the factors that influence state and Indian nationsí decisions to enter into gaming compacts. In this paper we seek to achieve two objectives. First, we seek to understand the expansion of Indian-state gaming compacts by studying how characteristics of states and Indian nations, along with spatial and temporal diffusion, affect the number of compacts negotiated. Most importantly, we focus on Indian nationís relationships with the states; their political influence with respect to the state and the contact they have with state government. Second, we introduce an empirical model new to the study of state politics by modeling the compacting process between Indian nations and states as an event count process. The event count model allows us to explain why some states have more Indian gaming than others and how the compacting process has evolved over time.

26
Paper
Modeling Time Series Count Data: A State-Space Approach to Event Counts
Brandt, Patrick T.
Williams, John T.
Fordham, Benjamin

Uploaded 07-08-1998
Keywords Poisson models
event counts
state-space models
Kalman filter
non-normal time series
Abstract This is a revised version, dated July 16, 1998. Time series count data is prevalent in political science. We argue that political scientists should employ time series methods to analyze time series count data. A simple state-space model is presented that extends the Kalman filter to count data. The properties of this model are outlined and further evaluated by a Monte Carlo study. We then show how time series of counts present special problems by turning to two replications: the number of hospital deaths that are the subject of a recent criminal court case, and Pollins (1996) MIDs data from international relations.

27
Paper
Stochastic Dependence in Competing Risks
Gordon, Sanford C.

Uploaded 09-05-2001
Keywords Competing risks
duration models
survival models
event history
random effects
frailty models
unobserved heterogeneity
Monte Carlo simulation
Congress
legislative position-taking
cabinet survival
numeric integration
Markov Chain Monte Carlo
Abstract The term "Competing Risks" describes duration models in which spells may terminate via multiple outcomes: The term of a cabinet, for example, may end with or without an election; wars persist until the loss or victory of the aggressor. Analysts typically assume stochastic independence among risks, the duration modeling equivalent of independence of irrelevant alternatives. However, many political examples violate this assumption. I review competing risks as a latent variables approach. After discussing methods for modeling dependence that place restrictions on the nature of association, I introduce a parametric generalized dependent risks model in which inter-risk correlation may be estimated and its significance tested. The method employs risk-specific random effects drawn from a multivariate normal distribution. Estimation is conducted using numerical methods and/or Bayesian simulation. Monte Carlo simulation reveals desirable large sample properties of the estimator. Finally, I examine two applications using data on cabinet survival and legislative position taking.

28
Paper
Evaluating Zero-Inflated and Hurdle Poisson Specifications
Zorn, Christopher

Uploaded 00-00-0000
Keywords event count
overdispersion
dual regime
zero-inflated
hurdle
Abstract This paper examines two alternative specifications for estimating event count models in which the data generating process results in a larger number of zero counts than would be expected under standard distributional assumptions. I compare King's "hurdle" event count model and Greene's "zero-inflated" Poisson model, using data on Congressional responses to Supreme Court decisions from 1979 to 1988. I show that each of these models is a special case of a more general dual regime data generating process which results in extra-Poisson zero counts. Furthermore, because this data generating process can produce overdispersion in its own right, these models are also shown to be related to "variance function" negative binomial specifications. The underlying correspondence between these models leads to similar results in estimating and interpreting them in practice.

29
Paper
Analyzing the dynamics of international mediation processes
Schrodt, Philip A.
Gerner, Deborah J.

Uploaded 07-16-2001
Keywords event data
cross-correlation
mediation
Cox proportional hazard
pattern recognition
Abstract This paper presents initial results from a project that will formally test a number of the hypotheses embedded in the theoretical and qualitative literatures on mediation, using automated coding of event data from news-wire sources. In contrast to most of the existing quantitative literature, which emphasizes the structural aspects of mediation, we will focus on the dynamics. The initial part of the paper focuses on two issues of design. First, we discuss the advantages of generating data using fully automated methods, which increases the transparency and replicability of the research. This transparency is extended to the development of more complex variables that cannot be captured as single events: these are defined as pattern of the underlying event data. We also suggest that these can be usefully studied using conventional inferential statistics rather than computational pattern recognition. Second, we justify the "statistical case study" approach which focuses on a small number of cases that are limited in geographical and temporal scope. While the risk of this approach is that one will find patterns of behavior that apply only in those circumstances, we point out that the more conventional large-N time-series cross-sectional studies also carry inferential risks. The statistical tests reported in this paper look at three different issues using data on the Israel-Lebanon and Israel-Palestinian conflicts in the Levant (1979-1999), and the Serbia-Croatia and Serbia-Bosnia conflicts in the Balkans (1991-1999). First, cross- correlation is used to look at the effects of mediation on the level of violence over time. Second, we test the "sticks-or-carrots" hypothesis on whether mediation is more effective in reducing violence if accompanied by cooperative or conflictual behavior by the mediator. Finally, we estimate Cox proportional hazard models to assess the factors that influence (1) whether mediation is accepted by the parties in a conflict, (2) whether formal agreements are reached, and (3) whether the agreements reduce the level of conflict. Future work in the project involves development of a new event coding scheme specifically designed for the study of mediation, and expansion of the list of cases to include other mediated conflicts in the Middle East and West Africa.

