Advertisement

Empirica

, Volume 46, Issue 2, pp 205–227 | Cite as

Empirical modelling of survey-based expectations for the design of economic indicators in five European regions

  • Oscar ClaveriaEmail author
  • Enric Monte
  • Salvador Torra
Original Paper
  • 90 Downloads

Abstract

In this study we use agents’ expectations about the state of the economy to generate indicators of economic activity in twenty-six European countries grouped in five regions (Western, Eastern, and Southern Europe, and Baltic and Scandinavian countries). We apply a data-driven procedure based on evolutionary computation to transform survey variables in economic growth rates. In a first step, we design five independent experiments to derive a formula using survey variables that best replicates the evolution of economic growth in each region by means of genetic programming, limiting the integration schemes to the main mathematical operations. We then rank survey variables according to their performance in tracking economic activity, finding that agents’ “perception about the overall economy compared to last year” is the survey variable with the highest predictive power. In a second step, we assess the out-of-sample forecast accuracy of the evolved indicators. Although we obtain different results across regions, Austria, Slovakia, Portugal, Lithuania and Sweden are the economies of each region that show the best forecast results. We also find evidence that the forecasting performance of the survey-based indicators improves during periods of higher growth.

Keywords

Economic indicators Qualitative survey data Expectations Symbolic regression Evolutionary algorithms Genetic programming 

JEL Classification

C51 C55 C63 C83 C93 

Notes

Acknowledgements

This research was supported by the Projects ECO2016-75805-R and TEC2015-69266-P from the Spanish Ministry of Economy and Competitiveness. We would like to thank the Editor and 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.

Supplementary material

10663_2017_9395_MOESM1_ESM.xlsx (347 kb)
Supplementary material 1 (XLSX 347 kb)

