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Mixed frequency composite indicators for measuring public sentiment in the EU

Author

Listed:
  • Raffaele Mattera

    (Sapienza University of Rome
    University of Naples “Federico II”)

  • Michelangelo Misuraca

    (University of Calabria)

  • Maria Spano

    (University of Naples “Federico II”)

  • Germana Scepi

    (University of Naples “Federico II”)

Abstract

Monitoring the state of the economy in a short time is a crucial aspect for designing appropriate and timely policy responses in the presence of shocks and crises. Short-term confidence indicators can help policymakers in evaluating both the effect of policies and the economic activity condition. The indicator commonly used in the EU to evaluate the public opinion orientation is the Economic Sentiment Indicator (ESI). Nevertheless, the ESI shows some drawbacks, particularly in the adopted weighting scheme that is static and not country-specific. This paper proposes an approach to construct novel composite confidence indicators, focusing on both the weights and the information set to use. We evaluate these indicators by studying their response to the policies introduced to contain the COVID-19 pandemic in some selected EU countries. Furthermore, we carry out an experimental study where the proposed indicators are used to forecast economic activity.

Suggested Citation

  • Raffaele Mattera & Michelangelo Misuraca & Maria Spano & Germana Scepi, 2023. "Mixed frequency composite indicators for measuring public sentiment in the EU," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2357-2382, June.
    • Handle: RePEc:spr:qualqt:v:57:y:2023:i:3:d:10.1007_s11135-022-01468-9
    DOI: 10.1007/s11135-022-01468-9
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    as
    1. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    2. Grupp, Hariolf & Mogee, Mary Ellen, 2004. "Indicators for national science and technology policy: how robust are composite indicators?," Research Policy, Elsevier, vol. 33(9), pages 1373-1384, November.
    3. Panarello, Demetrio & Tassinari, Giorgio, 2022. "One year of COVID-19 in Italy: are containment policies enough to shape the pandemic pattern?," Socio-Economic Planning Sciences, Elsevier, vol. 79(C).
    4. Boumans Dorine & Garnitz Johanna, 2017. "Ifo World Economic Survey Database – An International Economic Expert Survey," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 237(1), pages 71-80, February.
    5. Robert Gausden & Mohammad S. Hasan, 2020. "Comparative performances of measures of consumer and economic sentiment in forecasting consumption: a multi-country analysis," Applied Economics, Taylor & Francis Journals, vol. 52(10), pages 1088-1104, February.
    6. Koen Decancq & María Ana Lugo, 2013. "Weights in Multidimensional Indices of Wellbeing: An Overview," Econometric Reviews, Taylor & Francis Journals, vol. 32(1), pages 7-34, January.
    7. Christian Gayer & Julien Genet, 2006. "Using factor models to construct composite indicators from BCS data - a comparison with European Commission confidence indicators," European Economy - Economic Papers 2008 - 2015 240, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    8. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
    9. Saltelli A. & Tarantola S., 2002. "On the Relative Importance of Input Factors in Mathematical Models: Safety Assessment for Nuclear Waste Disposal," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 702-709, September.
    10. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    11. Alain Kabundi & Elmarie Nel & Franz Ruch, 2016. "Nowcasting Real GDP growth in South Africa," Working Papers 7068, South African Reserve Bank.
    12. Cecilia Frale & Massimiliano Marcellino & Gian Luigi Mazzi & Tommaso Proietti, 2011. "EUROMIND: a monthly indicator of the euro area economic conditions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(2), pages 439-470, April.
    13. repec:zbw:bofrdp:2021_004 is not listed on IDEAS
    14. Massimo Stella & Emilio Ferrara & Manlio De Domenico, 2018. "Bots increase exposure to negative and inflammatory content in online social systems," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 115(49), pages 12435-12440, December.
    15. Sarah Gelper & Aurelie Lemmens & Christophe Croux, 2007. "Consumer sentiment and consumer spending: decomposing the Granger causal relationship in the time domain," Applied Economics, Taylor & Francis Journals, vol. 39(1), pages 1-11.