30
Paper
Modelling Space and Time: The Event History Approach
Beck, Nathaniel

Uploaded 08-22-1996
Keywords duration analysis
event history analysis
time-series--cross-section data
discrete duration data
duration dependence
Abstract This is an elementary exposition of duration modelling prepared for a volume in celebration of the 30th anniversary of the Essex Summer School (Research Strategies in the Social Sciences, Elinor Scarbrough and Eric Tanenbaum, editors). The approach is non-mathematical. The running example used is the King et al. model of cabinet durations with particular attention paid to detecting and interpreting duration dependence in that model. There is some new discussion of ascertaining duration dependence using discrete methods and the relationship between discrete duration data and binary time-series--cross-section data.

31
Paper
Automated Coding of International Event Data Using Sparse Parsing Techniques
Schrodt, Philip A.

Uploaded 06-28-2001
Keywords event data
natural language processing
conflict
content analysis
open source
Abstract "Event data" record the interactions of political actors reported in sources such as newspapers and news services; this type of data is widely used in research in international relations. Over the past ten years, there has been a shift from coding event data by humans -- typically university students -- to using computerized coding. The automated methods are dramatically faster, enabling data sets to be coded in real time, and provide far greater transparency and consistency than human coding. This paper reviews the experience of the Kansas Event Data System (KEDS) project in developing automated coding using "sparse parsing" machine coding methods, discusses a number of design decisions that were made in creating the program, and assesses features that would improve the effectiveness of these programs.

32
Paper
Using Cluster Analysis to Derive Early Warning Indicators for Political Change in the Middle East, 1979-1996
Schrodt, Philip A.
Gerner, Deborah J.

Uploaded 08-22-1996
Keywords event data
conflict
early warning
Middle East
cluster analysis
genetic algorithms
Abstract This paper uses event data to develop an early warning model of major political changes in the Levant for the period April 1979 to July 1996. Following a general review of statistical early warning research, the analysis focuses on the behavior of eight Middle Eastern actors—Egypt, Israel, Jordan, Lebanon, the Palestinians, Syria, the United States and USSR/Russia—using WEIS-coded event data generated from Reuters news service lead sentences with the KEDS machine-coding system. The analysis extends earlier work (Schrodt and Gerner 1995) demonstrating that clusters of behavior identified by conventional statistical methods correspond well with changes in political behavior identified a priori. We employ a new clustering algorithm that uses the correlation between the dyadic behaviors at two points in time as a measure of distance, and identifies cluster breaks as those time points that are closer to later points than to preceding points. We also demonstrate that these data clusters begin to "stretch" prior to breaking apart; this characteristic is used as an early-warning indicator. A Monte- Carlo analysis shows that the clustering and early warning measures perform very differently in simulated data sets having the same mean, variance, and autocorrelation as the observed data (but no cross-correlation) which reduces the likelihood that the clustering patterns are due to chance. The initial analysis uses Goldstein's (1992) weighting system to aggregate the WEIS-coded data. In an attempt to improve on the Goldstein scale, we use a genetic algorithm to optimize the weighting of the WEIS event categories for the purpose of clustering. This does not prove very successful and only differentiates clusters in the first half of the data set, a result similar to one we obtained using the cross-sectional K- Means clustering procedure. Correlating the frequency of events in the twenty-two 2-digit WEIS categories, on the other hand, gives clustering and early warning results similar to those produced by the Goldstein scale. The paper concludes with some general remarks on the role of quantitative early warning and directions for further research. This paper was presented at the American Political Science Association, San Francisco, 28 August - 1 September 1996

33
Paper
Monitoring conflict using automated coding of newswire reports
Schrodt, Philip A.
Gerner, Deborah J.
Simpson Gerner, Erin M.

