This work is licensed under the Creative Commons Attribution 4.0 International License.
Abraham-Frois, G. (1998). Non-linear dynamics and endogenous cycles (Vol. 463). Springer Science & Business Media.Abraham-FroisG.1998463Springer Science & Business MediaSearch in Google Scholar
Beyer, J. (2012). Acting in Good time–Conceptual reflections on the Sequencing of Political reform Processes. Central European Journal of Public Policy, 6(02), 4–28.BeyerJ.2012Acting in Good time–Conceptual reflections on the Sequencing of Political reform Processes602428Search in Google Scholar
Box, G. E., & Jenkins, G. M. (1970). Time series models for forecasting and control. San Francisco.BoxG. E.JenkinsG. M.1970San FranciscoSearch in Google Scholar
Curran, T., & Singh, R. (2011). E-democracy as the future face of democracy: a case study of the 2011 Irish elections. European View, 10(1), 25–31.CurranT.SinghR.2011E-democracy as the future face of democracy: a case study of the 2011 Irish elections1012531Search in Google Scholar
Dijkstra, L., Poelman, H., & Rodríguez-Pose, A. (2020). The geography of EU discontent. Regional Studies, 54(6), 737–753.DijkstraL.PoelmanH.Rodríguez-PoseA.2020The geography of EU discontent546737753Search in Google Scholar
Dylko, I. B. (2013). On the role of technology in political communication research. Javnost-The Public, 20(1), 55–69.DylkoI. B.2013On the role of technology in political communication research2015569Search in Google Scholar
Doornik, J. A., & Ooms, M. (2003). Computational aspects of maximum likelihood estimation of autoregressive fractionally integrated moving average models. Computational Statistics & Data Analysis, 42(3), 333–348.DoornikJ. A.OomsM.2003Computational aspects of maximum likelihood estimation of autoregressive fractionally integrated moving average models423333348Search in Google Scholar
Eviews 9. (2022). Eviews 9 [Software]. Irvin, CA: Quantitative Micro Software, LLC.Eviews 92022Irvin, CAQuantitative Micro Software, LLCSearch in Google Scholar
Flores-Muñoz, F., Báez-García, A. J., & Gutiérrez-Barroso, J. (2019). Fractional differencing in stock market price and online presence of global tourist corporations. Journal of Economics, Finance and Administrative Science, 24(48), 194–204.Flores-MuñozF.Báez-GarcíaA. J.Gutiérrez-BarrosoJ.2019Fractional differencing in stock market price and online presence of global tourist corporations2448194204Search in Google Scholar
Geweke, J., & Porter-Hudak, S. (1983). The estimation and application of long memory time series models. Journal of Time Series Analysis, 4(4), 221–238.GewekeJ.Porter-HudakS.1983The estimation and application of long memory time series models44221238Search in Google Scholar
Gibson, R. K., & McAllister, I. (2011). Do online election campaigns win votes? The 2007 Australian “YouTube” election. Political Communication, 28(2), 227–244.GibsonR. K.McAllisterI.2011Do online election campaigns win votes? The 2007 Australian “YouTube” election282227244Search in Google Scholar
Gong, R. (2011). Internet politics and state media control: Candidate weblogs in Malaysia. Sociological Perspectives, 54(3), 307–328.GongR.2011Internet politics and state media control: Candidate weblogs in Malaysia543307328Search in Google Scholar
Google. (2022). Google Trends https://trends.google.com/Google2022https://trends.google.com/Search in Google Scholar
Graves, T., Gramacy, R., Watkins, N., & Franzke, C. (2017). A brief history of long memory: Hurst, Mandelbrot and the road to ARFIMA, 1951–1980. Entropy, 19(9), 437.GravesT.GramacyR.WatkinsN.FranzkeC.2017A brief history of long memory: Hurst, Mandelbrot and the road to ARFIMA, 1951–1980199437Search in Google Scholar
Granger, C. W., & Joyeux, R. (1980) An introduction to long-memory time series models and fractional differencing. Journal of Time Series Analysis, 1(1), 15–29.GrangerC. W.JoyeuxR.1980An introduction to long-memory time series models and fractional differencing111529Search in Google Scholar
Huberty, M. (2015). Can we vote with our tweet? On the perennial difficulty of election forecasting with social media. International Journal of Forecasting, 31(3), 992–1007.HubertyM.2015Can we vote with our tweet? On the perennial difficulty of election forecasting with social media3139921007Search in Google Scholar
Hutter, S., & Kriesi, H. (Eds.). (2019). European party politics in times of crisis. Cambridge University Press.HutterS.KriesiH.(Eds.).2019Cambridge University PressSearch in Google Scholar
Hurst, H. E. (1951) Long-term storage capacity of reservoirs. Trans. Amer. Soc. Civil Eng., 116, 770–808.HurstH. E.1951Long-term storage capacity of reservoirs116770808Search in Google Scholar
Jun, S. P., Yoo, H. S., & Choi, S. (2018). Ten years of research change using Google Trends: From the perspective of big data utilizations and applications. Technological forecasting and social change, 130, 69–87.JunS. P.YooH. S.ChoiS.2018Ten years of research change using Google Trends: From the perspective of big data utilizations and applications1306987Search in Google Scholar
