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Below results based on the criteria 'time series'
Total number of records returned: 49

Taking the State Space Seriously: The Dynamic Linear Model and Bayesian Time Series Analysis
Buckley, Jack

Uploaded 08-02-2002
Keywords time series
state space
think tanks
Abstract No abstract submitted.

Nuisance vs. Substance: Specifying and Estimating Time-Series--Cross-Section Model
Beck, Nathaniel
Katz, Jonathan

Uploaded 01-01-1995
Keywords Econometrics
Robust standard errors
Abstract In a previous article we showed that ordinary least squares with panel corrected standard errors is superior to the Parks generalized least squares approach to the estimation of time-series--cross-section models. In this article we compare our proposed method to another leading technique, Kmenta's ``cross-sectionally heteroskedastic and timewise autocorrelated'' model. This estimator uses generalized least squares to correct for both panel heteroskedasticity and temporally correlated errors. We argue that it is best to model dynamics via a lagged dependent variable, rather than via serially correlated errors. The lagged dependent variable approach makes it easier for researchers to examine dynamics and allows for natural generalizations in a manner that the serially correlated errors approach does not. We also show that the generalized least squares correction for panel heteroskedasticity is, in general, no improvement over ordinary least squares and is, in the presence of parameter heterogeneity, inferior to it. In the conclusion we present a unified method for analyzing time-series--cross-section data.

Spatio-Temporal Models for Political-Science Panel and Time-Series-Cross-Section Data
Franzese, Robert
Hays, Jude

Uploaded 07-18-2006
Keywords Spatial Econometrics
Spatial-Lag Model
Spatio-Temporal Model
Panel Data
Time-Series-Cross-Section Data
Spatio-Temporal Multiplier
Spatio-Temporal Dynamics
Spatio-Temporal Steady-State Effects
Abstract 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.

Time Series Cross-Sectional Analyses with Different Explanatory Variables in Each Cross-Section
Girosi, Federico
King, Gary

Uploaded 07-11-2001
Keywords Bayesian hierarchical model
time series
Abstract The current animosity between quantitative cross-national comparativists and area studies scholars originated in the expanding geographic scope of data collection in the 1960s. As quantitative scholars sought to include more countries in their regressions, the measures they were able to find for all observations became less comparable, and those which were available (or appropriate) for fewer than the full set were excluded. Area studies scholars appropriately complain about the violence these procedures do to the political reality they find from their in depth analyses of individual countries, but as quantitative comparativists continue to seek systematic comparisons, the conflict continues. We attempt to eliminate a small piece of the basis of this conflict by developing models that enable comparativists to include different explanatory variables, or the same variables with different meanings, in the time-series regression in each country. This should permit more powerful statistical analyses and encourage more context-sensitive data collection strategies. We demonstrate the advantages of this approach in practice by showing how out-of-sample forecasts of mortality rates in 25 countries, 17 age groups, and 17 causes of death in males and 20 in females from this model out-perform a standard regression approach.

Unit Roots and Causal Inference in Political Science
Freeman, John R.
Williams, John T.
Houser, Daniel
Kellstedt, Paul

Uploaded 01-01-1995
Keywords Time series
unit roots
Abstract In the 1980s political scientists were introduced to vector autoregression (Sims, 1980). In the years that followed, they used this method to evaluate competing theories (Goldstein and Freeman, 1990, 199l; Freeman and Alt, 1994; Williams, 1990) and to test the validity of the restrictions in their regression models (MacKuen, Erikson, and Stimson, 1992). In the process, important empirical anomalies came to light. At about this same time, econometricians identified and began to evaluate the problems which unit roots and cointegration produced in vector autoregression and related time series methods. These problems had to do with nothing less than the validity of Granger causality tests and other inferential tools which are the heart of the approach. This research was important because econometricians had discovered years before that many economic time series are first-order integrated (Nelson and Plosser, 1982). Studying the trend properties of economic time series therefore is considered essential in time series econometrics. Recently political scientists (Ostrom and Smith, 1993; Durr, 1993) have argued that certain political time series contain unit roots as well. Yet, to date, no political scientist has made any such demonstration, let alone explained what should be done to put our results on sounder footings if, in fact, our level VARs are faulty. This is the purpose of this paper. In it, we explain the problems which unit roots and cointegration produce in level VARs--why it is so important to take into account the trend properties of one's data. We then review several approaches to solving these problems. One of these approaches, Phillips's (1995) Fully Modified Vector Autoregression (FM-VAR) is singled out for closer study. The theoretical nature of FM-VAR is briefly explained and some practical difficulties in implementing the associated estimation techniques and hypothesis tests are discussed. Finally, the usefulness of FM-VAR is explored in several analyses which parallel the main uses of level VARs mentioned above. These are a stylized Monte Carlo analysis; a reanalysis of Freeman's (1983) study of arms races; a retest of the specifications of MacKuen, Erikson, and Stimson's (1992) model of approval; and a reexamination of the exogeneity-of-vote intentions anomaly in Freeman, Williams and Lin's (1989) study of British government spending.

Bayesian Analysis of Structural Changes: Historical Changes in US Presidential Uses of Force Abroad
Park, Jong Hee

Uploaded 07-16-2007
Keywords structural changes
changepoint models
discrete time series data
use of force data
state space models
time-varying parameter models
Bayesian inference
Abstract While many theoretical models in political science are inspired by structural changes in politics, most empirical methods assume stable patterns of causal processes and fail to capture dynamic changes in theoretical relationships. In this paper, I introduce an efficient Bayesian approach to the multiple changepoint problem presented by Chib (1998) and discuss the utility of the Bayesian changepoint models in the context of generalized linear models. As an illustration, I revisit the debate over whether and how U.S. presidents have used forces abroad in response to domestic factors since 1890.

