Forecasting Elections with High Volatility

This article uses data from the social survey Allbus 1998 to introduce a method of forecasting elections in a context of electoral volatility. The approach models the processes of change in electoral behaviour, exploring patterns in order to model the volatility expressed by voters. The forecast is based on the matrix of transition probabilities, following the logic of Markov chains. The power of the matrix, and the use of the mover-stayer model, is debated for alternative forecasts. As an example of high volatility, the model uses data from the German general election of 1998. The unification of two German states in 1990 caused the incorporation of around 15 million new voters from East Germany who had limited familiarity and no direct experience of the political culture in West Germany. Under these circumstances, voters were expected to show high volatility.

Dynamic D-Vine copula model with applications to Value-at-Risk (VaR)

Regular vine copulas are multivariate dependence models constructed from pair-copulas (bivariate copulas). In this paper, we allow the dependence parameters of the pair-copulas in a D-vine decomposition to be potentially time-varying, following a nonlinear restricted ARMA(1,m) process, in order to obtain a very flexible dependence model for applications to multivariate financial return data. We investigate the dependence among the broad stock market indexes from Germany (DAX), France (CAC 40), Britain (FTSE 100), the United States (S&P 500) and Brazil (IBOVESPA) both in a crisis and in a non-crisis period. We find evidence of stronger dependence among the indexes in bear markets. Surprisingly, though, the dynamic D-vine copula indicates the occurrence of a sharp decrease in dependence between the indexes FTSE and CAC in the beginning of 2011, and also between CAC and DAX during mid-2011 and in the beginning of 2008, suggesting the absence of contagion in these cases. We also evaluate the dynamic D-vine copula with respect to Value-at-Risk (VaR) forecasting accuracy in crisis periods. The dynamic D-vine outperforms the static D-vine in terms of predictive accuracy for our real data sets.

Election Forecasting under Opaque Conditions

Election forecasting models assume retrospective economic voting and clear mechanisms of accountability. Previous research indeed indicates that incumbent political parties are being held accountable for the state of the economy. In this article we develop a ‘hard case’ for the assumptions of election forecasting models. Belgium is a multiparty system with perennial coalition governments. Furthermore, Belgium has two completely segregated party systems (Dutch and French language). Since the prime minister during the period 1974-2011 has always been a Dutch language politician, French language voters could not even vote for the prime minister, so this cognitive shortcut to establish political accountability is not available. Results of an analysis for the French speaking parties (1981-2010) show that even in these conditions of opaque accountability, retrospective economic voting occurs as election results respond to indicators with regard to GDP and unemployment levels. Party membership figures can be used to model the popularity function in election forecasting.

A review of the driver-vehicle effectiveness (DRIVEM) model. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Brush Creek, Kansas City, Missouri : hydraulic model investigation /

Cover title.

Yellow Creek sedimentation study : numerical model investigation /

"September 1993."

Comite River diversion, Comite River, Louisiana : hydraulic model investigation /

"June 1994."

Consumer behavior towards fuel efficient vehicles. Volume IV: operating instructions and programs documentation for the CS Vehicle Choice Simulation Model. Final report.

National Highway Traffic Safety Administration, Technology Assessment Division, Washington, D.C.

The demand for light duty trucks: the Wharton EFA Motor Vehicle Demand Model (Mark II). Final report.

National Highway Traffic Safety Administration, Office of Research and Development, Washington, D.C.

An analysis of the automobile market: modeling of the long-run determinants of the demand for automobiles. Volume III: appendices to the Wharton E.F.A. Automobile Demand Model. Final report.

Transportation Department, Washington, D.C.

Revisions to the Wharton EFA Automobile Demand Model (Mark I). Final report.

National Highway Traffic Safety Administration, Office of Research and Development, Washington, D.C.

An analysis of the automobile market: modeling of the long-run determinants of the demand for automobiles. Volume II: simulation analysis using the Wharton E.F.A. Automobile Demand Model. Final report.

Transportation Department, Washington, D.C.

An analysis of the automobile market: modeling of the long-run determinants of the demand for automobiles. Volume I: the Wharton E.F.A. Automobile Demand Model. Final report.

Transportation Department, Washington, D.C.

An econometric model of the American minor cycle,

Bibliography: p. 30-34.

The LFM model - 1976 : a documentation /

Mode of access: Internet.