Forecasting daily political opinion polls using the fractionally cointegrated VAR model
Morten Nielsen () and
Sergei Shibaev ()
CREATES Research Papers from Department of Economics and Business Economics, Aarhus University
We examine forecasting performance of the recent fractionally cointegrated vector autoregressive (FCVAR) model. We use daily polling data of political support in the United Kingdom for 2010-2015 and compare with popular competing models at several forecast horizons. Our findings show that the four variants of the FCVAR model considered are generally ranked as the top four models in terms of forecast accuracy, and the FCVAR model significantly outperforms both univariate fractional models and the standard cointegrated VAR (CVAR) model at all forecast horizons. The relative forecast improvement is higher at longer forecast horizons, where the root mean squared forecast error of the FCVAR model is up to 15% lower than that of the univariate fractional models and up to 20% lower than that of the CVAR model. In an empirical application to the 2015 UK general election, the estimated common stochastic trend from the model follows the vote share of the UKIP very closely, and we thus interpret it as a measure of Euro-skepticism in public opinion rather than an indicator of the more traditional left-right political spectrum. In terms of prediction of vote shares in the election, forecasts generated by the FCVAR model leading into the election appear to provide a more informative assessment of the current state of public opinion on electoral support than the hung parliament prediction of the opinion poll.
Keywords: forecasting; fractional cointegration; opinion poll data; vector autoregressive model (search for similar items in EconPapers)
JEL-codes: C32 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-for, nep-ore and nep-pol
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Working Paper: Forecasting daily political opinion polls using the fractionally cointegrated VAR model (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2016-30
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