Zen and the Art of Policy Analysis: A Response to Nielsen and Wolf
Meier, Kenneth J.
Eller, Warren
Wrinkle, Robert D.
Polinard, J. L.

Uploaded 03-12-2001
Keywords education
pooled time series
Abstract Neilsen and Wolf (N.d.) lodge several criticism of Meier, Wrinkle and Polinard (1999). Although most of the criticisms deal with tangential issues rather than our core argument, their criticisms are flawed by misguided estimation strategies, erroneous results, and an inattention to existing theory and scholarship. Our re-analysis of their work demonstrates these problems and presents even stronger evidence for our initial conclusion–both minority and Anglo students perform better in schools with more minority teachers.

Macropartisanship: A Replication and Critique
Palmquist, Bradley
Green, Donald P.
Schickler, Eric

Uploaded 07-11-1996
Keywords partisanship
presidential approval
time series
Abstract This paper reevaluates the thesis of MacKuen, Erikson, and Stimson (1989, 1992) that aggregate party identification balance (macropartisanship) shifts significantly over short periods of time in response to changes in presidential popularity and consumer sentiment. The data originally used by MacKuen, et al. were a sample of the complete set of Gallup polls available from 1953 to 1988. Because their data are no longer extant, and to make use of more information, we analyze party id data from 677 personal and 305 telephone Gallup polls, aggregated quarterly from 1953 to 1995. Comparisons are also made with analyses from CBS/New York Times data. As well as attempting to replicate the MacKuen, et al. results (an attempt which is not entirely successful, perhaps because of the data differences), we develop a more flexible and parsimonious time series model linking approval, consumer sentiment, and macropartisanship. The estimates obtained lead to the conclusion that macropartisanship adjusts to short-term shocks in a limited and gradual fashion. These shifts are not large enough to call into question the traditional views of realignment and the stabilizing role that party identification plays in a party system.

Path, Phat, and State Dependence in Observation-driven Markov
Walker, Robert

Uploaded 07-17-2007
Keywords Markov models
qualitative time series
ergodic theorem
Abstract Many social science theories posit dynamics that depend in important ways on the present state and focus on a reasonably small number of states. Despite the importance of theoretical notions of path dependence, empirical models, with a few exceptions (Alvarez, Cheibub, Limongi and Przewroski 2000; Epstein, Bates, Goldstein, O'Halloryn, and Kristensen 2006; Beck. Jackman, Epstein, and O'Halloryn 2001), have paid little attention to the implications of state dependence for empirical studies. This despite the fact that there are many possible ways in which history might matter -- we focus on the categorization given by Page (2006) -- and these different ways that history might matter manifest themselves in sets of models that can be tested and compared. This paper considers the basic properties of observation-driven Markov chains [stationarity/time homogeneity, communication, transience, periodicity, irreducibility, and ergodicity] and the issues that arise in their implementation as likelihood estimators to provide a window into methods for the study of path dependence. Application of these concepts to longitudinal data on human rights abuses and exchange rate regime transitions provides evidence that history may also not exert uniform effects. The empirical examples highlight the subtle substantive assumptions that manifest in different modeling choices. The human rights example calls for an important qualification in the widely studied relationship between democracy and human rights abuses. The exchange rate regime example highlights the usefulness of Markov models for multinomial processes.

Aggregation and Dynamics of Survey Responses: The Case of Presidential Approval
Alvarez, R. Michael
Katz, Jonathan

Uploaded 10-01-2001
Keywords presidential approval
fractional integration
Abstract In this paper we critique much of the empirical literature on the important political science concept of presidential approval. We first argue that dynamics attributed to the aggregate presidential approval series are often logically inconsistent and always substantively implausible. In particular, we show that is no way for a bounded series, such as the approval series, to be integrated. However, even in non-integrated models often lead to implausible substantive findings due to aggregation both across Presidential administrations and from models of individual level behavior to aggregate survey marginals. We argue that using individual-level survey responses is superior for methodological and theoretical reasons, and we provide an example of such an analysis using Gallup Organization survey data.

Measurement Models for Time Series Analysis: Estimating Dynamic Linear Errors in Variables
McAvoy, Gregory

Uploaded 07-12-1999
Keywords measurement models
time series analysis
Rolling Thunder
Abstract This paper uses state space modelling and Kalman filtering to estimate a dynamic linear errors-in-variables model with random measurement error in both the dependent and independent variables. I begin with a general description of the dynamic errors-in-variables model, translate it into state space form, and show how it can be estimated via the Kalman filter. I then use the model in a substantive example to examine the effects of aggregate partisanship on evaluations of President Reagan's job performance using data from the 1984 primary campaign and compare the OLS estimates for this example to those derived from maximum likelihood estimates of the dynamic shock-error setup. Next, I report the results of a simulation in which the amount of random measurement error is varied and, thus, demonstrate the importance of estimating measurement error models and the superiority that Kalman filtering has over regression. Finally, I estimate a dynamic linear errors-in-variables model using multiple indicators for the latent variables.