References

  1. Abberger K (2007) Qualitative business surveys and the assessment of employment—a case study for Germany. Int J Forecast 23(2):249–258Google Scholar
  2. Acosta-González E, Fernández F (2014) Forecasting financial failure of firms via genetic algorithms. Comput Econ 43(2):133–157Google Scholar
  3. Acosta-González E, Fernández F, Sosvilla S (2012) On factors explaining the 2008 financial crisis. Econ Lett 115(2):215–217Google Scholar
  4. Alexandridis AK, Kampouridis M, Cramer S (2017) A comparison of wavelet networks and genetic programming in the context of temperature derivatives. Int J Forecast 33(1):21–47Google Scholar
  5. Altug S, Çakmakli C (2016) Forecasting inflation using survey expectations and target inflation: evidence from Brazil and Turkey. Int J Forecast 32(1):138–153Google Scholar
  6. Álvarez-Díaz M, Álvarez A (2005) Genetic multi-model composite forecast for non-linear prediction of exchange rates. Empir Econ 30(3):643–663Google Scholar
  7. Anderson O (1952) The business test of the IFO-Institute for Economic Research, Munich, and its theoretical model. Rev l’Inst Int Stat 20:1–17Google Scholar
  8. Balcombe K (1996) The Carlson–Parkin method applied to NZ price expectations using QSBO survey data. Econ Lett 51(1):51–57Google Scholar
  9. Banzhaf W, Nordin P, Keller RE, Francone FD (2008) Genetic programming: an introduction. On the automatic evolution of computer programs and its applications. Morgan Kaufmann, San Francisco, CAGoogle Scholar
  10. Barmpalexis P, Kachrimanis K, Tsakonas A, Georgarakis E (2011) Symbolic regression via genetic programming in the optimization of a controlled release pharmaceutical formulation. Chemometr Intell Lab Syst 107(1):75–82Google Scholar
  11. Batchelor RA (1981) Aggregate expectations under the stable laws. J Econom 16(2):199–210Google Scholar
  12. Batchelor RA (1982) Expectations, output and inflation: the European experience. Eur Econ Rev 17(1):1–25Google Scholar
  13. Batchelor RA (1986) Quantitative v. qualitative measures of inflation expectations. Oxf Bull Econ Stat 48(2):99–120Google Scholar
  14. Batchelor R, Dua P (1992) Survey expectations in the time series consumption function. Rev Econ Stat 74(4):598–606Google Scholar
  15. Batchelor R, Dua P (1998) Improving macro-economic forecasts. Int J Forecast 14(1):71–81Google Scholar
  16. Batchelor R, Orr AB (1988) Inflation expectations revisited. Economica 55(2019):317–331Google Scholar
  17. Bennett A (1984) Output expectations of manufacturing industry. Appl Econ 16(6):869–879Google Scholar
  18. Bergström R (1995) The relationship between manufacturing production and different business survey series in Sweden 1968–1992. Int J Forecast 11(3):379–393Google Scholar
  19. Berk JM (1999) Measuring inflation expectations: a survey data approach. Appl Econ 31(11):1467–1480Google Scholar
  20. Białowolski P (2016) The influence of negative response style on survey-based household inflation expectations. Qual Quant 50(2):509–528Google Scholar
  21. Bovi M (2013) Are the representative agent’s beliefs based on efficient econometric models? J Econ Dyn Control 37(3):633–648Google Scholar
  22. Bovi M (2016) The tale of two expectations. Qual Quant 50(6):2677–2705Google Scholar
  23. Breitung J, Schmeling M (2013) Quantifying survey expectations: What’s wrong with the probability approach? Int J Forecast 29(1):142–154Google Scholar
  24. Bruestle S, Crain WM (2015) A mean-variance approach to forecasting with the consumer confidence index. Appl Econ 47(23):2430–2444Google Scholar
  25. Bruno G (2014) Consumer confidence and consumption forecast: a non-parametric approach. Empirica 41(1):37–52Google Scholar
  26. Carlson JA, Parkin M (1975) Inflation expectations. Economica 42(166):123–138Google Scholar
  27. Ceperic V, Bako N, Baric A (2014) A symbolic regression-based modelling strategy of AC/DC rectifiers for RFID applications. Expert Syst Appl 41(16):7061–7067Google Scholar
  28. Chen SH, Kuo TW (2002) Evolutionary computation in economics and finance: a bibliography. In: Chen SH (ed) Evolutionary computation in economics and finance. Physica-Verlag, Heidelberg, pp 419–455Google Scholar
  29. Chen SH, Kuo TW, Hoi KM (2008) Genetic programming and financial trading: how much about “what we know”. In: Zopounidis C et al (eds) Handbook of financial engineering. Springer, New York, pp 99–154Google Scholar
