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A Data-Driven Approach to Construct Survey-Based Indicators by Means of Evolutionary Algorithms

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Abstract

In this paper we propose a data-driven approach for the construction of survey-based indicators using large data sets. We make use of agents’ expectations about a wide range of economic variables contained in the World Economic Survey, which is a tendency survey conducted by the Ifo Institute for Economic Research. By means of genetic programming we estimate a symbolic regression that links survey-based expectations to a quantitative variable used as a yardstick, deriving mathematical functional forms that approximate the target variable. We use the evolution of GDP as a target. This set of empirically-generated indicators of economic growth, are used as building blocks to construct an economic indicator. We compare the proposed indicator to the Economic Climate Index, and we evaluate its predictive performance to track the evolution of the GDP in ten European economies. We find that in most countries the proposed indicator outperforms forecasts generated by a benchmark model.

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References

  • Abberger, K. (2007). Qualitative business surveys and the assessment of employment—A case study for Germany. International Journal of Forecasting, 23(2), 249–258.

    Article  Google Scholar 

  • Acosta-González, E., Fernández, F., & Sosvilla, S. (2012). On factors explaining the 2008 financial crisis. Economics Letters, 115(2), 215–217.

    Article  Google Scholar 

  • Altug, S., & Çakmakli, C. (2016). Forecasting inflation using survey expectations and target inflation: Evidence from Brazil and Turkey. International Journal of Forecasting, 32(1), 138–153.

    Article  Google Scholar 

  • Álvarez-Díaz, M., & Álvarez, A. (2005). Genetic multi-model composite forecast for non-linear prediction of exchange rates. Empirical Economics, 30(3), 643–663.

    Article  Google Scholar 

  • Anderson, O. (1951). Konjunkturtest und Statistik. Allgemeines Statistical Archives, 35, 209–220.

    Google Scholar 

  • Anderson, O. (1952). The Business Test of the IFO-Institute for Economic Research, Munich, and its theoretical model. Revue de l’Institut International de Statistique, 20, 1–17.

    Article  Google Scholar 

  • Batchelor, R. A. (1982). Expectations, output and inflation: The European experience. European Economic Review, 17(1), 1–25.

    Article  Google Scholar 

  • Batchelor, R. A. (1986). Quantitative v. qualitative measures of inflation expectations. Oxford Bulletin of Economics and Statistics, 48(2), 99–120.

    Article  Google Scholar 

  • Batchelor, R., & Dua, P. (1992). Survey expectations in the time series consumption function. The Review of Economics and Statistics, 74(4), 598–606.

    Article  Google Scholar 

  • Batchelor, R., & Dua, P. (1998). Improving macro-economic forecasts. International Journal of Forecasting, 14(1), 71–81.

    Article  Google Scholar 

  • Batchelor, R., & Orr, A. B. (1988). Inflation expectations revisited. Economica, 55(2019), 317–331.

    Article  Google Scholar 

  • Berk, J. M. (1999). Measuring inflation expectations: A survey data approach. Applied Economics, 31(11), 1467–1480.

    Article  Google Scholar 

  • Białowolski, P. (2015). Concepts of confidence in tendency survey research: An assessment with multi-group confirmatory factor analysis. Social Indicators Research, 123(1), 281–302.

    Article  Google Scholar 

  • Białowolski, P. (2016). The influence of negative response style on survey-based household inflation expectations. Quality & Quantity, 50(2), 509–528.

    Article  Google Scholar 

  • Biart, M., & Praet, P. (1987). The contribution of opinion surveys in forecasting aggregate demand in the four main EC countries. Journal of Economic Psychology, 8(4), 409–428.

    Article  Google Scholar 

  • Breitung, J., & Schmeling, M. (2013). Quantifying survey expectations: What’s wrong with the probability approach? International Journal of Forecasting, 29(1), 142–154.

    Article  Google Scholar 

  • Camacho, M., & Perez-Quiros, G. (2010). Introducing the Euro-Sting: Short-term indicator of Euro Area growth. Journal of Applied Econometrics, 25(4), 663–694.

    Article  Google Scholar 

  • Carlson, J. A., & Parkin, M. (1975). Inflation expectations. Economica, 42(166), 123–138.

    Article  Google Scholar 

  • CESifo World Economic Survey. (2011). Vol. 10(2), May 2011.

  • Claveria, O. (2010). Qualitative survey data on expectations. Is there an alternative to the balance statistic? In A. T. Molnar (Ed.), Economic forecasting (pp. 181–190). Hauppauge, NY: Nova Science.