Uploaded 06-28-2001
Keywords event data
natural language processing
conflict
content
Abstract his paper discusses the experience of the Kansas Event Data System (KEDS) project in developing event data sets for monitoring conflict levels in five geographical areas: the Levant (Arab-Israeli conflict), Persian Gulf, former Yugoslavia, Central Asia (Afghanistan, Armenia-Azerbijan, former Soviet republics), and West Africa (Liberia, Sierra Leone). These data sets were coded from commercial news sources using the KEDS and TABARI automated coding systems. The paper discusses our experience in developing the dictionaries required for this coding, the problems with the number of reported events in the various areas, and provides examples of the statistical summaries that can be produced from event data. We also compare the coverage of the Reuters and Agence France Presse news services for selected years in the Levant and former Yugoslavia. We conclude with suggestions for four topics where additional efforts that could be usefully undertaken by multiple research projects.

34
Paper
Early Warning of Conflict in Southern Lebanon using Hidden Markov Models
Schrodt, Philip A.

Uploaded 08-24-1997
Keywords hidden Markov models
event data
early warning
international crisis
sequence analysis
Middle East
WEIS
BCOW
Abstract This paper extends earlier work on the application of hidden Markov models (HMMs) to the problem of forecasting international conflict. HMMs are a sequence comparison method widely used in computerized speech recognition as a computationally efficient method of generalizing a set of sequences observed in a noisy environment. The technique is easily be adapted to work with sequences of international event data. The paper provides a theoretical "micro-foundation" for the use of sequence comparison in conflict early- warning based on coadaptation of organizational standard operating procedures. The left-right (LR) HMM used in speech recognition is first extended to a left-right-left (LRL) model that allows a crisis to escalate and de-escalate. This model is tested for its ability to correctly discriminate between BCOW crisis that involve and do not involve war. The LRL model provides slightly more accurate classification than the LR model. The interpretation of the hidden states in the LRL models, however, is more ambiguous than in the LR model. The HMM is then applied to the problem of forecasting the outbreak of armed violence between Israel and Arab forces in south Lebanon during the period 1979 to 1997 (excluding 1982-1985). An HMM is estimated using six cases of "tit-for-tat" escalation, then fitted to the entire time period. The model identifies about half of the TFT conflicts—including all of the training cases—that occur in the full sequence, with only one false positive. This result suggests that HMMs could be used in an event-based monitoring system. However, the fit of the model is very sensitive to the number of days in a sequence when no events occurred, and consequently the fit measure is ineffective as an early warning indicator. Nonetheless, in a subset of models, the maximum likelihood estimate of the sequence of hidden Markov states provides a robust early warning indicator with a three to six-month lead. These models are valid in a split-sample test, and the patterns of cross-correlation of the individual states of the model are consistent with the theoretical expectations. While this approach clearly needs further validation, it appears promising. The paper concludes with observations on the extent to which the HMM approach can be generalized to other categories of conflict, some suggestions on how the method of estimation can be improved, and the implications that sequence-based forecasting techniques have for theories of the causes of conflict.

35
Poster
Robust Estimation of the Cox Proportional Hazards Model
Harden, Jeff

Uploaded 07-17-2010
Keywords Event History Modeling
Cox Proportional Hazards Model
Partial Likelihood Maximization
Iteratively-Reweighted Robust Estimation
Cross-Validation
Abstract The Cox proportional hazards model is often used with time-to-event data in political science. However, misspecification issues such as measurement error or omitted covariates can cause substantial coefficient bias when it is estimated via the conventional Partial Likelihood Maximization (PLM). Here we review an iteratively-reweighted robust (IRR) estimator of the Cox model that is proven to reduce this bias under such conditions and propose a cross-validated median fit (CVMF) test to select between PLM and IRR. Then we apply the test to data in political science. We consider several typologies of replications with respect to (1) the test's selection (PLM or IRR) and (2) the implications of IRR for the original hypotheses (less support, more support, or mixed results). Overall, we demonstrate that PLM and IRR can each be optimal, that substantive conclusions can depend on which one is used, and that the CVMF test is effective in choosing between them.

36
Poster
The Past is Ever-Present: The Dynamic Nature of Intrastate Conflict
Jones, Benjamin

Uploaded 07-28-2012
Keywords Event history
Survival models
Multi-state models
Civil war
Path dependence
Abstract Civil wars pose a grave challenge to international stability as they tend to recur frequently over time. Nevertheless, existing theory treats civil wars as independent events. I reconceptualize civil war as a dynamic process, which creates a new statistical challenge ‚Äď modeling multi-stage processes through a series of transitions within a longitudinal process. To overcome this problem, I introduce a multi-state event history model, which models the entire civil war process as a series of successive stages in which previous outcomes shape subsequent events, and apply it to a dataset of all civil wars from 1950-2004. The results provide strong evidence that previous outcomes exert both a direct, and indirect effect on subsequent transitions, revealing the conditional nature of factors frequently associated with war and peace.


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