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Evolutionary Computation for Macroeconomic Forecasting

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Abstract

The main objective of this study is twofold. First, we propose an empirical modelling approach based on genetic programming to forecast economic growth by means of survey data on expectations. We use evolutionary algorithms to 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. The set of empirically-generated proxies of economic growth are used as building blocks to forecast the evolution of GDP. Second, we use these estimates of GDP to assess the impact of the 2008 financial crisis on the accuracy of agents’ expectations about the evolution of the economic activity in four Scandinavian economies. While we find an improvement in the capacity of agents’ to anticipate economic growth after the crisis, predictive accuracy worsens in relation to the period prior to the crisis. The most accurate GDP forecasts are obtained for Sweden.

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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.

    Google Scholar 

  • Acosta-González, E., & Fernández-Rodríguez, F. (2014). Forecasting financial failure of firms via genetic algorithms. Computational Economics, 43(2), 133–147.

    Google Scholar 

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

    Google Scholar 

  • Acosta-González, E., Fernández-Rodríguez, F., & Sosvilla-Rivero, S. (2014). An empirical examination of the determinants of the shadow economy. Applied Economics Letters, 21(5), 304–307.

    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.

    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.

    Google Scholar 

  • Álvarez-Díaz, M., Mateu-Sbert, J., & Rosselló-Nadal, J. (2009). Forecasting tourist arrivals to Balearic Islands using genetic programming. International Journal of Computational Economics and Econometrics, 1(1), 64–75.

    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.

    Google Scholar 

  • Barmpalexis, P., Kachrimanis, K., Tsakonas, A., & Georgarakis, E. (2011). Symbolic regression via genetic programming in the optimization of a controlled release pharmaceutical formulation. Chemometrics and Intelligent Laboratory Systems, 107(1), 75–82.

    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.

    Google Scholar 

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

    Google Scholar 

  • Bergström, R. (1995). The relationship between manufacturing production and different business survey series in Sweden 1968–1992. International Journal of Forecasting, 11(3), 379–393.

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  • Bovi, M. (2013). Are the representative agent’s beliefs based on efficient econometric models? Journal of Economic Dynamics & Control, 37(3), 633–648.

    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.

    Google Scholar 

  • Bruestle, P., & Crain, W. M. (2015). A mean-variance approach to forecasting with the consumer confidence index. Applied Economics, 47(23), 2430–2444.

    Google Scholar 

  • Bruno, P. (2014). Consumer confidence and consumption forecast: A non-parametric approach. Empirica, 41(1), 37–52.

    Google Scholar 

  • Cai, W., Pacheco-Vega, A., Sen, M., & Yang, K. T. (2006). Heat transfer correlations by symbolic regression. International Journal of Heat and Mass Transfer, 49(23–24), 4352–4359.

    Google Scholar 

  • Can, B., & Heavey, C. (2011). Comparison of experimental designs for simulation-based symbolic regression of manufacturing systems. Computers & Industrial Engineering, 61(3), 447–462.

    Google Scholar 

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

    Google Scholar 

  • Ceperic, V., Bako, N., & Baric, A. (2014). A symbolic regression-based modelling strategy of AC/DC rectifiers for RFID applications. Expert Systems with Applications, 41(16), 7061–7067.

    Google Scholar 

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

  • Chen, S. H., & Kuo, T. W. (2002). Evolutionary computation in economics and finance: A bibliography. In S. H. Chen (Ed.), Evolutionary computation in economics and finance (pp. 419–455). Heidelberg: Physica-Verlag.

    Google Scholar 

  • Chen, S. H., Kuo, T. W., & Hoi, K. M. (2008). Genetic programming and financial trading: How much about “what we know”. In C. Zopounidis, et al. (Eds.), Handbook of financial engineering (pp. 99–154). New York: Springer.

    Google Scholar 

  • Chen, X., Pang, Y., & Zheng, G. (2010). Macroeconomic forecasting using GP based vector error correction model. In J. Wang (Ed.), Business intelligence in economic forecasting: Technologies and techniques (pp. 1–15). Hershey, PA: IGI Global.

    Google Scholar 

  • 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 Publishers.

    Google Scholar 

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

    Google Scholar 

  • Common, M. (1985). Testing for rational expectations with qualitative survey data. Manchester School of Economic and Social Statistics, 53(2), 138–148.

    Google Scholar 

  • Cramer, N. (1985). A representation for the adaptive generation of simple sequential programs. In 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.

    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.

    Google Scholar 

  • Drake, A. E., & Marks, R. E. (2008). Genetic algorithms in economics and finance: Forecasting stock market prices and foreign exchange—A review. In S. H. Chen (Ed.), Genetic algorithms and genetic programming in computational finance (pp. 29–54). New York: Springer.

    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.