    Google Scholar 

  • Claveria, O., Monte, E., & Torra, S. (2016). Quantification of survey expectations by means of symbolic regression via genetic programming to estimate economic growth in Central and Eastern European economies. Eastern European Economics, 54(2), 171–189.

    Article  Google Scholar 

  • Claveria, O., Pons, E., & Ramos, R. (2007). Business and consumer expectations and macroeconomic forecasts. International Journal of Forecasting, 23(1), 47–69.

    Article  Google Scholar 

  • Cotsomitis, J. A., & Kwan, A. C. C. (2006). Can consumer confidence forecast house-hold spending? Evidence from the European Commission business and consumer surveys. Southern Economic Journal, 73(3), 597–610.

    Article  Google Scholar 

  • Cramer, N. (1985). A representation for the adaptive generation of simple sequential programs. Proceedings of the International Conference on Genetic Algorithms and their Applications, 24–26 June. Pittsburgh, PA.

  • Dabhi, V. K., & Chaudhary, S. (2015). Empirical modeling using genetic programming: A survey of issues and approaches. Natural Computing, 14(2), 303–330.

    Article  Google Scholar 

  • Dasgupta, S., & Lahiri, K. (1992). A comparative study of alternative methods of quantifying qualitative survey responses using NAPM data. Journal of Business and Economic Statistics, 10(4), 391–400.

    Google Scholar 

  • Dees, S., & Brinca, P. S. (2013). Consumer confidence as a predictor of consumption spending: Evidence for the United States and the Euro area. International Economics, 134, 1–14.

    Article  Google Scholar 

  • Driver, C., & Urga, G. (2004). Transforming Qualitative Survey Data: Performance Comparisons for the UK. Oxford Bulletin of Economics and Statistics, 66(1), 71–89.

    Article  Google Scholar 

  • European Commission. (2014). The Joint Harmonised EU Programme of Business and Consumer Surveys. A user manual to the Joint Harmonised EU Programme of Business and Consumers Surveys. Brussels: European Commission, DG Economic and Financial Affairs.

    Google Scholar 

  • Fogel, D. B. (2006). Evolutionary computation. Toward a new philosophy of machine intelligence (Third Edition). John Wiley & Sons: Hoboken, NJ.

  • Fortin, F. A., De Rainville, F. M., Gardner, M. A., Parizeau, M., & Gagné, C. (2012). DEAP: Evolutionary algorithms made easy. Journal of Machine Learning Research, 13(1), 2171–2175.

    Google Scholar 

  • Frale, C., Marcellino, M., Mazzi, G. L., & Proietti, T. (2010). Survey data as coincident or leading indicators. Journal of Forecasting, 29(1–2), 109–131.

    Article  Google Scholar 

  • Franses, P. H., Kranendonk, H. C., & Lanser, D. (2011). One model and various experts: Evaluating Dutch macroeconomic forecasts. International Journal of Forecasting, 27(2), 482–495.

    Article  Google Scholar 

  • Garnitz, J., Nerb, G., & Wohlrabe, K. (2015). CESifo World Economic Survey—November 2015. CESifo World Economic Survey, 14(4), 1–28.

    Google Scholar 

  • Gelper, S., & Christophe, C. (2010). On the construction of the European Economic Sentiment Indicator. Oxford Bulletin of Economics and Statistics, 72(1), 47–62.

    Article  Google Scholar 

  • Gelper, S., & Croux, C. (2007). The predictive power of the European Economic Sentiment Indicator. KBI Working paper 0707, University of Leuven, Leuven.

  • Gelper, S., & Croux, C. (2010). On the construction of the European economic sentiment indicator. Oxford Bulletin for Economics and Statistics, 72(1), 47–62.

    Article  Google Scholar 

  • Gelper, S., Lemmens, A., & Croux, C. (2007). Consumer sentiment and consumer spending: Decomposing the Granger causal relationship in the time domain. Applied Economics, 39(1), 1–11.

    Article  Google Scholar 

  • Ghonghadze, J., & Lux, T. (2012). Modelling the dynamics of EU economic sentiment indicators: An interaction-based approach. Applied Economics, 44(24), 3065–3088.

    Article  Google Scholar 

  • Girardi, A. (2014). Expectations and macroeconomic fluctuations in the euro area. Economics Letters, 125(2), 315–318.

    Article  Google Scholar 

  • Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Reading Boston, MA: Addison-Wesley.

