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Below results based on the criteria 'Panel data'
Total number of records returned: 17

Comparing GEE and Robust Standard Errors, with an Application to Judicial Voting
Zorn, Christopher

Uploaded 11-27-2000
Keywords GEE
panel data
robust variance
Abstract Implicit in most statistical analyses is the assumption that observations are conditionally independent; this claim has important implications, both statistical and substantive, for the conclusions we draw. I outline and compare two alternatives for addressing heterogeneity due to correlated data: the use of "robust" (or "heteroskedasticity-corrected") standard errors, and application of the method of generalized estimating equations ("GEEs"). I provide an example, based on an earlier study of judicial voting in search and seizure cases (Segal 1986), and use the example to discuss practical considerations in choosing among the various variance estimators in the presence of correlated data.

What to Do (and Not Do) With Dynamic Panel Data in Political Science (with apologies to Beck and Katz)
Wawro, Gregory

Uploaded 07-16-2000
Keywords dynamic panel data models
lagged endogenous variables
GMM estimators
party identification
campaign finance
Abstract Panel data is a very valuable resource for finding empirical solutions to political science puzzles. Yet numerous published studies in political science that use panel data to estimate models with dynamics have failed to take into account important estimation issues which call into question the inferences we can make from these analyses. Simply put, the failure to account explicitly for unobserved individual effects in panel data leads to inconsistent estimates of parameters of interest. The fundamental requirement for consistency of parameter estimates---that the explanatory variables in a regression equation must be uncorrleated with the disturbance term---is not met unless individual specific effects are adequately accounted for. Dynamic panel data estimators that eliminate this problem have become fairly standard in the economics literature. The purpose of this paper is to introduce these methods to political scientists. First, I show how the problem of inconsistency arises in dynamic panel data. I then show how to correct for this problem using generalized method of moments (GMM) estimators. I then demonstrate the usefulness of these methods with replications of published analyses.

Application of Panel Data Analysis to Kramer's Economic Voting Problem
Yoon, David

Uploaded 07-16-2000
Keywords economic voting
panel data
Abstract Although the health of a nation's economy has come to be seen as a reliable predictor of election outcome at the national level (e.g., Fair 1978, 1988), the corollary link between economic conditions and electoral behavior at the individual level remains less clear. Kinder and Kiewiet (1979) concluded that while the ups and downs of personal finances had negligible effect on an individual's voting behavior in national elections, the trajectory of the national economy had a significant effect. The hypothesis of the ``sociotropic'' voter was to be preferred over the ``pocketbook'' voter in thinking about whose economy mattered in elections. In an influential critique, Kramer (1983) argued that such a conclusion could not be drawn from purely cross-sectional survey data (data type used by Kinder and Kiewiet). According to Kramer, only the analysis of aggregate-level time-series data provide unbiased estimates of the effects of economic conditions on votes. Unfortunately, the two main competing hypotheses cannot be tested since individual-level economic factors cannot be studied with aggregate-level time series data alone. In contrast to previous analyses, I employ panel data (also known as longitudinal data) and analytical methods sensitive to the individual-level time-series structure of the data to estimate the relative magnitudes of the sociotropic and pocketbook effects, and test the merits of the respective hypotheses. Others have attempted to solve the Kramer problem by pooling cross-sectional data (e.g., Markus (1988, 1992)). Although pooled cross-sectional data allow investigators to compare sociotropic and pocketbook effects, they suffer from many of the same shortcomings of purely cross-sectional data. I use the 1993-1996 NES panel study to demonstrate the robustness of the sociotropic model and the strengths of panel analysis. I explain the battery of tests, estimators, and statistical assumptions used and relate these in detail to prevalent substantive political assumptions. And finally an uncommonly long panel from an Italian Nielsen survey is analyzed to demonstrate the utility of such

A Panel Probit Analysis of Campaign Contributions and Roll Call Votes
Wawro, Gregory

Uploaded 09-07-1999
Keywords campaign finance
panel data methods
random effects
GMM estimators
Abstract Political scientists have long been concerned with the effects of campaign contributions on roll call voting. However, methodological problems have hampered attempts to assess the degree to which contributions affect voting. One of the key problems is that it is difficult to untangle the effect of contributions from the effect of a member's predisposition to vote one way or another. That is, political action committees (PACs) contribute to members of Congress who are likely to vote the way the PACs favor even in the absence of contributions. A PAC donation to a friendly member might be misconstrued as causing a member to vote a particular way, when in reality the member would have voted that way to begin with. It is therefore crucial to account for a member's propensity to vote in a particular way in order to assess the influence of contributions. One way that studies have done this is to use ideological ratings developed by interest groups. This approach is problematic, however, because the ratings are built from roll call votes and thus will introduce bias if campaign contributions affect the votes used to compute the ratings. In order to circumvent the problem of accounting for voting predispositions, I use panel data methods which, unfortunately, have seen almost no application in political science. These methods enable us to account for individual specific effects which are difficult or impossible to measure, such as the predisposition to vote for or against a particular type of legislation. To employ these methods, I build panels of roll call votes on legislation that business and labor groups have indicated are important for their interests. Using panel data estimators, I determine the effects of contributions from corporate and labor PACs on the probability of voting ``aye'' or ``nay'', while accounting for members' propensities to vote in particular directions. I find that contributions have minimal to no effects on roll call votes, while short-term factors including monthly unemployment and support for the president in the district have substantial effects.