    Google Scholar 

  • Duda, J., & Szydło, S. (2011). Collective intelligence of genetic programming for macroeconomic forecasting. In P. Jȩdrzejowicz, et al. (Eds.), Computational collective intelligence. Technologies and applications (pp. 445–454). Berlin: Springer.

    Google Scholar 

  • Erjavec, N., Lolić, I., & Sorić, P. (2015). How (i)rrational are we? A case of Croatian inflation. Croatian Operational Research Review, 6, 241–253.

    Google Scholar 

  • Ferreira, C. (2001). Gene expression programming: A new adaptive algorithm for solving problems. Complex Systems, 13(2), 87–129.

    Google Scholar 

  • Fogel, D. B. (2006). Evolutionary computation. Toward a new philosophy of machine intelligence (3rd ed.). Hoboken, NJ: Wiley.

    Google Scholar 

  • Fogel, L. J., Owens, A. J., & Walsh, M. J. (1966). Artificial intelligence through simulated evolution. New York: Wiley.

    Google Scholar 

  • 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 

  • Gandomi, A. H., & Roke, D. A. (2015). Assessment of artificial neural network and genetic programming as predictive tools. Advances in Engineering Software, 88(C), 63–72.

    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 

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

    Google Scholar 

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

    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.

    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.

    Google Scholar 

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

    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 

  • Huang, C. F., Hsu, C. J., Chen, C. C., Chang, B. R., & Li, C.-A. (2015). An intelligent model for pairs trading using genetic algorithms. Computational Intelligence and Neuroscience, 2015, 1–10.

    Google Scholar 

  • Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006). Extreme learning machine: Theory and applications. Neurocomputing, 70(1–3), 489–501.

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  • Kapetanios, G., Marcellino, M., & Papailias, F. (2016). Forecasting inflation and GDP growth using heuristic optimisation of information criteria and variable reduction methods. Computational Statistics and Data Analysis, 100, 369–382.

    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.

    Google Scholar 

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

    Google Scholar 

  • Kl’účik, 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.

    Google Scholar 

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

    Google Scholar 

  • Koza, J. R. (1995). Genetic Programming for econometric modeling. In S. Goonatilaje & P. Treleaven (Eds.), Intelligent systems for finance and business (pp. 251–269). London: Wiley.

    Google Scholar 

  • Krömer, P., Owais, S., Platoš, J., & Snášel, V. (2013). Towards new directions of data mining by evolutionary fuzzy rules and symbolic regression. Computers and Mathematics with Applications, 66(2), 190–200.

    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.

  • 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.

    Google Scholar 

  • Larkin, F., & Ryan, C. (2008). Good news: Using news feeds with genetic programming to predict stock prices. In M. O’Neil, et al. (Eds.), Genetic Programming (pp. 49–60). Berlin: Springer.

    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.

    Google Scholar 

  • Lee, K. C. (1994). Formation of price and cost inflation expectations in British manufacturing industries: A multi-sectoral analysis. Economic Journal, 104(423), 372–385.

    Google Scholar 

  • Lehmann, R., & Wohlrabe, K. (2017). Experts, firms, consumers or even hard data? Forecasting employment in Germany. Applied Economics Letters, 24(4), 279–283.

    Google Scholar 

  • Löffler, G. (1999). Refining the Carlson–Parkin method. Economics Letters, 64(2), 167–71.

    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.

    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.

    Google Scholar 

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

    Google Scholar 

  • Marković, D., Petković, D., Nikolić, V., Milovančević, M., & Petković, B. (2017). Soft computing prediction of economic growth based in science and technology factors. Physica A, 465, 217–220.

    Google Scholar 

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

    Google Scholar 

  • Maschek, M. K. (2010). Intelligent mutation rate control in an economic application of genetic algorithms. Computational Economics, 35(1), 25–49.

    Google Scholar 

  • Milutinović, B., Stefanović, G., Dekić, P. S., Mijailović, I., & Tomić, M. (2017). Environmental assessment of waste management scenarios with energy recovery using life cycle assessment and multi-criteria analysis. Energy, 137, 917–926.

  • Milutinović, B., Stefanović, G., Jović, S., & Škrijelj, H. (2015). Development of mathematical model for estimation of economic indicators of waste treatment. In Proceedings of the 5th international symposium on mining and environmental protection (Vrdnik) (pp. 162–170).

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

    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.

    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.

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  • Österholm, P. (2014). Survey data and short-term forecasts of Swedish GDP growth. Applied Economics Letters, 21(2), 135–139.

    Google Scholar 

  • Peng, Y., Yuan, C., Qin, X., Huang, J., & Shi, Y. (2014). An improved gene expression programming approach for symbolic regression problems. Neurocomputing, 137, 293–301.

    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.

    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.

    Google Scholar 

  • Petković, D. (2015). Adaptive neuro-fuzzy optimization of the net present value and internal rate of return of a wind farm project under wake effect. JCC: The Business and Economics Research Journal, 8(1), 11–28.