    Google Scholar 

  • Gong, Y. J., Chen, W. N., Zhan, Z. H., Zhang, J., Li, Y., Zhang, Q., et al. (2015). Distributed evolutionary algorithms and their models: A survey of the stat-of-the-art. Applied Soft Computing, 34, 286–300.

    Article  Google Scholar 

  • Graff, M. (2010). Does a multi-sectoral design improve indicator-based forecasts of the GDP growth rate? Evidence from Switzerland. Applied Economics, 42(21), 2759–2781.

    Article  Google Scholar 

  • Guizzardi, A., & Stacchini, A. (2015). Real-time forecasting regional tourism with business sentiment surveys. Tourism Management, 47, 213–223.

    Article  Google Scholar 

  • Hansson, J., Jansson, P., & Löf, M. (2005). Business survey data: Do they help in forecasting GDP growth? International Journal of Forecasting, 30(1), 65–77.

    Google Scholar 

  • Henzel, S., & Wollmershäuser, T. (2005). An alternative to the Carlson–Parkin method for the quantification of qualitative inflation expectations: Evidence from the Ifo World Economic Survey. Journal of Business Cycle Measurement and Analysis, 2(3), 321–352.

    Google Scholar 

  • Holland, J. H. (1975). Adaptation in natural and artificial systems. Ann Arbor, MI: University of Michigan Press.

    Google Scholar 

  • Hutson, M., Joutz, F., & Stekler, H. (2014). Interpreting and evaluating CESIfo’s World Economic Survey directional forecasts. Economic Modelling, 38, 6–11.

    Article  Google Scholar 

  • Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688.

    Article  Google Scholar 

  • Ilmakunnas, P. (1989). Survey expectations vs. rational expectations in the estimation of a dynamic model: demand for labour in Finish manufacturing. Oxford Bulletin of Economics and Statistics, 51(3), 297–314.

    Article  Google Scholar 

  • Ivaldi, M. (1992). Survey evidence on the rationality of expectations. Journal of Applied Econometrics, 7(1), 225–241.

    Article  Google Scholar 

  • Jean-Baptiste, F. (2012). Forecasting with the new Keynesian Phillips curve: Evidence from survey data. Economics Letters, 117(3), 811–813.

    Article  Google Scholar 

  • Jonsson, T., & Österholm, P. (2011). The forecasting properties of survey-based wage-growth expectations. Economics Letters, 113(3), 276–281.

    Article  Google Scholar 

  • Jonsson, T., & Österholm, P. (2012). The properties of survey-based inflation expectations in Sweden. Empirical Economics, 42(1), 79–94.

    Article  Google Scholar 

  • Kariya, T. (1990). A generalization of the Carlson–Parkin method for the estimation of expected inflation rate. The Economic Studies Quarterly, 41(2), 155–165.

    Google Scholar 

  • Kauppi, E., Lassila, J., & Teräsvirta, T. (1996). Short-term forecasting of industrial production with business survey data: Experience from Finland’s great depression 1990–1993. International Journal of Forecasting, 12(3), 373–381.

    Article  Google Scholar 

  • Klein, L. R., & Özmucur, S. (2010). The use of consumer and business surveys in forecasting. Economic Modelling, 27(6), 1453–1462.

    Article  Google Scholar 

  • Kľúčik, M. (2012). Estimates of foreign trade using genetic programming. In Proceedings of the 46 the scientific meeting of the Italian Statistical Society.

  • Kotanchek, M. E., Vladislavleva, E. Y., & Smits, G. F. (2010). Symbolic regression via genetic programming as a discovery engine: Insights on outliers and prototypes. In R. Riolo, et al. (Eds.), Genetic programming theory and practice VII, genetic and evolutionary computation (Vol. 8, pp. 55–72). Berlin: Springer Science + Business Media LLC.

    Google Scholar 

  • Koza, J. R. (1992). Genetic programming: On the programming of computers by means of natural selection. Cambridge, MA: MIT Press.

    Google Scholar 

  • Kronberger, G., Fink, S., Kommenda, M., & Affenzeller, M. (2011). Macro-economic time series modeling and interaction networks. In C. Di Chio et al. (Eds.), EvoApplications, Part II (pp. 101–110). LNCS 6625.

  • Kudymowa, E., Plenk, J., & Wohlrabe, K. (2013). Ifo World Economic Survey and the business cycle in selected countries. CESifo Forum, 14(4), 51–57.

    Google Scholar 

  • Lahiri, K., & Zhao, Y. (2015). Quantifying survey expectations: A critical review and generalization of the Carlson–Parkin method. International Journal of Forecasting, 31(1), 51–62.