    16. Tim Ginker & Tanya Suhoy, 2022. "Nowcasting and Monitoring Real Economic Activity in Israel," Bank of Israel Working Papers 2022.07, Bank of Israel.
    17. Page, Benjamin I. & Shapiro, Robert Y., 1983. "Effects of Public Opinion on Policy," American Political Science Review, Cambridge University Press, vol. 77(1), pages 175-190, March.
    18. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2005. "Leading Indicators for Euro‐area Inflation and GDP Growth," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 785-813, December.
    19. Ghirelli, Corinna & Pérez, Javier J. & Urtasun, Alberto, 2019. "A new economic policy uncertainty index for Spain," Economics Letters, Elsevier, vol. 182(C), pages 64-67.
    20. Luca Zanin, 2010. "The relationship between changes in the Economic Sentiment Indicator and real GDP growth: a time-varying coefficient approach," Economics Bulletin, AccessEcon, vol. 30(1), pages 837-846.
    21. Omer Ozcicek & W. DOUGLAS McMILLIN, 1999. "Lag length selection in vector autoregressive models: symmetric and asymmetric lags," Applied Economics, Taylor & Francis Journals, vol. 31(4), pages 517-524.
    22. Karl Taylor & Robert McNabb, 2007. "Business Cycles and the Role of Confidence: Evidence for Europe," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 69(2), pages 185-208, April.
    23. Chow, Gregory C & Lin, An-loh, 1971. "Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series," The Review of Economics and Statistics, MIT Press, vol. 53(4), pages 372-375, November.
    24. Annabelle Mourougane & Moreno Roma, 2003. "Can confidence indicators be useful to predict short term real GDP growth?," Applied Economics Letters, Taylor & Francis Journals, vol. 10(8), pages 519-522.
    25. Baffigi, Alberto & Golinelli, Roberto & Parigi, Giuseppe, 2004. "Bridge models to forecast the euro area GDP," International Journal of Forecasting, Elsevier, vol. 20(3), pages 447-460.
    26. repec:zbw:bofrdp:2020_011 is not listed on IDEAS
    27. Filippo Altissimo & Riccardo Cristadoro & Mario Forni & Marco Lippi & Giovanni Veronese, 2010. "New Eurocoin: Tracking Economic Growth in Real Time," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 1024-1034, November.
    28. Ghysels, Eric & Qian, Hang, 2019. "Estimating MIDAS regressions via OLS with polynomial parameter profiling," Econometrics and Statistics, Elsevier, vol. 9(C), pages 1-16.
    29. Matteo Mazziotta & Adriano Pareto, 2018. "Measuring Well-Being Over Time: The Adjusted Mazziotta–Pareto Index Versus Other Non-compensatory Indices," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 136(3), pages 967-976, April.
    30. Caggiano, Giovanni & Castelnuovo, Efrem & Kima, Richard, 2020. "The global effects of Covid-19-induced uncertainty," Economics Letters, Elsevier, vol. 194(C).
    31. Filimonau, Viachaslau & Vi, Le Hong & Beer, Sean & Ermolaev, Vladimir A., 2022. "The Covid-19 pandemic and food consumption at home and away: An exploratory study of English households," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    32. Ardia, David & Bluteau, Keven & Boudt, Kris, 2019. "Questioning the news about economic growth: Sparse forecasting using thousands of news-based sentiment values," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1370-1386.
    33. Leonardo S. Alaimo & Filomena Maggino, 2020. "Sustainable Development Goals Indicators at Territorial Level: Conceptual and Methodological Issues—The Italian Perspective," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 147(2), pages 383-419, January.
    34. Ciaran Driver & Giovanni Urga, 2004. "Transforming Qualitative Survey Data: Performance Comparisons for the UK," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(1), pages 71-89, February.
    35. Massimiliano Marcellino & Christian Schumacher, 2010. "Factor MIDAS for Nowcasting and Forecasting with Ragged‐Edge Data: A Model Comparison for German GDP," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 518-550, August.
    36. Virmantas Kvedaras & Alfredas Račkauskas, 2010. "Regression Models with Variables of Different Frequencies: The Case of a Fixed Frequency Ratio," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(5), pages 600-620, October.
    37. Ehlert, Andree, 2021. "The socio-economic determinants of COVID-19: A spatial analysis of German county level data," Socio-Economic Planning Sciences, Elsevier, vol. 78(C).