    Google Scholar 

  • Poli, R., Vanneschi, L., Langdon, W. B., & Mcphee, N. F. (2010). Theoretical results in genetic programming: The next ten years? Genetic Programming and Evolvable Machines, 11(3), 285–320.

    Google Scholar 

  • Qiao, Z., McAleer, M., & Wong, W. K. (2009). Linear and nonlinear causality between changes in consumption and consumer attitudes. Economic Letters, 102(3), 161–164.

    Google Scholar 

  • Rahiala, M., & Teräsvirta, T. (1993). Business survey data in forecasting the output of Swedish and Finnish metal and engineering industries: A Kalman filter approach. Journal of Forecasting, 12(3–4), 255–271.

    Google Scholar 

  • Ramos-Herrera, M. C., & Acosta-González, E. (2017). Factors determining exchange rate stability in member and candidate States of the European Union: An analysis based on genetic algorithms. Cuadernos de Economía, 40(112), 68–82.

    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.

    Google Scholar 

  • Sarradj, E., & Geyer, T. (2014). Symbolic regression modeling of noise generation at porous airfoils. Journal of Sound and Vibration, 333(14), 3189–3202.

    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.

    Google Scholar 

  • Sheta, A. F., Ahmed, S. E. M., & Faris, H. (2015). Evolving stock market prediction models using multi-gene symbolic regression genetic programming. Artificial Intelligence and Machine Learning, 15(1), 11–20.

    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.

    Google Scholar 

  • Sorić, P., Lolić, I., & Mačkić, V. (2013). An empirical assessment of the expected inflation quantification methods. A pan-European study. Global Review of Business and Economics Research, 9(2), 117–132.

    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.

  • 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.

    Google Scholar 

  • Vasilakis, G. A., Theofilatos, K. A., Georgopoulos, E. F., Karathanasopoulos, A., & Likothanassis, S. D. (2013). A genetic programming approach for EUR/USD exchange rate forecasting and trading. Computational Economics, 42(4), 415–431.

    Google Scholar 

  • Vladislavleva, E., Smits, G., & den Hertog, D. (2010). On the importance of data balancing for symbolic regression. IEEE Transactions in Evolutionary Computation, 14(2), 252–277.

    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.

    Google Scholar 

  • Waltman, L., Van Eck, N. J., Dekker, R., & Kaymak, U. (2011). Economic modeling using evolutionary algorithms: The effect of a binary encoding of strategies. Journal of Evolutionary Economics, 21(5), 737–756.

    Google Scholar 

  • Wu, C. H., Chou, H. J., & Su, W. H. (2008). Direct transformation of coordinates for GPS positioning using the techniques of genetic programming and symbolic regression. Engineering Applications of Artificial Intelligence, 21(8), 1347–1359.

    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.

    Google Scholar 

  • Yao, L., & Lin, C. C. (2009). Identification of nonlinear systems by the genetic programming-based volterra filter. IET Signal Processing, 3(2), 93–105.

    Google Scholar 

  • Wilms, I., Gelper, S., & Croux, C. (2016). The predictive power of the business and bank sentiment of firms: A high-dimensional Granger Causality approach. European Journal of Operational Research, 254(1), 138–147.

    Google Scholar 

  • Wilson, G., & Banzhaf, W. (2009). Prediction of interday stock prices using developmental and linear genetic programming. In M. Giacobini, et al. (Eds.), Applications of Evolutionary Computing (pp. 172–181). Berlin: Springer.

    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.

    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 Publishers.

    Google Scholar 

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Acknowledgements

We would like to thank Johanna Garnitz at the Ifo Institut für Wirtschaftsforschung München for providing us the data used in the study. This research was supported by by the Projects ECO2016-75805-R and TEC2015-69266-P from the Spanish Ministry of Economy and Competitiveness.

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Authors and Affiliations

  1. AQR-IREA (Institute of Applied Economics Research), University of Barcelona (UB), Diagonal, 690, 08034, Barcelona, Spain

    Oscar Claveria

  2. Department of Signal Theory and Communications, Polytechnic University of Catalunya (UPC), Jordi Girona, 1-3, 08034, Barcelona, Spain

    Enric Monte

  3. Riskcenter-IREA, Department of Econometrics and Statistics, University of Barcelona (UB), Diagonal, 690, 08034, Barcelona, Spain

    Salvador Torra

  4. Department of Econometrics, University of Barcelona, Diagonal, 690, 08034, Barcelona, Spain

    Oscar Claveria

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  1. Oscar Claveria
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Correspondence to Oscar Claveria.

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Claveria, O., Monte, E. & Torra, S. Evolutionary Computation for Macroeconomic Forecasting. Comput Econ 53, 833–849 (2019). https://doi.org/10.1007/s10614-017-9767-4

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