Causal Inference of Repeated Observations: A Synthesis of the Propensity Score Methods and Multilevel Modeling
Su, Yu-Sung

Uploaded 07-03-2008
Keywords causal inference
balancing score
multilevel modeling
propensity score
time-series-cross-sectional data
Abstract The fundamental problem of causal inference is that an individual cannot be simultaneously observed in both the treatment and control states (Holland 1986). The propensity score methods that compare the treatment and control groups by discarding the unmatched units are now widely used to deal with this problem. In some situations, however, it is possible to observe the same individual or unit of observation in the treatment and control states at different points in time. The data has the structure that is often refer to as time-series-cross-sectional (TSCS) data. While multilevel modeling is often applied to analyze TSCS data, this paper proposes that synthesizing the propensity score methods and multilevel modeling is preferable. The paper conducts a Monte Carlo simulation with 36 different scenarios to test the performance of the two combined methods. The result shows that synthesizing the propensity score matching with multilevel modeling performs better in that such method yields less biased and more efficient estimates. An empirical case study that reexamine the model of Przeworksi et al (2000) on democratization and development also shows the advantage of this synthesis.

Why Lagged Dependent Variables Can Suppress the Explanatory Power of Other Independent Variables
Achen, Christopher H.

Uploaded 07-14-2000
Keywords time series
serial correlation
arms races
Abstract In many time series applications in the social sciences, lagged dependent variables have no obvious causal interpretation, and researchers omit them. When they are left out, the other coefficients take on sensible values. However, when an autoregressive term is put in ``as a control,'' it often acquires a large, statistically significant coefficient and improves the fit dramatically, while many or all of the remaining substantive coefficients collapse to implausibly small and insignificant values. Occasionally, the substantive variables even take on the wrong sign. This paper explains why this phenomenon occurs and how the resulting confusions have often misled researchers into inaccurate inferences. The standard findings that government budgets are caused primarily by past budgets and that arms races are driven mainly by domestic forces are shown to be likely statistical artifacts. Applications are made to vector autoregressions, error-correction models, and panel studies.

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.

Beyond "Fixed Versus Random Effects": A Framework for Improving Substantive and Statistical Analysis of Panel, TSCS, and Multilevel Data
Bartels, Brandon

Uploaded 09-30-2008
Keywords random effects
fixed effects
time-series cross-sectional data
panel data
multilevel modeling
Abstract Researchers analyzing panel, time-series cross-sectional, and multilevel data often choose between a random effects, fixed effects, or complete pooling modeling approach. While pros and cons exist for each approach, I contend that some core issues concerning clustered data continue to be ignored. I present a unified and simple modeling framework for analyzing clustered data that solves many of the substantive and statistical problems inherent in extant approaches. The approach: (1) solves the substantive interpretation problems associated with cluster confounding, which occurs when one assumes that within- and between-cluster effects are equal; (2) accounts for cluster-level unobserved heterogeneity via a random intercept model; (3) satisfies the controversial statistical assumption that level-1 variables be uncorrelated with the random effects term; (4) allows for the inclusion of level-2 variables; and (5) allows for statistical tests of cluster confounding. I illustrate this approach using three substantive examples: global human rights abuse, oil production for OPEC countries, and Senate voting on Supreme Court nominations. Reexaminations of these data produce refined interpretations of some of the core substantive conclusions.

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

Uploaded 03-04-1999
Keywords event data
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

Conditional Party Government And Member Turnout On Senate Recorded Votes, 1873-1935
Sala, Brian R.
Forgette, Richard

Uploaded 12-29-1997
Keywords rational voter model
time series
roll-call voting
Abstract According to the conditional party government thesis, party members bond or precommit themselves to supporting "party" positions under certain circumstances. A test of this thesis asks whether party members are more likely to participate in a roll call vote when the question has been identified by party leaders as important to the party. We show that party leadership signals systematically affected member turnout levels in the U.S. Senate during 1873-1935. On average, two-party turnout on party-salient votes rose by more than five members during 1873-1923 and more than three members during 1923-35 relative to "non-salient" votes. These results also provide evidence of cohesive partisan behavior in the Senate well before the parties began the regular practice of designating floor leaders and whips.

Sweeping fewer things under the rug: tis often (usually?) better to model than be robust
Beck, Nathaniel

Uploaded 07-16-2012
Keywords Cluster Robust Standard Errors
Moulton Problem
Time Series Cross Section Data
Difference in Difference
Random Effects
Abstract The use of ``robust'' standard errors is now commonplace in political science. This paper considers one such type of errors, those that are robust to clustering of the data. While these give accurate estimates of parameter variability, we often can do better by direct modeling of the clustering process; such modeling can give insight into important sources of cluster effects. Applications are to grouped data with group level variables, difference in difference designs and time-series--cross-section data. Analysts should always ask whether clustering can be no more than an estimation nuisance before simply resorting to cluster robust standard errors.

Time Series Models for Compositional Data
Brandt, Patrick T.
Monroe, Burt L.
Williams, John T.

Uploaded 07-09-1999
Keywords compositional data
time series analysis
Monte Carlo simulation
Abstract Who gets what? When? How? Data that tell us who got what are compositional data - they are proportions that sum to one. Political science is, unsurprisingly, replete with examples: vote shares, seat shares, budget shares, survey marginals, and so on. Data that also tell us when and how are compositional time series data. Standard time series models are often used, to detrimental consequence, to model compositional time series. We examine methods for modeling compositional data generating processes using vector autoregression (VAR). We then use such a method to reanalyze aggregate partisanship in the United States.

The Reciprocal Relationship Between State Defense Interest and Committee Representation in Congress
Carsey, Thomas
Rundquist, Barry

Uploaded 11-04-1997
Keywords Distributive Politics
Pooled Time Series
Abstract Does prior representation of a state on a Congressional defense committee lead to higher levels of per capita defense contract, or do higher levels of prior per capita contract awards to a state increase its probability of being represented on a defense committee? To solve this puzzle, we estimate a cross-lagged three-equation model on data from all 50 states from 1963 to 1989 using maximum likelihood within LISREL. We find a substantial reciprocal but non-confounding relationship between representation and the allocation of benefits for the House, but not for the Senate. Thus, for the House, this more appropriate model of distributive politics in Congress supports both the committee-induced benefits hypothesis and the recruitment hypothesis. Further, the paper elaborates on how this reciprocal relationship plays out over time.

