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The MIMIC–CUB Model for the Prediction of the Economic Public Opinions in Europe

Abstract

To study the Europeans’ perception on the economic conditions, a model that combine Multiple Indicators Multiple Causes (MIMIC) and Combination of Uniform and shifted Binomial (CUB) is proposed. The MIMIC–CUB Model, estimated at country-level using the Partial Least Squares, specifies the influence of the economic forecast news on a latent variable named “Citizens’ perception of the European economics health state”. The survey is related, at both national and EU level, to the period 2005–2014.

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Source: European Commission—European Economic Forecast Report, Winter 2013

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Notes

  1. We used the R package PLS-PM ver. 0.4.1 by Sanchez, Trinchera and Russolillo, available online at the url: https://cran.r-project.org/web/packages/plspm/index.html.

  2. In our study we use the R functions CUB Models INFERENCE ver. 3.0 by Iannario and Piccolo, available online at the url: www.labstat.it/home/research/resourses/cub-data-sets-2/.

  3. The full index of European Economic Forecasts is available at: http://ec.europa.eu/economy_finance/publications/european_economy/forecasts_en.htm.

  4. Available at the url: http://ec.europa.eu/public_opinion/cf/index_en.cfm.

References

  • Antonides, G., & Van Der Sar, N. L. (1990). Individual expectations, risk perception and preferences in relation to investment decision making. Journal of Economic Psychology, 11(2), 227–245.

    Google Scholar 

  • Bertaccini, B., Grilli, L., & Rampichini, C. (2013). An IRT-MIMIC Model for the analysis of university student careers. Journal of Methodological and Applied Statistics, 15, 95–110.

    Google Scholar 

  • Bottazzi, L., Da Rin, M., & Hellmann, T. (2016). The importance of trust for investment: Evidence from venture capital. Review of Financial Studies, 29, 2283–2318.

    Google Scholar 

  • Braun, D., & Tausendpfund, M. (2014). The impact of the euro crisis on citizens support for the european union. Journal of European Integration, 36(3), 231–245.

    Google Scholar 

  • Callens, M. (2017). Long term trends in life satisfaction, 1973–2012: Flanders in europe. Social Indicators Research, 130(1), 107–127.

    Google Scholar 

  • Carpita, M., & Manisera, M. (2012). Constructing indicators of unobservable variables from parallel measurements. Electronic Journal of Applied Statistical Analysis, 5(3), 320–326.

    Google Scholar 

  • Ciavolino, E. (2012). General distress as second-order latent variable estimated through PLS-PM approach. Electronic Journal of Applied Statistical Analysis, 5(3), 458–464.

    Google Scholar 

  • Ciavolino, E., & Al-Nasser, A. D. (2009). Comparing generalised maximum entropy and partial least squares methods for structural equation models. Journal of Nonparametric Statistics, 21(8), 1017–1036.

    Google Scholar 

  • Ciavolino, E., Carpita, M., & Al-Nasser, A. (2015a). Modelling the quality of work in the Italian social co-operatives combining NPCA-RSM and SEM-GME approaches. Journal of Applied Statistics, 42(1), 161–179.

    Google Scholar 

  • Ciavolino, E., Carpita, M., & Nitti, M. (2015b). High-order PLS path model with qualitative external information. Quality & Quantity, 49(4), 1609–1620.

    Google Scholar 

  • Ciavolino, E., & Nitti, M. (2013a). Simulation study for PLS path modelling with high-order construct: A job satisfaction model evidence. In A. N. Proto, M. Squillante, & J. Kacprzyk (Eds.), Advanced dynamic modeling of economic and social systems (pp. 185–207). Berlin: Springer.

    Google Scholar 

  • Ciavolino, E., & Nitti, M. (2013b). Using the hybrid two-step estimation approach for the identification of second-order latent variable models. Journal of Applied Statistics, 40(3), 508–526.

    Google Scholar 

  • Colombi, R., & Giordano, S. (2016). A class of mixture models for multidimensional ordinal data. Statistical Modelling, 16(4), 322–340.

    Google Scholar 

  • Corduas, M. (2014). Analyzing bivariate ordinal data with cub margins. Statistical Modelling,. https://doi.org/10.1177/1471082X14558770.

    Article  Google Scholar 

  • D’Elia, A., & Piccolo, D. (2005). A mixture model for preference data analysis. Computational Statistics and Data Analysis, 49, 917–934.

    Google Scholar 

  • Diamantopoulos, A., Riefler, P., & Roth, K. P. (2008). Advancing formative measurement models. Journal of Business Research, 61(12), 1203–1218.