    Article  Google Scholar 

  • Leduc, S., & Sill, K. (2013). Expectations and economic fluctuations: An analysis using survey data. The Review of Economic and Statistics, 95(4), 1352–1367.

    Article  Google Scholar 

  • Lemmens, A., Croux, C., & Dekimpe, M. G. (2005). On the predictive content of production surveys: A pan-European study. International Journal of Forecasting, 21(2), 363–375.

    Article  Google Scholar 

  • Lemmens, A., Croux, C., & Dekimpe, M. G. (2008). Measuring and testing Granger causality over the spectrum: An application to European production expectation surveys. International Journal of Forecasting, 24(3), 414–431.

    Article  Google Scholar 

  • Löffler, G. (1999). Refining the Carlson–Parkin method. Economics Letters, 64(2), 167–171.

    Article  Google Scholar 

  • Lui, S., Mitchell, J., & Weale, M. (2011a). The utility of expectational data: Firm-level evidence using matched qualitative-quantitative UK surveys. International Journal of Forecasting, 27(4), 1128–1146.

    Article  Google Scholar 

  • Lui, S., Mitchell, J., & Weale, M. (2011b). Qualitative business surveys: Signal or noise? Journal of The Royal Statistical Society, Series A (Statistics in Society), 174(2), 327–348.

    Article  Google Scholar 

  • Łyziak, T., & Mackiewicz-Łyziak, J. (2014). Do consumers in Europe anticipate future inflation? Eastern European Economics, 52(3), 5–32.

    Article  Google Scholar 

  • Martinsen, K., Ravazzolo, F., & Wulfsberg, F. (2014). Forecasting macroeconomic variables using disaggregate survey data. International Journal of Forecasting, 30(1), 65–77.

    Article  Google Scholar 

  • Mitchell, J. (2002). The use of non-normal distributions in quantifying qualitative survey data on expectations. Economics Letters, 76(1), 101–107.

    Article  Google Scholar 

  • Mitchell, J., Smith, R., & Weale, M. (2002). Quantification of qualitative firm-level survey data. Economic Journal, 112(478), 117–135.

    Article  Google Scholar 

  • Mitchell, J., Smith, R., & Weale, M. (2005a). Forecasting manufacturing output growth using firm-level survey data. The Manchester School, 73(4), 479–499.

    Article  Google Scholar 

  • Mitchell, J., Smith, R., & Weale, M. (2005b). An indicator of monthly GDP and an early estimate of quarterly GDP growth. The Economic Journal, 115(501), F108–F129.

    Article  Google Scholar 

  • Mittnik, S., & Zadrozny, P. (2005). Forecasting quarterly German GDP at monthly intervals using monthly IFO business conditions data. In J. E. Sturm & T. Wollmershäuser (Eds.), IFO survey data in business cycle analysis and monetary policy analysis (pp. 19–48). Heidelberg: Physica.

    Google Scholar 

  • Mokinski, F., Sheng, X., & Yang, J. (2015). Measuring disagreement in qualitative expectations. Journal of Forecasting, 34(5), 405–426.

    Article  Google Scholar 

  • Müller, C. (2010). You CAN Carlson–Parkin. Economics Letters, 108(1), 33–35.

    Article  Google Scholar 

  • Nardo, M. (2003). The quantification of qualitative data: A critical assessment. Journal of Economic Surveys, 17(5), 645–668.

    Article  Google Scholar 

  • Nolte, I., & Pohlmeier, W. (2007). Using forecasts of forecasters to forecast. International Journal of Forecasting, 23(1), 15–28.

    Article  Google Scholar 

  • Paloviita, M. (2006). Inflation dynamics in the euro area and the role of expectations. Empirical Economics, 31, 847–860.

    Article  Google Scholar 

  • Parigi, G., & Schlitzer, G. (1995). Quarterly forecasts of the Italian business-cycle by means of monthly economic indicators. Journal of Forecasting, 14(2), 117–141.

    Article  Google Scholar 

  • Pehkonen, J. (1992). Survey expectations and stochastic trends in modelling the employment-output equation. Oxford Bulletin of Economics and Statistics, 54(2), 579–589.

    Google Scholar 

  • Pesaran, M. H. (1984). Expectation formation and macroeconomic modelling. In P. Malgrange & P. A. Muet (Eds.), Contemporary macroeconomic modelling (pp. 27–55). Oxford: Basil Blackwell.

    Google Scholar 

  • Pesaran, M. H. (1985). Formation of inflation expectations in British manufacturing industries. Economic Journal, 95(380), 948–975.

