CREATES

Forecasting daily political opinion polls using the fractionally cointegrated vector auto-regressive model

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Standard

Forecasting daily political opinion polls using the fractionally cointegrated vector auto-regressive model. / Nielsen, Morten Ørregaard; Shibaev, Sergei S.

In: Journal of the Royal Statistical Society. Series A: Statistics in Society, Vol. 181, No. 1, 01.01.2018, p. 3-33.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Harvard

Nielsen, MØ & Shibaev, SS 2018, 'Forecasting daily political opinion polls using the fractionally cointegrated vector auto-regressive model', Journal of the Royal Statistical Society. Series A: Statistics in Society, vol. 181, no. 1, pp. 3-33. https://doi.org/10.1111/rssa.12251

APA

Nielsen, M. Ø., & Shibaev, S. S. (2018). Forecasting daily political opinion polls using the fractionally cointegrated vector auto-regressive model. Journal of the Royal Statistical Society. Series A: Statistics in Society, 181(1), 3-33. https://doi.org/10.1111/rssa.12251

CBE

Nielsen MØ, Shibaev SS. 2018. Forecasting daily political opinion polls using the fractionally cointegrated vector auto-regressive model. Journal of the Royal Statistical Society. Series A: Statistics in Society. 181(1):3-33. https://doi.org/10.1111/rssa.12251

MLA

Nielsen, Morten Ørregaard and Sergei S. Shibaev. "Forecasting daily political opinion polls using the fractionally cointegrated vector auto-regressive model". Journal of the Royal Statistical Society. Series A: Statistics in Society. 2018, 181(1). 3-33. https://doi.org/10.1111/rssa.12251

Vancouver

Nielsen MØ, Shibaev SS. Forecasting daily political opinion polls using the fractionally cointegrated vector auto-regressive model. Journal of the Royal Statistical Society. Series A: Statistics in Society. 2018 Jan 1;181(1):3-33. https://doi.org/10.1111/rssa.12251

Author

Nielsen, Morten Ørregaard ; Shibaev, Sergei S. / Forecasting daily political opinion polls using the fractionally cointegrated vector auto-regressive model. In: Journal of the Royal Statistical Society. Series A: Statistics in Society. 2018 ; Vol. 181, No. 1. pp. 3-33.

Bibtex

@article{8ea3bc2cf66847139abe0e62d8a75f53,
title = "Forecasting daily political opinion polls using the fractionally cointegrated vector auto-regressive model",
abstract = "We examine forecasting performance of the recent fractionally cointegrated vector auto-regressive (FCVAR) model. We use daily polling data of political support in the UK 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 vector auto-regressive 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 cointegrated vector auto-regressive 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 UK Independence Party very closely, and we thus interpret it as a measure of Euroscepticism 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 to 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 auto-regressive model",
author = "Nielsen, {Morten {\O}rregaard} and Shibaev, {Sergei S.}",
year = "2018",
month = jan,
day = "1",
doi = "10.1111/rssa.12251",
language = "English",
volume = "181",
pages = "3--33",
journal = "Journal of the Royal Statistical Society, Series A (Statistics in Society)",
issn = "0964-1998",
publisher = "John Wiley & Sons, Inc.",
number = "1",

}

RIS

TY - JOUR

T1 - Forecasting daily political opinion polls using the fractionally cointegrated vector auto-regressive model

AU - Nielsen, Morten Ørregaard

AU - Shibaev, Sergei S.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - We examine forecasting performance of the recent fractionally cointegrated vector auto-regressive (FCVAR) model. We use daily polling data of political support in the UK 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 vector auto-regressive 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 cointegrated vector auto-regressive 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 UK Independence Party very closely, and we thus interpret it as a measure of Euroscepticism 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 to 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.

AB - We examine forecasting performance of the recent fractionally cointegrated vector auto-regressive (FCVAR) model. We use daily polling data of political support in the UK 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 vector auto-regressive 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 cointegrated vector auto-regressive 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 UK Independence Party very closely, and we thus interpret it as a measure of Euroscepticism 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 to 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.

KW - Forecasting

KW - Fractional cointegration

KW - Opinion poll data

KW - Vector auto-regressive model

U2 - 10.1111/rssa.12251

DO - 10.1111/rssa.12251

M3 - Journal article

AN - SCOPUS:85006018050

VL - 181

SP - 3

EP - 33

JO - Journal of the Royal Statistical Society, Series A (Statistics in Society)

JF - Journal of the Royal Statistical Society, Series A (Statistics in Society)

SN - 0964-1998

IS - 1

ER -