  30. Chen X, Pang Y, Zheng G (2010) Macroeconomic forecasting using GP based vector error correction model. In: Wang J (ed) Business intelligence in economic forecasting: technologies and techniques. IGI Global, Hershey, pp 1–15Google Scholar
  31. Christiansen C, Eriksen J, Moller S (2014) Forecasting US recessions: the role of sentiment. J Bank Finance 49:459–468Google Scholar
  32. Claveria O (2010) Qualitative survey data on expectations. Is there an alternative to the balance statistic? In: Molnar AT (ed) Economic forecasting. Nova Science Publishers, Hauppauge, pp 181–190Google Scholar
  33. Claveria O, Pons E, Suriñach J (2006) Quantification of expectations. Are they useful for forecasting inflation? Economic Issues 11(2):19–38Google Scholar
  34. Claveria O, Pons E, Ramos R (2007) Business and consumer expectations and macroeconomic forecasts. Int J Forecast 23(1):47–69Google Scholar
  35. Claveria O, Monte E, Torra S (2015) A new forecasting approach for the hospitality industry. Int J Contemp Hosp Manage 27(7):1520–1538Google Scholar
  36. 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):177–189Google Scholar
  37. Claveria O, Monte E, Torra S (2017) A new approach for the quantification of qualitative measures of economic expectations. Qual Quant 51(6):2685–2706Google Scholar
  38. Common M (1985) Testing for rational expectations with qualitative survey data. Manch Sch Econ Soc Stat 53(2):138–148Google Scholar
  39. Cowles A, Jones H (1937) Some a posteriori probabilities in stock market action. Econometrica 5(3):280–294Google Scholar
  40. 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, PAGoogle Scholar
  41. Dabhi VK, Chaudhary S (2015) Empirical modeling using genetic programming: a survey of issues and approaches. Nat Comput 14(2):303–330Google Scholar
  42. Dees S, Brinca PS (2013) Consumer confidence as a predictor of consumption spending: evidence for the United States and the Euro area. Int Econ 134:1–14Google Scholar
  43. Drake AE, Marks RE (2002) Genetic algorithms in economics and finance: forecasting stock market prices and foreign exchange—a review. In: Chen SH (ed) Genetic algorithms and genetic programming in computational finance. Springer, New York, pp 29–54Google Scholar
  44. Dreger C, Kholodilin D (2013) Forecasting private consumption by consumer surveys. J Forecast 32(1):10–18Google Scholar
  45. Driver C, Urga G (2004) Transforming qualitative survey data: performance comparisons for the UK. Oxf Bull Econ Stat 66(1):71–89Google Scholar
  46. Duda J, Szydło S (2011) Collective intelligence of genetic programming for macroeconomic forecasting. In: Jędrzejowicz P et al (eds) Computational collective intelligence. Technologies and applications. Springer, Berlin, pp 445–454Google Scholar
  47. Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129Google Scholar
  48. Fogel DB (2006) Evolutionary computation. Toward a new philosophy of machine intelligence, 3rd edn. Wiley, HobokenGoogle Scholar
  49. Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution. John Wiley, New YorkGoogle Scholar
  50. Fortin FA, De Rainville FM, Gardner MA, Parizeau M, Gagné C (2012) DEAP: evolutionary algorithms made easy. J Mach Learn Res 13(1):2171–2175Google Scholar
  51. Franses PH, Kranendonk HC, Lanser D (2011) One model and various experts: evaluating Dutch macroeconomic forecasts. Int J Forecast 27(2):482–495Google Scholar
  52. Gandomi AH, Roke D (2015) Assessment of artificial neural network and genetic programming as predictive tools. Adv Eng Softw 88:63–72Google Scholar
  53. Garnitz J, Nerb G, Wohlrabe K (2015) CESifo World Economic Survey—November 2015. CESifo World Econ Survey 14(4):1–28Google Scholar
  54. Ghonghadze J, Lux T (2012) Modelling the dynamics of EU economic sentiment indicators: an interaction-based approach. Appl Econ 44(24):3065–3088Google Scholar
  55. Girardi A (2014) Expectations and macroeconomic fluctuations in the Euro area. Econ Lett 125(2):315–318Google Scholar
  56. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, BostonGoogle Scholar
  57. Gong YJ, Chen WN, Zhan ZH, Zhang J, Li Y, Zhang Q, Li JJ (2015) Distributed evolutionary algorithms and their models: a survey of the stat-of-the-art. Appl Soft Comput 34:286–300Google Scholar
  58. Graff M (2010) Does a multi-sectoral design improve indicator-based forecasts of the GDP growth rate? Evidence from Switzerland. Appl Econ 42(21):2759–2781Google Scholar