A Dynamic Panel Analysis of Campaign Contributions in Elections for the U.S. House of Representatives
Himmelberg, Charles P.
Wawro, Gregory

Uploaded 07-17-1998
Keywords campaign finance
panel data methods
GMM estimators
Abstract Political scientists have recognized the importance of dynamics in understanding the role of campaign finance in congressional elections. Yet for the most part, researchers have not exploited available data to its fullest or used appropriate methods to answer questions of interest. Though the Federal Election Commission's reporting and disclosure requirements enable us to use panel data models, researchers have ignored these powerful tools. One of the main advantages of panel data methods is that they enable us to account for unobserved individual and temporal effects that, if not accounted for, might lead us to incorrect inferences. In this paper we describe the problems with estimating dynamic panel models and discuss techniques that correct for these problems. We apply recently developed panel data methods to estimate a dynamic model of campaign finance and assess the usefulness of these methods by examining the robustness of results obtained with more traditional methods. We examine the relationship between past and current campaign contributions to incumbents and challengers during the 1986 through 1992 election cycles. Dynamic panel estimators give results that differ in substantively interesting ways from those given by standard estimators. In particular, the estimates obtained from dynamic panel methods suggest that challengers who are successful fundraisers can cut into the fundraising efforts of incumbents.

The Consequences of Majority-Minority Districts for Representation: Evidence of Partisan Mobilization, Countermobilization and Demobilization
Brandt, Patrick T.
Bailey, Michael

Uploaded 08-21-1997
Keywords Multinomial probit
panel data methods
simulated maximum likelihood
probability simulation
Abstract Few analyses of the effects of race-based congressional redistricting have used survey data to analyze the implications of redistricting. This type of micro-level data can add significant intuition to aggregate data analysis. This paper looks at whether voters respond to redistricting by mobilizing, demobilizing, or countermobilizing using panel data from the 1990-1992 National Election Study. A 2-period vote choice model is estimated using a multiperiod multinomial probit model, and controlling for the effects of redistricting. Results show that the presence of black Democratic candidates in majority-minority districts after redistricting reduces turnout by white voters for the Democratic candidates.

Legislative Entrepreneurship and Campaign Finance
Wawro, Gregory

Uploaded 07-21-1997
Keywords campaign finance
fixed effects
panel data
selection bias
Abstract Drawing on models of service--induced and investor PAC campaign contributions, I analyze the role that legislative entrepreneurship plays in PACs' contribution decisions. I explore the possibility that PACs use campaign contributions to invest in members of Congress with the expectation that members will reciprocate by engaging in entrepreneurial behavior to the benefit of PACs. To determine whether a relationship exists between legislative entrepreneurship and PAC contributions I compute measures of entrepreneurial behavior for individual members of the U.S. House using detailed data on bill sponsorship and congressional hearings from the 97th through the 101st Congress. In order to cleanly estimate the effects of legislative entrepreneurship, we need to account for unobservable member--specific factors that enter into the PAC contribution calculus. To account for such factors I employ panel data methods which require very few assumptions about the data and provide a way to test whether the manipulations of the data that are required for a panel analysis introduce bias.

Recent Developments in Econometric Modelling: A Personal Viewpoint
Maddala, G.S.

Uploaded 07-17-1997
Keywords dynamic panel data models
dynamic models with limited dependent variables
unit roots
Abstract The quotation above (more than three thousand years ago) essentially summarizes my perception of what is going on in econometrics. Dynamic economic modelling is a comprehensive term. It covers everything except pure cross-section analysis. Hence, I have to narrow down the scope of my paper. I shall not cover duration models, event studies, count data and Markovian models. The areas covered are: dynamic panel data models, dynamic models with limited dependent variables, unit roots, cointegration, VAR’s and Bayesian approaches to all these problems. These are areas I am most familiar with. Also, the paper is not a survey of recent developments. Rather, it presents what I feel are important issues in these areas. Also, as far as possible, I shall relate the issues with those considered in the work on Political Methodology. I have a rather different attitude towards econometric methods which my own colleagues in the profession may not share. In my opinion, there is too much technique and not enough discussion of why we are doing what we are doing. I am often reminded of the admonition of the queen to Pollonius in Shakespeare’s Hamlet, “More matter, less art.”

Heterogeneity and Individual Party Identification
Box-Steffensmeier, Janet M.
Smith, Renee M.