    Google Scholar 

  • Diamantopoulos, A., & Winklhofer, H. M. (2001). Index construction with formative indicators: An alternative to scale development. Journal of Marketing Research, 38(2), 269–277.

    Google Scholar 

  • Dickes, P., Fusco, A., & Marlier, E. (2010). Structure of national perceptions of social needs across EU countries. Social Indicators Research, 95(1), 143.

    Google Scholar 

  • Djankov, S., Nikolova, E., & Zilinsky, J. (2016). The happiness gap in eastern europe. Journal of Comparative Economics, 44(1), 108–124.

    Google Scholar 

  • Edwards, J. R., & Bagozzi, R. P. (2000). On the nature and direction of relationships between constructs and measures. Psychological Methods, 5(2), 155.

    Google Scholar 

  • Esposito Vinzi, V., Chin, W. W., Henseler, J., & Wang, H. (2010). Handbook of partial least squares. Berlin: Springer.

    Google Scholar 

  • European Commission. (2014). Public opinion in the European Union. Standard Eurobarometer 81, Spring. European Commission. http://ec.europa.eu/public_opinion/cf/index_en.cfm. Accessed Oct 2015.

  • Finch, W. H., & French, B. F. (2011). Estimation of MIMIC Model parameters with multilevel data. Structural Equation Modeling, 18(2), 229–252.

    Google Scholar 

  • Gabel, M., & Palmer, H. D. (1995). Understanding variation in public support for european integration. European Journal of Political Research, 27(1), 3–19.

    Google Scholar 

  • Gifi, A. (1990). Nonlinear multivariate analysis. Chichester: Wiley.

    Google Scholar 

  • Goldberger, A. S. (1972). Structural equation methods in the social sciences. Econometrica: Journal of the Econometric Society, 40(6), 979–1001.

    Google Scholar 

  • Golia, S. (2015). On the interpretation of the uncertainty parameter in CUB Models. Electronic Journal of Applied Statistical Analysis, 8(3), 312–328.

    Google Scholar 

  • Grilli, L., Iannario, M., Piccolo, D., & Rampichini, C. (2014). Latent class CUB Models. Advances in Data Analysis and Classification, 8(1), 105–119.

    Google Scholar 

  • Hand, D. J., & Crowder, M. J. (2005). Measuring customer quality in retail banking. Statistical Modelling, 5(2), 145–158.

    Google Scholar 

  • Havasi, V. (2013). Financial situation and its consequences on the quality of life in the EU countries. Social Indicators Research, 113(1), 17–35.

    Google Scholar 

  • Hester, J. B., & Gibson, R. (2003). The economy and second-level agenda setting: A time-series analysis of economic news and public opinion about the economy. Journalism & Mass Communication Quarterly, 80(1), 73–90.

    Google Scholar 

  • Hetherington, M. J. (1996). The media’s role in forming voters’ national economic evaluations in 1992. American Journal of Political Science, 40, 372–395.

    Google Scholar 

  • Hobolt, S. B., & de Vries, C. E. (2016). Public support for european integration. Annual Review of Political Science, 19, 413–432.

    Google Scholar 

  • Hovi, M., & Laamanen, J.-P. (2016). Mind the gap? Business cycles and subjective well-being. Applied Economics Letters,23, 1206–1209.

    Google Scholar 

  • Iannario, M. (2012). Modelling shelter choices in a class of mixture models for ordinal responses. Statistical Methods and Applications, 20, 1–22.

    Google Scholar 

  • Iannario, M. (2014). Modelling uncertainty and overdispersion in ordinal data. Communications in Statistics-Theory and Methods, 43(4), 771–786.

    Google Scholar 

  • Iannario, M., & Piccolo, D. (2012). CUB Models: Statistical methods and empirical evidence. In R. S. Kenett & S. Salini (Eds.), Modern analysis of customer surveys (pp. 231–258). New York: Wiley.

    Google Scholar 

  • Jarvis, C. B., MacKenzie, S. B., & Podsakoff, P. M. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30(2), 199–218.

    Google Scholar 

  • Jöreskog, K. G., & Goldberger, A. S. (1975). Estimation of a model with multiple indicators and multiple causes of a single latent variable. Journal of the American Statistical Association, 70(351a), 631–639.

    Google Scholar 

  • Kim, E. S., Yoon, M., Wen, Y., Luo, W., & Kwok, O.-M. (2015). Within-level group factorial invariance with multilevel data: Multilevel factor mixture and multilevel MIMIC Models. Structural Equation Modeling: A Multidisciplinary Journal, 22(4), 603–616.