Knobloch-Westerwick, S., Johnson, B. K., & Westerwick, A. (2014). Confirmation bias in online searches: Impacts of selective exposure before an election on political attitude strength and shifts. Journal of Computer-Mediated Communication, 20(2), 171–187.Knobloch-WesterwickS.JohnsonB. K.WesterwickA.2014Confirmation bias in online searches: Impacts of selective exposure before an election on political attitude strength and shifts202171187Search in Google Scholar
Lorenz, E.N. (1963). Deterministic non-periodic flow. Journal of Atmospheric Sciences, 20, 130–41.LorenzE.N.1963Deterministic non-periodic flow2013041Search in Google Scholar
Mandelbrot, B. B. (1983) The fractal geometry of nature: Revised and enlarged edition. New York: WH Freeman and Co.MandelbrotB. B.1983New YorkWH Freeman and Co.Search in Google Scholar
Magalhães, P. C. (2014). Introduction–financial crisis, austerity, and electoral politics. Journal of Elections, Public Opinion & Parties, 24(2), 125–133.MagalhãesP. C.2014Introduction–financial crisis, austerity, and electoral politics242125133Search in Google Scholar
Mellon, J. (2013). Where and when can we use Google Trends to measure issue salience?. PS: Political Science & Politics, 46(2), 280–290.MellonJ.2013Where and when can we use Google Trends to measure issue salience?462280290Search in Google Scholar
Milan, S. (2015). Mobilizing in times of social media. From a politics of identity to a politics of visibility. In Critical Perspectives on Social Media and Protest (pp. 53–71), Rowman & Littlefield.MilanS.2015Mobilizing in times of social media. From a politics of identity to a politics of visibilityIn5371Rowman & LittlefieldSearch in Google Scholar
Mora Rodríguez, M., Flores Muñoz, F., & Valentinetti, D. (2021). Corporate impact of carbon disclosures: a nonlinear empirical approach. Journal of Financial Reporting and Accounting, 19(1), 4–27, in which the concept of structural breakpoint in time series analysis is highlighted.Mora RodríguezM.Flores MuñozF.ValentinettiD.2021Corporate impact of carbon disclosures: a nonlinear empirical approach191427in which the concept of structural breakpoint in time series analysis is highlightedSearch in Google Scholar
Olds, C. (2013). Assessing presidential agenda-setting capacity: dynamic comparisons of presidential, mass media, and public attention to economic issues. Congress & the Presidency, 40(3), 255–284.OldsC.2013Assessing presidential agenda-setting capacity: dynamic comparisons of presidential, mass media, and public attention to economic issues403255284Search in Google Scholar
Reilly, S., Richey, S., & Taylor, J. B. (2012). Using google search data for state politics research: an empirical validity test using roll-off data. State Politics & Policy Quarterly, 12(2), 146–159ReillyS.RicheyS.TaylorJ. B.2012Using google search data for state politics research: an empirical validity test using roll-off data122146159Search in Google Scholar
Park, H. W. (2012). How do social scientists use link data from search engines to understand Internet-based political and electoral communication?. Quality & Quantity, 46(2), 679–693.ParkH. W.2012How do social scientists use link data from search engines to understand Internet-based political and electoral communication?462679693Search in Google Scholar
Serricchio, F., Tsakatika, M., & Quaglia, L. (2013). Euroscepticism and the global financial crisis. JCMS: Journal of Common Market Studies, 51(1), 51–64.SerricchioF.TsakatikaM.QuagliaL.2013Euroscepticism and the global financial crisis5115164Search in Google Scholar
Sowell, F. (1992). Maximum likelihood estimation of stationary univariate fractionally integrated time series models. Journal of Econometrics, 53(1–3), 165–188.SowellF.1992Maximum likelihood estimation of stationary univariate fractionally integrated time series models531–3165188Search in Google Scholar
Urman, A., Makhortykh, M., & Ulloa, R. (2022). The matter of chance: auditing web search results related to the 2020 US presidential primary elections across six search engines. Social Science Computer Review, 40(5), 1323–1339.UrmanA.MakhortykhM.UlloaR.2022The matter of chance: auditing web search results related to the 2020 US presidential primary elections across six search engines40513231339Search in Google Scholar
Utych, S. M., & Kam, C. D. (2013). Viability, information seeking, and vote choice. The Journal of Politics, 76(1), 152–166.UtychS. M.KamC. D.2013Viability, information seeking, and vote choice761152166Search in Google Scholar
Wagner, K. M., & Gainous, J. (2009). Electronic grassroots: Does online campaigning work?. The Journal of Legislative Studies, 15(4), 502–520.WagnerK. M.GainousJ.2009Electronic grassroots: Does online campaigning work?154502520Search in Google Scholar
Yasseri, T., & Bright, J. (2014). Can electoral popularity be predicted using socially generated big data? it-Information Technology, 56(5), 246–253.YasseriT.BrightJ.2014Can electoral popularity be predicted using socially generated big data?565246253Search in Google Scholar