    38. Sarah Gelper & Christophe Croux, 2010. "On the Construction of the European Economic Sentiment Indicator," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(1), pages 47-62, February.
    39. Vargas-Lopez, Adrian & Cicatiello, Clara & Principato, Ludovica & Secondi, Luca, 2022. "Consumer expenditure, elasticity and value of food waste: A Quadratic Almost Ideal Demand System for evaluating changes in Mexico during COVID-19," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    40. Ferhat Pehlivanoğlu & Saffet Akdağ & Andrew Adewale Alola, 2021. "The causal nexus of geopolitical risks, consumer and producer confidence indexes: evidence from selected economies," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(4), pages 1261-1273, August.
    41. Larsen, Vegard H. & Thorsrud, Leif A., 2019. "The value of news for economic developments," Journal of Econometrics, Elsevier, vol. 210(1), pages 203-218.
    42. Joseph E Stiglitz & Martin M Guzman, 2021. "Economic fluctuations and pseudo-wealth [Emerging market business cycles: the cycle is the trend]," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 30(2), pages 297-315.
    43. Teresa Santero & Niels Westerlund, 1996. "Confidence Indicators and Their Relationship to Changes in Economic Activity," OECD Economics Department Working Papers 170, OECD Publishing.
    44. Qiao, Zhuo & McAleer, Michael & Wong, Wing-Keung, 2009. "Linear and nonlinear causality between changes in consumption and consumer attitudes," Economics Letters, Elsevier, vol. 102(3), pages 161-164, March.
    45. Ghysels, Eric & Kvedaras, Virmantas & Zemlys, Vaidotas, 2016. "Mixed Frequency Data Sampling Regression Models: The R Package midasr," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 72(i04).
    46. Chatfield, Chris, 1992. "A commentary on error measures," International Journal of Forecasting, Elsevier, vol. 8(1), pages 100-102, June.
    47. Binderkrantz, Anne Skorkjær & Bonafont, Laura Chaqués & Halpin, Darren R., 2017. "Diversity in the News? A Study of Interest Groups in the Media in the UK, Spain and Denmark," British Journal of Political Science, Cambridge University Press, vol. 47(2), pages 313-328, April.
    48. Salvatore Greco & Alessio Ishizaka & Menelaos Tasiou & Gianpiero Torrisi, 2019. "On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 141(1), pages 61-94, January.
    49. Jaba Ghonghadze & Thomas Lux, 2012. "Modelling the dynamics of EU economic sentiment indicators: an interaction-based approach," Applied Economics, Taylor & Francis Journals, vol. 44(24), pages 3065-3088, August.
    50. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "A new approach for the quantification of qualitative measures of economic expectations," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(6), pages 2685-2706, November.
    51. M. Saisana & A. Saltelli & S. Tarantola, 2005. "Uncertainty and sensitivity analysis techniques as tools for the quality assessment of composite indicators," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(2), pages 307-323, March.
    52. Claudia Foroni & Massimiliano Marcellino & Christian Schumacher, 2015. "Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 57-82, January.
    53. Tobback, Ellen & Naudts, Hans & Daelemans, Walter & Junqué de Fortuny, Enric & Martens, David, 2018. "Belgian economic policy uncertainty index: Improvement through text mining," International Journal of Forecasting, Elsevier, vol. 34(2), pages 355-365.
    54. Marcellino, Massimiliano, 1999. "Some Consequences of Temporal Aggregation in Empirical Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 129-136, January.
    55. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    56. Gelper, S. & Lemmens, A. & Croux, C., 2007. "Consumer sentiment and consumer spending : Decomposing the granger causal relationship in the time domain," Other publications TiSEM 55ac7230-2985-41f1-a42c-7, Tilburg University, School of Economics and Management.
    57. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    58. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "A Data-Driven Approach to Construct Survey-Based Indicators by Means of Evolutionary Algorithms," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(1), pages 1-14, January.
    59. Jaba Ghonghadze & Thomas Lux, 2012. "Modelling the dynamics of EU economic sentiment indicators: an interaction-based approach," Applied Economics, Taylor & Francis Journals, vol. 44(24), pages 3065-3088, August.
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