Learning in Campaigns: A Policy Moderating Model of Individual Contributions to House Candidates
Wand, Jonathan
Mebane, Walter R.

Uploaded 04-18-1999
Keywords FEC
campaign contributions
campaign finance
policy moderation
generalized linear model
negative binomial
time series
U.S. House of Representatives
1984 election
Abstract We propose a policy moderating model of individual campaign contributions to House campaigns. Based on a model that implies moderating behavior by voters, we hypothesize that individuals use expectations about the Presidential election outcome when deciding whether to donate money to a House candidate. Using daily campaign contributions data drawn from the FEC Itemized Contributions files for 1984, we estimate a generalized linear model for count data with serially correlated errors. We expand on previous empirical applications of this type of model by comparing standard errors derived from a sandwich estimator to confidence intervals produced by a nonparametric bootstrap.

Beyond Ordinary Logit: Taking Time Seriously in Binary Time-Series--Cross-Section Models
Beck, Nathaniel
Katz, Jonathan
Tucker, Richard

Uploaded 08-22-1997
Keywords binary time-series--cross-section data
temporal dependence
grouped duration models
complementary log-log
cubic spline
economic interdependence
democratic peace
Abstract Researchers typically analyze time-series--cross-section data with a binary dependent variable (BTSCS) using ordinary logit or probit. However, BTSCS observations are likely to violate the independence assumption of the ordinary logit or probit statistical model. It is well known that if the observations are temporally related that the results of an ordinary logit or probit analysis may be misleading. In this paper, we provide a simple diagnostic for temporal dependence and a simple remedy. Our remedy is based on the idea that BTSCS data is identical to grouped duration data. This remedy does not require the BTSCS analyst to acquire any further methodological skills and it can be easily implemented in any standard statistical software package. While our approach is suitable for any type of BTSCS data, we provide examples and applications from the field of International Relations, where BTSCS data is frequently used. We use our methodology to re-assess Oneal and Russett's (1997) findings regarding the relationship between economic interdependence, democracy, and peace. Our analyses show that 1) their finding that economic interdependence is associated with peace is an artifact of their failure to account for temporal dependence and 2) their finding that democracy inhibits conflict is upheld even taking duration dependence into account.

Explaining Fixed Effects: Random Effects modelling of Time-Series Cross-Sectional and Panel Data
Bell, Andrew
Jones, Kelvyn

Uploaded 09-11-2013
Keywords Random Effects models
Fixed Effects models
Random coefficient models
Mundlak formulation
Fixed effects vector decomposition
Hausman test
Panel Data
Time-Series Cross-Sectional Data
Abstract This article challenges Fixed Effects (FE) modelling as the ‘default’ for time-series-cross-sectional and panel data. Understanding differences between within- and between-effects is crucial when choosing modelling strategies. The downside of Random Effects (RE) modelling – correlated lower-level covariates and higher-level residuals – is omitted-variable bias, solvable with Mundlak’s (1978a) formulation. Consequently, RE can provide everything FE promises and more, and this is confirmed by Monte-Carlo simulations, which additionally show problems with another alternative, Plümper and Troeger’s Fixed Effects Vector Decomposition method, when data are unbalanced. As well as being able to model time-invariant variables, RE is readily extendable, with random coefficients, cross-level interactions, and complex variance functions. An empirical example shows that disregarding these extensions can produce misleading results. We argue not simply for technical solutions to endogeneity, but for the substantive importance of context and heterogeneity, modelled using RE. The implications extend beyond political science, to all multilevel datasets.

Estimation and Inference by Bayesian Simulation: an on-line resource for social scientists
Jackman, Simon

Uploaded 08-30-1999
Keywords Markov chain Monte Carlo
Bayesian statistics
ordinal probit
time series
Abstract http://tamarama.stanford.edu/mcmc a Web-based on-line resource for Markov chain Monte Carlo, specifically tailored for social scientists. MCMC is probably the most exciting development in statistics in the last ten years. But to date, most applications of MCMC methods are in bio-statistics, making it difficult for social scientists to fully grasp the power of MCMC methods. In providing this on-line resource I aim to overcome this deficiency, helping to put MCMC in the reach of social scientists. The resource comprises: (*) a set of worked examples (*) data and programs (*) links to other relevant web sites (*) notes and papers At the meetings in Atlanta, I will present two of the worked examples, which are part of this document: (*) Cosponsor: computing auxiliary quantities from MCMC output (e.g., percent correctly predicted in a logit/probit model of legislative behavior; cf Herron 1999). (*) Delegation: estimating a time-series model for ordinal data (e.g., changes to the U.S. president's discretionary power in trade policy, 1890-1990; cf Epstein and O'Halloran 1996).

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.

