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Below results based on the criteria 'VAR'
Total number of records returned: 4

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.

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.

Politicians and the Press: Who Leads, Who Follows?
Bartels, Larry M.

Uploaded 08-23-1996
Keywords media
Abstract his paper examines the interplay between politicians and the press in setting the national policy agenda. The data for the analysis consist of daily counts of executive branch activities, congressional activities, New York Times stories, local newspaper stories, and ABC News coverage of Bosnia, Medicare, NAFTA, and Whitewater during the first three years of the Clinton administration. Vector autoregressions suggest that all three media outlets (and the politicians themselves) followed the lead of the executive branch on Bosnia and NAFTA and of Congress on Medicare and Whitewater. However, New York Times coverage led political activities even more than it followed them, with especially strong agenda-setting effects for NAFTA and Whitewater. The independent agenda-setting power of ABC News was substantially less than that of the Times, but still considerable, while local newspapers tended, by and large, to follow the lead of politicians and the national news media. Prepared for presentation at the Annual Meeting of the American Political Science Association, San Francisco, September 1996.

Moving Mountains: Bayesian Forecasting As Policy Evaluation
Brandt, Patrick T.
Freeman, John R.

Uploaded 04-24-2002
Keywords Bayesian vector autoregression
policy evaluation
conditional forecasting
Abstract Many policy analysts fail to appreciate the dynamic, complex causal nature of political processes. We advocate a vector autoregression (VAR) based approach to policy analysis that accounts for various multivariate and dynamic elements in policy formulation and for both dynamic and specification uncertainty of parameters. The model we present is based on recent developments in Bayesian VAR modeling and forecasting. We present an example based on work in Goldstein et al. (2001) that illustrates how a full accounting of the dynamics and uncertainty in multivariate data can lead to more precise and instructive results about international mediation in Middle Eastern conflict.

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