    Article  Google Scholar 

  • Pesaran, M. H. (1987). The limits to rational expectations. Oxford: Basil Blackwell.

    Google Scholar 

  • Pesaran, M. H., & Weale, M. (2006). Survey expectations. In G. Elliott, C. W. J. Granger, & A. Timmermann (Eds.), Handbook of economic forecasting (Vol. 1, pp. 715–776). Amsterdam: Elsevier North-Holland.

    Chapter  Google Scholar 

  • Robinzonov, N., Tutz, G., & Hothorn, T. (2012). Boosting techniques for nonlinear time series models. AStA Advances in Statistical Analysis, 96(1), 99–122.

    Article  Google Scholar 

  • Schmeling, M., & Schrimpf, A. (2011). Expected inflation, expected stock returns, and money illusion: What can we learn from survey expectations. European Economic Review, 55(5), 702–719.

    Article  Google Scholar 

  • Seitz, H. (1988). The estimation of inflation forecasts from business survey data. Applied Economics, 20(4), 427–438.

    Article  Google Scholar 

  • Smith, J., & McAleer, M. (1995). Alternative procedures for converting qualitative response data to quantitative expectations: An application to Australian manufacturing. Journal of Applied Econometrics, 10(2), 165–185.

    Article  Google Scholar 

  • Stangl, A. (2007). Ifo World Economic Survey micro data. Journal of Applied Social Science Studies, 127(3), 487–496.

    Google Scholar 

  • Stangl, A. (2008). Essays on the measurement of economic expectations. Dissertation. Munich: Universität München.

  • Taylor, K., & McNabb, R. (2007). Business cycles and the role of confidence: Evidence for Europe. Oxford Bulletin for Economics and Statistics, 69(2), 185–208.

    Article  Google Scholar 

  • Terai, A. (2009). Measurement error in estimating inflation expectations from survey data: An evaluation by Monte Carlo simulations. Journal of Business Cycle Measurement and Analysis, 8(2), 133–156.

    Google Scholar 

  • Theil, H. (1952). On the time shape of economic microvariables and the Munich Business Test. Revue de l’Institut International de Statistique, 20, 105–120.

    Article  Google Scholar 

  • Toyoda, T. (1979). Formation of inflation expectations in Japan. Economic Studies Quarterly, 30(3), 193–201.

    Google Scholar 

  • Van den Berg, G. J., Lindeboom, M., & Dolton, P. (2006). Survey non-response and unemployment duration. Journal of the Royal Statistical Society, Series A, 169(3), 585–604.

    Article  Google Scholar 

  • Vermeulen, P. (2014). An evaluation of business survey indices for short-term forecasting: Balance method versus Carlson–Parkin method. International Journal of Forecasting, 30(4), 882–897.

    Article  Google Scholar 

  • Yang, G., Li, X., Wang, J., Lian, L., & Ma, T. (2015). Modeling oil production based on symbolic regression. Energy Policy, 82(1), 48–61.

    Article  Google Scholar 

  • Zanin, L. (2010). The Relationship between changes in the Economic Sentiment Indicator and real GDP growth: A time-varying coefficient approach. Economics Bulletin, 30(1), 837–846.

    Google Scholar 

  • Zárate, H. M., Sánchez, K., & Marín, M. (2012). Quantification of ordinal surveys and rational testing: An application to the Colombian monthly survey of economic expectations. Revista Colombiana de Estadística, 35(1), 77–108.

    Google Scholar 

  • Zelinka, I. (2015). A survey on evolutionary algorithms dynamics and its complexity—Mutual relations, past, present and future. Swarm and Evolutionary Computation, 25, 2–14.

    Article  Google Scholar 

  • Zimmermann, K. F. (1997). Analysis of business surveys. In M. H. Pesaran & P. Schmidt (Eds.), Handbook of applied econometrics. Volume II: Microeconomics (pp. 407–441). Oxford: Blackwell.

    Google Scholar 

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Acknowledgements

This paper has been partially financed by the project ECO2016-75805-R of the Spanish Ministry of Economy and Competitiveness. We would like to thank two anonymous referees for their useful comments and suggestions. We also wish to thank Johanna Garnitz and Klaus Wohlrabe at the Ifo Institute for Economic Research in Munich for providing us the data used in the study.

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Claveria, O., Monte, E. & Torra, S. A Data-Driven Approach to Construct Survey-Based Indicators by Means of Evolutionary Algorithms. Soc Indic Res 135, 1–14 (2018). https://doi.org/10.1007/s11205-016-1490-3

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