The Political Economy of Non-Tariff Trade Barriers: A Test of the Veto Players Theory of Policy Change
Kotin, Daniel

Uploaded 07-13-1999
Keywords cross-sectional time-series
missing data
Abstract This paper tests George Tsebelis's (1995) veto players model of policy stability, as applied to international trade policy. The veto players model argues that policy change is more difficult with the number of political actors that can veto such change, their ideological polarization, and (for collective veto players) their cohesion. I test this model's ability to predict the variation in non-tariff barriers (NTBs) to international trade for 16 industrial democracies, over the period 1981-94. Such barriers may be becoming increasingly attractive to states seeking to maintain trade protection in the face of secular declines in tariff rates. In a regression model controlling for economic factors and other domestic political influences on NTBs, such as politicians' trade policy preferences, minority government, and constituency pressures, I find support for Tsebelis's theory: Governments in the sample that are more polarized on the trade policy dimension are less able to change NTB policy. This finding holds despite the presence of a significant amount of missing data on the dependent variable, which consists of first differences taken across missing years, according to an alternative model in which the missing NTB levels are imputed via interpolation, and from which the first differences are then computed. Although more NTB data is needed to verify them, these preliminary results add to a growing body of literature finding empirical support for the veto players theory.

Social Capital, Government Performance, and the Dynamics of
Keele, Luke

Uploaded 10-14-2004
Keywords trust in government
social capital
time series
error correction models
Abstract In extant research on trust in government, a tension has developed between whether the movement of trust over time is a function of political performance or political alienation. In performance based explanations trust responds to the economy, Congress and the President and should move frequently over time. Under theories of political alienation, government performance matters little as hostility toward both political leaders and the political process causes distrust of government, and is a direct threat to government legitimacy. Using aggregate data in a time series analysis of trust in government, I find that both political alienation, as measured by social capital and performance have important but differing effects on trust. Government performance has an immediate effect on trust while movement in social capital sets the long-term level of trust. The qualitative outcome is that trust embodies both performance and political alienation and is an important indicator of citizen satisfaction with government.

Cointegration and Military Rivalry: Some Evidence on 5 Modern Rivalries
Gerace, Michael P.

Uploaded 11-29-1999
Keywords cointegration
military rivalry
military expenditures
arms race
time series
Johansen Method
Abstract his article investigates the possibilities for stability in arms races, with its starting point being Richardson's discussion of stability conditions. Most discussions of stability focus on whether armaments levels become stable, but there could also be a stable relationship between the armaments of rivals. By employing a time series approach, the behavioral aspects of a model and underlying stability conditions can be related clearly to data characteristics, which clarifies the possibilities for a model. The military expenditures of 5 sets of rivals are then investigated for stationarity, the nature of the trend, and for cointegration. Whether the data are stationary and, if not, the nature of the trend, have implications for what kind of stability can exist over the long-run (or whether the models are explosive). The Johansen method is used for the cointegration tests, and VEC models are evaluated for two cases. While the results are mixed, there is some support for cointegrating relationships among rivals, there is no indication of stability in the level of expenditures or of explosive instability over the long-run.

Dynamic Models for Dynamic Theories: The Ins and Outs of Lagged Dependent Variables
Keele, Luke
Kelly, Nathan

Uploaded 06-28-2005
Keywords time series
lagged dependent variables
Abstract A lagged dependent variable in an OLS regression is often used as a means of capturing dynamic effects in political processes and as a method for ridding the model of autocorrelation. But recent work contends that the lagged dependent variable specification is too problematic for use in most situations. More specifically, if residual autocorrelation is present, the lagged dependent variable causes the coefficients for explanatory variables to be biased downward. We use a Monte Carlo analysis to assess empirically how much bias is present when a lagged dependent variable is used under a wide variety of circumstances. In our analysis, we compare the performance of the lagged dependent variable model to several other time series models. We show that while the lagged dependent variable is inappropriate in some circumstances, it remains the an appropriate model for the dynamic theories often tested by applied analysts. From the analysis, we develop several practical suggestions on when and how to use lagged dependent variables on the right hand side of a model.

GEE Models of Judicial Behavior
Zorn, Christopher

Uploaded 04-02-1998
Keywords generalized estimating equations
time-series cross-sectional data
temporal dependence
judicial decision making
Abstract The assumption of independent observations in judicial decision making flies in the face of our theoretical understanding of the topic. In particular, two characteristics of judicial decision making on collegial courts introduce heterogeneity into successive decisions: individual variation in the extent to which different jurists maintain consistency in their voting behavior over time, and the ability of one judge or justice to influence another in their decisions. This paper addresses these issues by framing judicial behavior in a time-series cross-section context and using the recently developed technique of generalized estimating equations (GEE) to estimate models of that behavior. Because the GEE approach allows for flexible estimation of the conditional correlation matrix within cross-sectional observations, it permits the researcher to explicitly model interjustice influence or over-time dependence in judicial decisions. I utilize this approach to examine two issues in judicial decision making: latent interjustice influence in civil rights and liberties cases during the Burger Court, and temporal consistency in Supreme Court voting in habeas corpus decisions in the postwar era. GEE estimators are shown to be comparable to more conventional pooled and TSCS techniques in estimating variable effects, but have the additional benefit of providing empirical estimates of time- and panel- based heterogeneity in judicial behavior.

Testing the Pooling Assumption with Cross-Sectional Time Series Data: A Proposal and an Assesment with Simulation Experiments
Stanig, Piero

Uploaded 07-17-2005
Keywords Cross-Sectional Time Series Data
heterogeneity of coefficients
Abstract I propose to use the loss of fit of the cross-validated predictions relative to the fit of the predictions from a pooled regression to test the assumption of constant betas across countries in a CSTS setting. The performance of this measure is a) evaluated in several simulation experiments that reproduce research situations common in comparative politics, and b) compared to the “cross-validated standard error of the regression”, proposed by Franzese(2002). I show that the measure I propose depends much less on the stochastic component in the DGP, and is better able to detect the country-specificity of the betas. I calculate the critical values that can be used to test the pooling assumption in some typical comparative politics CSTS situations. Finally, to evaluate the behavior of the measure with an actual dataset, I replicate the results of Alvarez et al. (1991) as replicated in Beck et al. (1993), calculate the proposed measure, and show that the pooling assumption does not seem to be inappropriate for the model they estimate.

