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Forecasting the 2005 Parliamentary Elections in the United Kingdom


The British Journal of Politics and International Relations is about to publish a collection of articles on election forecasting. Five articles apply different models to the forthcoming British election and a sixth casts a skeptical eye on the entire enterprise. Courtesy of that journal, the abstracts are reproduced below.

Election Forecasting: Principles and Practice

Michael S. Lewis-Beck

To forecast an election means to declare the outcome before it happens. Scientific approaches to election forecasting include polls, political stock markets and statistical models. I review these approaches, with an emphasis on the last, since it offers more lead time. Consideration is given to the history and politics of statistical forecasting models of elections. Rules for evaluating such models are offered. Examples of actual models come from the United States, France and the United Kingdom, where this work is rather new. Compared to other approaches, statistical modeling seems a promising method for forecasting elections.

Forecasting Seats from Votes in British General Elections

Paul F. Whiteley

This article develops a forecasting model of seat shares in the House of Commons applied to general election outcomes. The model utilizes past information about party seat shares, together with data from the polls gathered prior to the election, to forecast the number of seats won by the parties. Once it has been estimated the model will be used to make a forecast of the outcome of a possible general election in May 2005. The article starts by focusing on research into translating votes into seats, or the cube rule and its modifications. It then goes on to develop the forecasting model, which is based on electoral and poll data from 1945 to 2001.

Popularity Function Forecasts for the 2005 UK General Election

David Sanders

The article provides a set of contingent forecasts for the forthcoming UK general election. The forecasts are based on popularity function derived from monthly time series data covering the period 1997-2004. On most likely assumptions, the forecasts produce a clear Labour victory in the early summer of 2005, with the Liberal Democrats increasing their vote share by roughly four percentage points.

A Political Economy Forecast for the 2005 British General Election

Eric Bélanger, Michael S. Lewis-Beck and Richard Nadeau
(the E in ERIC and the first E in Belanger have accents over them)

Recently, we proposed an original statistical model for forecasting general elections in the United Kingdom, based on the observation of a few key indicators of the political and economic system. That vote function model was tested against the results of the 2001 general election. Here we evaluate the results of that test, and offer an appropriately revised model for the forecasting of the upcoming 2005 general election. According to our forecast, a Labour victory appears the most likely outcome.

Forecasting the 2005 General Election: A Neural Network Approach

Roman Borisyuk, Galina Borisyuk, Colin Rallings and Michael Thrasher

Although neural networks are increasingly used in a variety of disciplines there are few applications in political science. Approaches to electoral forecasting traditionally employ some form of linear regression modeling. By contrast, neural networks offer the opportunity to consider also the non-linear aspects of the process, promising a better performance, efficacy and flexibility. The initial development of this approach preceded the 2001 general election and models correctly predicted a Labour victory. The original data used for training and testing the network were based on the responses of two experts to a set of questions covering each general election held since 1835 up to 1997. To bring the model up to date, 2001 election data were added to the training set and two separate neural networks were trained using the views of our original two experts. To generate a forecast for the forthcoming general election, answers to the same questions about the performance of parties during the current parliament, obtained from a further 35 expert respondents, were offered to the neural networks. Both models, with slightly different probabilities, forecast another Labour victory. Modeling electoral forecasts using neural networks is at an early stage of development but the method is to be adapted to forecast party shares in local council elections. The greater frequency of such elections will offer better opportunities for training and testing the neural networks.

Election Forecasting: A Skeptical View

Cees van der Eijk

This brief note contains some doubts about the predominant kind of statistical election forecasting that is discussed in this issue. It is not meant to be a full-scale critique of the approach, the methods and the models that have been reported in the literature. Rather, it is intended to be an attempt to explicate some of the recurring feeling of disenchantment that can be experienced every time we come across these forecasts. . . . [T]his note briefly discusses the theoretical core of statistical forecasting models, and argues that its theoretical foundations are unsatisfactory. It then discusses the implausibility of the functional specification of the core specifications of forecasting models. It then concludes with some comments on the theoretical scope of the forecasting tradition. . . .