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Moving up. Applying aggregate level time series analysis in the study of media coverage

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  • Rens Vliegenthart

Abstract

In this article the advantages of aggregate level time series analysis for the study of media coverage are discussed. This type of analysis offers the opportunity to answer questions relating to causes and effects of media attention for issues and all kind of other content characteristics. Data that ask for a time series approach have become widely available during the past years, due to the rise of digital archives and social media such as Twitter and Facebook. This type of analysis allows for answering a set of interesting research questions and strong inferences about causal processes. Common challenges in time series analysis, relating to stationarity, accounting for a series’ past and autoregressive conditional heteroscedasticity are discussed. Two useful approaches, ARIMA and VAR, are introduced stepwise. An empirical example, dealing with intermedia agenda-setting between different newspapers in the Netherlands, demonstrates how both techniques can be applied and how they provide insightful answers to interesting research problems. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Rens Vliegenthart, 2014. "Moving up. Applying aggregate level time series analysis in the study of media coverage," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(5), pages 2427-2445, September.
    • Handle: RePEc:spr:qualqt:v:48:y:2014:i:5:p:2427-2445
    DOI: 10.1007/s11135-013-9899-0
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    References listed on IDEAS

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    1. Van Noije, Lonneke & Kleinnijenhuis, Jan & Oegema, Dirk, 2008. "Loss of Parliamentary Control Due to Mediatization and Europeanization: A Longitudinal and Cross-Sectional Analysis of Agenda Building in the United Kingdom and the Netherlands," British Journal of Political Science, Cambridge University Press, vol. 38(3), pages 455-478, July.
    2. Wisniewski, Tomasz Piotr & Lambe, Brendan, 2013. "The role of media in the credit crunch: The case of the banking sector," Journal of Economic Behavior & Organization, Elsevier, vol. 85(C), pages 163-175.
    3. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
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    5. Wilson, Sven E. & Butler, Daniel M., 2007. "A Lot More to Do: The Sensitivity of Time-Series Cross-Section Analyses to Simple Alternative Specifications," Political Analysis, Cambridge University Press, vol. 15(2), pages 101-123, April.
    6. Hollanders, David & Vliegenthart, Rens, 2011. "The influence of negative newspaper coverage on consumer confidence: The Dutch case," Journal of Economic Psychology, Elsevier, vol. 32(3), pages 367-373, June.
    7. Suzanna De Boef & Luke Keele, 2008. "Taking Time Seriously," American Journal of Political Science, John Wiley & Sons, vol. 52(1), pages 184-200, January.
    8. Kramer, Gerald H., 1983. "The Ecological Fallacy Revisited: Aggregate- versus Individual-level Findings on Economics and Elections, and Sociotropic Voting," American Political Science Review, Cambridge University Press, vol. 77(1), pages 92-111, March.
    9. repec:bla:econom:v:47:y:1980:i:188:p:387-406 is not listed on IDEAS
    10. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Cited by:

    1. Anne Cornelia Kroon & Damian Trilling & Martine Selm & Rens Vliegenthart, 2019. "Biased media? How news content influences age discrimination claims," European Journal of Ageing, Springer, vol. 16(1), pages 109-119, March.

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