Time-Series--Cross-Section Issues: Dynamics, 2004
Beck, Nathaniel
Katz, Jonathan

Uploaded 07-24-2004
Keywords Time-series--cross-section data
lagged dependent variables
Nickell bias
Abstract This paper deals with a variety of dynamic issues in the analysis of time-series--cross-section (TSCS) data raised by recent papers; it also more briefly treats some cross-sectional issues. 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). It is also shown that there is nothing pernicious in using a lagged dependent variable, and 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 models, and analysts may choose on grounds of convenience (assuming that the model passes standard econometric tests). When adjustment is slow it may be the case that the data are integrated, which means that no method developed for the stationary case is appropriate. At the cross-sectional level, it is argued that the critical issue is assessing heterogeneity; a variety of strategies for this assessment are discussed.

Cointegration Tests when Data are Near-Integrated
De Boef, Suzanna
Granato, Jim

Uploaded 04-22-1998
Keywords time series
DickeyFuller tests
Monte Carlo
Abstract Testing theories about political change requires analysts to make assumptions about the nature of the memory of their time series. Applied analyses are often based on inferences that the time series of interest are integrated and cointegrated. Typically these analyses rest on Dickey-Fuller pretests for unit roots and tests for cointegration based on the residuals from a cointegrating regression in the context of the Engle-Granger two-step methodology. We argue that this approach is not a good one and use Monte Carlo results to show that these tests can lead analysts to falsely conclude that the data are cointegrated (or nearly- cointegrated) when the data is near-integrated and not cointegrating. Further, analysts are likely to falsely conclude the relationship is not cointegrating when it is. We show how inferences are highly sensitive to sample size and the signal to noise ratio in the data. We suggest that analysts use the single equation error correction test for cointegrating relationships, and that caution be used in all cases where near-integration is a reasonable alternative to unit roots. Finally, we suggest that in many cases analysts can drop the language of cointegration and adopt single equation error correction models when the theory of error correction is relevant.

Revisiting Dynamic Specification
De Boef, Suzanna
Keele, Luke

Uploaded 07-18-2005
Keywords time series
error correction
auto-distributed lag models
Abstract 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.

The Macro Mechanics of Social Capital
Keele, Luke

Uploaded 10-15-2003
Keywords social capital
time series
public opinion
Abstract Interest in social capital has grown as it has become apparent that it is an important predictor of collective well-being. Recently, however, attention has shifted to how levels of social capital have changed over time. But focusing on how a society moves from one level of social capital to another requires that we alter current theory. In particular, by moving to the context of temporal change, we must not treat it as a lumpy concept with general causes and effects. Instead, we need a theory that explains the macro mechanics between civic activity and interpersonal trust. In the following analysis, I develop a macro theory of social capital through a careful delineation of the social capital aggregation process which demonstrates that we should expect civic engagement to affect interpersonal trust over time with the reverse not being true. Then, I develop and use new longitudinal measures of civic engagement and interpersonal trust to test the direction of causality between the two components of social capital. Finally, I model civic engagement as a function of resources and demonstrate how the decline in civic engagement has adversely affected levels of interpersonal trust over the last thirty years.

Estimating Time-Varying Parameters with Flexible Least Squares
Wood, B. Dan

Uploaded 07-02-1998
Keywords time series
time-varying parameters
stochastic parameters
flexible least squares
Abstract A common assumption among time series analysts is that estimated coefficients remain constant through time. Yet this strong assumption often has little grounds in substantive theory or empirical tests. If coefficients vary through time in an infinite time sequence, but are estimated with constant coefficient methods in a finite time sequence, then this can lead to significant information loss, as well as to errors of inference. This paper demonstrates a method for exploring the relative stability of time series coefficients, Flexible Least Squares (FLS). In particular, FLS is superior to other such methods, in that it enables the analyst to diagnose the magnitude of coefficient variation, as well as detect which particular coefficients are changing. FLS also provides an estimated vector of time-varying coefficients that can be used for exploratory or descriptive purposes. FLS properties are demonstrated through simulation analysis and an evaluation of the time-varying equilibrium between federal revenues and expenditures from 1904-1996.

Taking Time Seriously: Dynamic Regression
Keele, Luke
De Boef, Suzanna

Uploaded 10-14-2004
Keywords Time series
error correction models
lagged dependent variables
ADL models
Abstract 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. While advances in time series methods have helped us to think about political change in important ways, too often published time series analysis displays shortcomings in three areas. First, analysts often estimate models without testing the restrictions implied by their specification. Second, applied researchers link the theoretical concept of equilibrium with the existence of cointegration and use of error correction models. Third, those estimating time series models have often done a poor job of interpreting their statistical results. The consequences, at best, are poor connections between theory and tests and thus a limited cumulation of knowledge. Often, the costs include biased results and incorrect inferences as well. Here, we outline techniques for the estimation of linear models with dynamic specification. In general, we recommend that analysts start with a combination of general dynamic models and test for restrictions before adopting a particular specification. Finally, we recommend that analysts make use of the wide array of information that can be gleaned from dynamic specifications. We illustrate this strategy with data Congressional approval and tax rates across OECD countries.