Uploaded 05-01-1997
Keywords heterogeneity
party identification
panel data
Monte Carlo
Abstract Box-Steffensmeier and Smith (1996) suggest that heterogeneity in individual-level party identification accounts for aggregate dynamics in macropartisanship. Wiley-Wiley estimates of partisan persistence suggesting a very high degree of individual-level partisan persistence have been made under the assumption of no heterogeneity. Stratifying panel data by subgroups based on information, interest, and age, shows some heterogeneity in persistence even when the Wiley-Wiley estimator is used. Analytical and Monte Carlo results show, however, that the Wiley-Wiley estimator is biased upward when heterogeneity is present. Given these problems, we estimate a beta-logistic model of heterogeneity and persistence in individual-level party identification and show (a) heterogeneity in the probabilities of persistent response does exist and (b) a portion of that heterogeneity is systematically explained by interest in political campaigns in the 1990-91-92 ANES panel three-wave panel. Our estimates indicate Markov models assuming true state dependence may not be needed. Further, we find that our estimate of one of the parameters of the beta distribution is consistent with the estimate of that parameter that would be derived from our previous aggregate-level analysis.

Markov Chain Models for Rolling Cross-section Data: How Campaign Events and Political Awareness Affect Vote Intentions and Partisanship in the United States and Canada
Mebane, Walter R.
Wand, Jonathan

Uploaded 04-07-1997
Keywords Markov chains
rolling cross-section data
macro data
measurement error
categorical data
ordinal data
panel data
survey data
party identification
American politics
Canadian politics
Abstract We use a new approach we have developed for estimating discrete, finite-state Markov chain models from ``macro'' data to analyze the dynamics of individual choice probabilities in two collections of rolling cross-sectional survey data that were designed to support investigations of what happens to voters' information and preferences during campaigns. Using data from the 1984 American National Election Studies Continuous Monitoring Study, we show that not only did individual party identification vary substantially during the year, but the dynamics of party identification changed significantly in response to the conclusion of the Democratic party's nomination contest. Party identification appears to have measurement error only when the model misspecifies the dynamics. There are rapid oscillations among some categories of partisanship that may reflect individual stances regarding not only competition between the parties but also competition among party factions. Using data from the 1993 Canadian Election Study, we show that the critical events that shaped voting intentions in the election varied tremendously depending on an individual's level of political awareness, and that the effects of awareness varied across regions of the country.

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.

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.

Should I Use Fixed or Random Effects?
Clark, Tom
Linzer, Drew

Uploaded 03-26-2012
Keywords Fixed effects
Random effects
Panel data
Abstract Empirical analyses in political science very commonly confront data that are grouped---multiple votes by individual legislators, multiple years in individual states, multiple conflicts during individual years, and so forth. Modeling these data presents a series of potential challenges, of which accounting for differences across the groups is perhaps the most well-known. Two widely-used methods are the use of either "fixed" or "random" effects models. However, how best to choose between these approaches remains unclear in the applied literature. We employ a series of simulation experiments to evaluate the relative performance of fixed and random effects estimators for varying types of datasets. We further investigate the commonly-used Hausman test, and demonstrate that it is neither a necessary nor sufficient statistic for deciding between fixed and random effects. We summarize the results into a typology of datasets to offer practical guidance to the applied researcher.

On the Use of Linear Fixed E ects Regression Models for Causal Inference
Imai, Kosuke
Kim, In Song

Uploaded 07-23-2012
Keywords difference-in-differences
first difference
observational data
panel data
propensity score
randomized experiments
Abstract Linear fixed effects regression models are a primary workhorse for causal inference among applied researchers. And yet, it has been shown that even when the treatment is exogenous within each unit, the linear regression models with unit-specific fixed effects may not consistently estimate the average treatment effect. In this paper, we offer a simple solution. Specifically, we show that weighted linear fixed effects regression models can accomodate a number of identification strategies including matching, stratification, first difference, propensity score weighting, and difference-in-differences. We prove the results by establishing finite sample equivalence relationships between weighted fixed effects and these estimators. Our analysis identifies the information implicitly used by standard fixed effects models to estimate counterfactual outcomes necessary for causal inference, highlighting the potential sources of their bias and inefficiency. In addition, we develop efficient computation strategies, model-based standard errors, and a specification test for weighted fixed effects estimators. Finally, we illustrate the proposed methodology by revisiting the controversy concerning the effects of the General Agreement on Tariffs and Trade (GATT) membership on international trade. Open-source software is available for fitting the proposed weighted linear fixed effects estimators.

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.

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.

Latent Variables and Rolling Panels: A New Approach to Modeling Campaign Effects
Therriault, Andrew

Uploaded 07-27-2011
Keywords panel data
latent variables
campaign effects
public opinion
Abstract Election panels which reinterview participants in rolling cross-sectional surveys offer new opportunities to study campaign effects, but also present unique methodological challenges. I develop an original approach to modeling this data, and demonstrate how its application leads to much stronger evidence for informing and persuasion effects from campaign ads than that found in existing research

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