    Google Scholar 

  • Krishnakumar, J., & Nagar, A. L. (2008). On exact statistical properties of multidimensional indices based on principal components, factor analysis, mimic and structural equation models. Social Indicators Research, 86(3), 481–496.

    Google Scholar 

  • Lee, N., Cadogan, J. W., & Chamberlain, L. (2013). The MIMIC Model and formative variables: Problems and solutions. AMS Review, 3(1), 3–17.

    Google Scholar 

  • MacKenzie, S. B., Podsakoff, P. M., & Jarvis, C. B. (2005). The problem of measurement model misspecification in behavioral and organizational research and some recommended solutions. Journal of Applied Psychology, 90(4), 710.

    Google Scholar 

  • Maltritz, D., Buehn, A., & Eichler, S. (2012). Modelling country default risk as a latent variable: A multiple indicators multiple causes approach. Applied Economics, 44(36), 4679–4688.

    Google Scholar 

  • Manisera, M., & Zuccolotto, P. (2014). Modeling rating data with Nonlinear CUB Models. Computational Statistics & Data Analysis, 78, 100–118.

    Google Scholar 

  • Manisera, M., & Zuccolotto, P. (2015). Visualizing multiple results from nonlinear CUB Models with r grid viewports. Electronic Journal of Applied Statistical Analysis, 8(3), 360–373.

    Google Scholar 

  • Moustaki, I., & Steele, F. (2005). Latent variable models for mixed categorical and survival responses, with an application to fertility preferences and family planning in Bangladesh. Statistical Modelling, 5(4), 327–342.

    Google Scholar 

  • Nissen, S. (2014). The eurobarometer and the process of European integration. Quality & Quantity, 48(2), 713–727.

    Google Scholar 

  • Nitti, M., & Ciavolino, E. (2014). A deflated indicators approach for estimating second-order reflective models through PLS-PM: An empirical illustration. Journal of Applied Statistics, 41(10), 2222–2239.

    Google Scholar 

  • Oberski, D., & Vermunt, J. (2015). The CUB Model and its variations are restricted loglinear latent class models. Electronic Journal of Applied Statistical Analysis, 8(6), 374–383.

    Google Scholar 

  • Pruitt, S. W., & Hoffer, G. E. (1989). Economic news as a consumer product: An analysis of the effects of alternative media sources on the formation of consumer economic expectations. Journal of Consumer Policy, 12(1), 59–69.

    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.

    Google Scholar 

  • Skrondal, A., & Rabe-Hesketh, S. (2004). Generalized latent variable modeling: Multilevel, longitudinal, and structural equation models. Boca Raton: CRC Press.

    Google Scholar 

  • Stapleton, D. C. (1978). Analyzing political participation data with a MIMIC Model. Sociological Methodology, 9, 52–74.

    Google Scholar 

  • Tekwe, C. D., Carter, R. L., & Cullings, H. M. (2016). Generalized multiple indicators, multiple causes measurement error models. Statistical Modelling, 16(2), 140–159.

    Google Scholar 

  • Wold, H. (1975). Path models with latent variables: The NIPALS approach. In Blalock, H. M. (Ed.), Quantitative Sociology (pp. 307–357). New York: Academic Press, INC.

    Google Scholar 

  • Woods, C. M. (2009). Evaluation of MIMIC-Model methods for DIF testing with comparison to two-group analysis. Multivariate Behavioral Research, 44(1), 1–27.

    Google Scholar 

  • Yang, C.-C. (2005). MIMIC latent class analysis model for alcoholic diagnosis. Structural Equation Modeling, 12(1), 130–147.

    Google Scholar 

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Funding

Funding was provided by Seventh Framework Programme (Grant No. 320270).

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Correspondence to Maurizio Carpita.

Additional information

This study is part of the European Project SYRTO (Systemic Risk Tomography; syrtoproject.eu), which aims to create an early warning system to identify potential threats to financial stability and to inform policy measures in order to prevent and manage systemic crises in the Eurozone.

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Carpita, M., Ciavolino, E. & Nitti, M. The MIMIC–CUB Model for the Prediction of the Economic Public Opinions in Europe. Soc Indic Res 146, 287–305 (2019). https://doi.org/10.1007/s11205-018-1885-4

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  • DOI: https://doi.org/10.1007/s11205-018-1885-4

Keywords

  • MIMIC Model
  • CUB Model
  • Macro-economic indicators
  • Eurobarometer public opinion survey
  • Economic crisis