Lagging the Dog?: The Robustness of Panel Corrected Standard Errors in the Presence of Serial Correlation and Observation Specific Effects
Kristensen, Ida
Wawro, Gregory

Uploaded 07-13-2003
Keywords time-series cross-section data
serial correlation
fixed effects
panel data
lag models
Monte Carlo experiments
Abstract This paper examines the performance of the method of panel corrected standard errors (PCSEs) for time-series cross-section data when a lag of the dependent variable is included as a regressor. The lag specification can be problematic if observation-specific effects are not properly accounted for, leading to biased and inconsistent estimates of coefficients and standard errors. We conduct Monte Carlo studies to assess how problematic the lag specification is, and find that, although the method of PCSEs is robust when there is little to no correlation between unit effects and explanatory variables, the method's performance declines as that correlation increases. A fixed effects estimator with robust standard errors appears to do better in these situations.

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.

What to do About Missing Values in Time Series Cross-Section Data
King, Gary
Honaker, James

Uploaded 07-14-2006
Keywords Missing data
multiple imputation
time series
Abstract Applications of modern methods for analyzing data with missing values, based primarily on multiple imputation, have in the last half-decade become common in American politics and political behavior. Scholars in these fields have thus increasingly avoided the biases and inefficiencies caused by ad hoc methods like listwise deletion and best guess imputation. However, researchers in much of comparative politics and international relations, and others with similar data, have been unable to do the same because the best available imputation methods work poorly with the time-series cross-section data structures common in these fields. We attempt to rectify this situation. First, we build a multiple imputation model that allows smooth time trends, shifts across cross-sectional units, and correlations over time and space, resulting in far more accurate imputations. Second, we build nonignorable missingness models by enabling analysts to incorporate knowledge from area studies experts via priors on individual missing cell values, rather than on difficult-to-interpret model parameters. Third, since these tasks could not be accomplished within existing imputation algorithms, in that they cannot handle as many variables as needed even in the simpler cross-sectional data for which they were designed, we also develop a new algorithm that substantially expands the range of computationally feasible data types and sizes for which multiple imputation can be used. These developments made it possible for us to implement our methods in new open source software which, unlike all existing multiple imputation packages, virtually never crashes.

Space Is more than Geography
Beck, Nathaniel
Gleditsch, Kristian

Uploaded 07-11-2003
Keywords spatial econometrics
time-series--cross-section data
Abstract Most spatial models use some measure of distance in the spatial weighting matrix. But this is not required: any measure of "similarity" that has the mathematical properties of distance will work well. Here we use spatial methods to allow for dyads which share a common partner to be similar (and a directed dyad and its reverse to be especially similar). While we find evidence of spatial effects in a model with a spatially lagged error, we note that the substantive conseequences of taking this into account are not great. We then use various measures of "community" to assess the impact of similarity in models of democracy and development; the three similarity measures are physical distance, cultural (religious) similarity and trade. In a simple cross-sectional model the spatial lag has large consequences; however, when we move to time-series--cross-section data the impact of the spatial lag is very small. We also argue that one can simplify estimation in many time-series--cross-sectional data sets with temporally independent errors by using the first temporal lag of the spatial lag, which makes for simple estimation.

Time Series Models for Discrete Data: solutions to a problem with quantitative studies of international conflict
Jackman, Simon

Uploaded 07-21-1998
Keywords categorical time series
dependent binary data
Markov regression models
latent autoregressive process
Markov Chain Monte Carlo
international conflict
democratic peace
Abstract Discrete dependent variables with a time series structure occupy something of a statistical limbo for even well-trained political scientists, prompting awkward methodological compromises and dubious substantive conclusions. An important example is the use of binary response models in the analysis of longitudinal data on international conflict: researchers understand that the data are not independent, but lack any way to model serial dependence in the data. Here I survey methods for modeling categorical data with a serial structure. I consider a number of simple models that enjoy frequent use outside of political science (originating in biostatistics), as well as a logit model with an autoregressive error structure (the latter model is fit via Bayesian simulation using Markov chain Monte Carlo methods). I illustrate these models in the context of international conflict data. Like other re-analyses of these data addressing the issue of serial dependence, citeaffixed{beck:btscs}{e.g.,}, I find economic interdependence does not lessen the chances of international conflict. Other findings include a number of interesting asymmetries in the effects of covariates on transitions from peace to war (and vice versa). Any reasonable model of international conflict should take into account the high levels of persistence in the data; the models I present here suggest a number of methods for doing so.

An Automated Method of Topic-Coding Legislative Speech Over Time with Application to the 105th-108th U.S. Senate
Quinn, Kevin
Monroe, Burt
Colaresi, Michael
Crespin, Michael
Radev, Dragomir

Uploaded 07-18-2006
Keywords legislatures
content analysis
time series
cluster analysis
unsupervised learning
Abstract We describe a method for statistical learning from speech documents that we apply to the Congressional Record in order to gain new insight into the dynamics of the political agenda. Prior efforts to evaluate the attention of elected representatives across topic areas have largely been expensive manual coding exercises and are generally circumscribed along one or more features of detail: limited time periods, high levels of temporal aggregation, and coarse topical categories. Conversely, the Congressional Record has scarcely been used for such analyses, largely because it contains too much information to absorb manually. We describe here a method for inferring, through the patterns of word choice in each speech and the dynamics of word choice patterns across time, (a) what the topics of speeches are, and (b) the probability that attention will be paid to any given topic or set of topics over time. We use the model to examine the agenda in the United States Senate from 1997-2004, based on a new database of over 70 thousand speech documents containing over 70 million words. We estimate the model for 42 topics and provide evidence that we can reveal speech topics that are both distinctive and inter-related in substantively meaningful ways. We demonstrate further that the dynamics our model gives us leverage into important questions about the dynamics of the political agenda.

