Department of Economics and Business Economics

Forecasting daily political opinion polls using the fractionally cointegrated VAR model

Research output: Working paper/Preprint Working paperResearch

Standard

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

Aarhus : Institut for Økonomi, Aarhus Universitet, 2016.

Research output: Working paper/Preprint Working paperResearch

Harvard

Nielsen, MØ & Shibaev, SS 2016 'Forecasting daily political opinion polls using the fractionally cointegrated VAR model' Institut for Økonomi, Aarhus Universitet, Aarhus.

APA

Nielsen, M. Ø., & Shibaev, S. S. (2016). Forecasting daily political opinion polls using the fractionally cointegrated VAR model. Institut for Økonomi, Aarhus Universitet. CREATES Research Papers No. 2016-30

CBE

Nielsen MØ, Shibaev SS. 2016. Forecasting daily political opinion polls using the fractionally cointegrated VAR model. Aarhus: Institut for Økonomi, Aarhus Universitet.

MLA

Nielsen, Morten Ørregaard and Sergei S. Shibaev Forecasting daily political opinion polls using the fractionally cointegrated VAR model. Aarhus: Institut for Økonomi, Aarhus Universitet. (CREATES Research Papers; Journal number 2016-30). 2016., 34 p.

Vancouver

Nielsen MØ, Shibaev SS. Forecasting daily political opinion polls using the fractionally cointegrated VAR model. Aarhus: Institut for Økonomi, Aarhus Universitet. 2016 Oct 31.

Author

Nielsen, Morten Ørregaard ; Shibaev, Sergei S. / Forecasting daily political opinion polls using the fractionally cointegrated VAR model. Aarhus : Institut for Økonomi, Aarhus Universitet, 2016. (CREATES Research Papers; No. 2016-30).

Bibtex

@techreport{38649b162c4b46ce949f2e5a97e914b3,
title = "Forecasting daily political opinion polls using the fractionally cointegrated VAR model",
abstract = "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",
author = "Nielsen, {Morten {\O}rregaard} and Shibaev, {Sergei S.}",
year = "2016",
month = oct,
day = "31",
language = "English",
series = "CREATES Research Papers",
publisher = "Institut for {\O}konomi, Aarhus Universitet",
number = "2016-30",
type = "WorkingPaper",
institution = "Institut for {\O}konomi, Aarhus Universitet",

}

RIS

TY - UNPB

T1 - Forecasting daily political opinion polls using the fractionally cointegrated VAR model

AU - Nielsen, Morten Ørregaard

AU - Shibaev, Sergei S.

PY - 2016/10/31

Y1 - 2016/10/31

N2 - 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.

AB - 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.

KW - forecasting, fractional cointegration, opinion poll data, vector autoregressive model

M3 - Working paper

T3 - CREATES Research Papers

BT - Forecasting daily political opinion polls using the fractionally cointegrated VAR model

PB - Institut for Økonomi, Aarhus Universitet

CY - Aarhus

ER -