Covariate Balance in Time-Series Cross-Section Data
Dimmery, Drew

Uploaded 07-22-2013
Keywords time-series cross-section
causal inference
Abstract I develop and assess methods to evaluate covariate balance with panel and time-series cross-section (TSCS) data. Balance checking of this variety provides the foundation for non-parametric approaches to estimating causal effects with panel and time-series cross-section data, such as panel matching or panel reweighting. I consider a number of approaches, including a benchmark approach that simply evaluates balance on a set of lagged variables, ignoring trends. I compare this to alternative approaches that directly examine trends, including using 1) regression coefficients on a polynomial in time, 2) derivatives at the point of treatment estimated by local polynomial regression, and 3) the previous techniques re-estimated using shrinkage estimation to improve efficiency 4) parametric and non--parametric smoothing. I evaluate whether these approaches outperform the benchmark approach by borrowing strength across time periods (and across panels via shrinkage). I work with simulations and data from political science, varying various features of the data, including length of available histories and complexity of time-series trajectories.

Methods for Extremely Large Scale Media Experiments and Observational Studies
King, Gary
Schneer, Benjamin
White, Ariel

Uploaded 07-18-2014
Keywords experiments
public opinion
media effects
social media
text analysis
time series
causal inference
Abstract We develop statistical methods and large scale data engineering approaches for estimating the effects of a large number of mass and specialized media sites on opinions expressed in the daily flow of millions of social media posts. We first describe the instruments we adapt, develop, and validate for summarizing detailed opinions in social media posts, and then outline the procedures we devised for acquiring and summarizing news content from, and web traffic to, large numbers of media outlets. We then derive statistical methods for estimating the causal effect of changes in the news on social media opinions appropriate for observational, quasi-experimental, and experimental settings

Generalized Synthetic Control Method for Causal Inference in Time-Series Cross-Sectional Data
Xu, Yiqing

Uploaded 07-21-2014
Keywords difference-in-differences
synthetic control method
causal inference
time-series cross-sectional data
Abstract Difference-in-differences (DID) is commonly used for causal inference in time-series cross-sectional data. It requires the assumption that the average outcomes of treated and control units would have followed parallel paths in the absence of treatment. In this paper, I propose a method that not only relaxes this often-violated assumption, but also unifies the synthetic control method (Abadie, Diamond and Hainmueller 2010) with linear fixed effect models under a simple framework, of which DID is a special case. It imputes counterfactuals for each treated unit in post-treatment periods using control group information based on a linear interactive fixed effect model that incorporates unit-specific intercepts interacted with time-varying coefficients. This method has several advantages. First, it allows the treatment to be correlated with unobserved unit and time heterogeneities under reasonable modelling assumptions. Second, it generalizes the synthetic control method to the case of multiple treated units and variable treatment periods, and improves efficiency and interpretability. Third, with a built-in cross-validation procedure, it avoids specification searches and thus is transparent and easy to implement. Monte Carlo results show that this method performs well with small numbers of control units and pre-treatment periods.

Uncovering Common Latent Space of Multiple Networks Using Bayesian Spectral Approach
Sohn, Yunkyu
Park, Jong Hee

Uploaded 07-25-2014
Keywords multi-layer network
hierarchical Bayesian PARAFAC
network change point detection
network time series
stochastic block model
text analysis
international trade
North Korea
US Senate change points
Abstract Social scientists commonly encounter multiple realizations of networks, each of which is possibly governed by distinct generation rules, while sharing key traits. However, conventional network models in social sciences focus largely on a single-type, single-layer network, lacking a unified framework for the recovery of the interconnectivity associated with multiple networks. By employing spectral approach, we discuss a Bayesian statistical method to unravel a common latent space of actors from multiple relational data (e.g. networks with different link definition, time series networks). We extend this idea by incorporating change-point estimation method for proper grouping of layers in network time series data.

The Effectiveness of Public and Private Threats: A Document-Based Approach
Min, Eric
Katagiri, Azusa

Uploaded 07-21-2015
Keywords diplomacy
audience cost
machine learning
random forest
Abstract Despite the importance of diplomacy in international relations, few studies have gone beyond case studies and formal models to rigorously study the particular roles of public and private diplomacy during crises. Our paper performs a systematic, document-based study on the efficacy of public versus private threats. We digitize and analyze over 30,000 declassified American telegrams, collected from around the country, related to the Berlin Crisis from 1958 to 1963--a historically important period punctuated by multiple crisis moments that instigated both public and private diplomatic fervor. Using statistical learning techniques and time-series analysis, we find conditions under which Soviet public and private threats affect American leaders' conceptions regarding the credibility of Soviet claims. Contrary to much of the literature's key assumptions, neither public nor private diplomacy have a substantively important effect on shaping American elites' perceptions. We thus highlight the importance of text-based methods in investigating and challenging long-standing theories in international relations.

From Words to Time Series that are Ready for Analysis: A Bayesian Approach to Estimating Party Positions Over Time
Guntermann, Eric

Uploaded 07-24-2015
Keywords Text analysis
Time series
Abstract This paper proposes a two-stage Bayesian adaptation of the popular Wordfish text analysis program that allows scholars to create time series that can be used to answer interesting questions about dynamic relationships. It avoids assuming that words have stable meaning over time and that the first dimension underlying word usage is the dimension of interest. It does so by running the algorithm on a small number of debates in which we know parties express the relevant positions, which are also known a priori, as well as on all speeches by deputies from each party. Posterior distributions on word coefficients from these analyses are then combined to create priors for a second stage that produces time series for each party. In sum, this paper proposes an algorithm that uses the information we have about party positions in particular debates and their overall positions to produce information we do not have, time series of party positions. The method is applied to party positions on regional nationalism in the Spanish autonomous community of Catalonia.

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