  59. Guizzardi A, Stacchini A (2015) Real-time forecasting regional tourism with business sentiment surveys. Tour Manag 47:213–223Google Scholar
  60. Hansson J, Jansson P, Löf M (2005) Business survey data: Do they help in forecasting GDP growth? Int J Forecast 30(1):65–77Google Scholar
  61. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann ArborGoogle Scholar
  62. Hutson M, Joutz F, Stekler H (2014) Interpreting and evaluating CESIfo’s World Economic Survey directional forecasts. Econ Model 38:6–11Google Scholar
  63. Hyndman RJ, Koehler AB (2006) Another look at measures of forecast accuracy. Int J Forecast 22(4):679–688Google Scholar
  64. Ivaldi M (1992) Survey evidence on the rationality of expectations. J Appl Econom 7(3):225–241Google Scholar
  65. Jean-Baptiste F (2012) Forecasting with the new Keynesian Phillips curve: evidence from survey data. Econ Lett 117(3):811–813Google Scholar
  66. Jonsson T, Österholm P (2011) The forecasting properties of survey-based wage-growth expectations. Econ Lett 113(3):276–281Google Scholar
  67. Jonsson T, Österholm P (2012) The properties of survey-based inflation expectations in Sweden. Empir Econ 42(1):79–94Google Scholar
  68. Kaboudan MA (2000) Genetic programing prediction of stock prices. Comput Econ 16(3):207–236Google Scholar
  69. Klein LR, Özmucur S (2010) The use of consumer and business surveys in forecasting. Econ Model 27(6):1453–1462Google Scholar
  70. Kłopocka K (2017) Does consumer confidence forecast household saving and borrowing behavior? Evidence for Poland. Soc Indic Res 133(2):693–717Google Scholar
  71. Klúčik M (2012) Estimates of foreign trade using genetic programming. In: Proceedings of the 46 the scientific meeting of the Italian Statistical SocietyGoogle Scholar
  72. Kotanchek ME, Vladislavleva EY, Smits GF (2010) Symbolic regression via genetic programming as a discovery engine: insights on outliers and prototypes. In: Riolo R et al (eds) Genetic programming theory and practice VII, genetic and evolutionary computation, vol 8. Springer, Berlin, pp 55–72Google Scholar
  73. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, CambridgeGoogle Scholar
  74. Kronberger G, Fink S, Kommenda M, Affenzeller M (2011) Macro-economic time series modeling and interaction networks. In: Di Chio C et al (eds) Applications of evolutionary computation. EvoApplications 2011. Lecture Notes in Computer Science, vol 6625. Springer, Berlin, Heidelberg, pp 101–110Google Scholar
  75. Kudymowa E, Plenk J, Wohlrabe K (2013) Ifo World Economic Survey and the business cycle in selected countries. CESifo Forum 14(4):51–57Google Scholar
  76. Kumar V, Leone R, Gaskins J (1995) Aggregate and disaggregate sector fore-casting using consumer confidence measures. Int J Forecast 11(3):361–377Google Scholar
  77. Lacová Ž, Král P (2015) Measurement and characteristics of enterprise inflation expectations in Slovakia. Proc Econ Finance 30:505–512Google Scholar
  78. Lahiri K, Teigland C (1987) On the normality of probability distributions of inflation and GNP forecasts. Int J Forecast 3(2):269–279Google Scholar
  79. Lahiri K, Zhao Y (2015) Quantifying survey expectations: a critical review and generalization of the Carlson–Parkin method. Int J Forecast 31(1):51–62Google Scholar
  80. Lahiri K, Monokroussos G, Zhao Y (2016) Forecasting consumption: the role of consumer confidence in real time with many predictors. J Appl Econom 31(7):1254–1275Google Scholar
  81. Larkin F, Ryan C (2008) Good news: using news feeds with genetic programming to predict stock prices. In: O’Neil M et al (eds) Genetic programming. Springer, Berlin, pp 49–60Google Scholar
  82. Lawrenz C, Westerhoff F (2003) Modeling exchange rate behaviour with a genetic algorithm. Comput Econ 21(3):209–229Google Scholar
  83. Leduc S, Sill K (2013) Expectations and economic fluctuations: an analysis using survey data. Rev Econ Stat 95(4):1352–1367Google Scholar
  84. Lee KC (1994) Formation of price and cost inflation expectations in British manufacturing industries: a multi-sectoral analysis. Econ J 104(423):372–385Google Scholar
  85. Lehmann R, Wohlrabe K (2017) Experts, firms, consumers or even hard data? Forecasting employment in Germany. Appl Econ Lett 24(4):279–283Google Scholar
  86. Lemmens A, Croux C, Dekimpe MG (2005) On the predictive content of production surveys: a pan-European study. Int J Forecast 21(2):363–375Google Scholar
  87. Löffler G (1999) Refining the Carlson–Parkin method. Econ Lett 64(2):167–171Google Scholar
  88. Lui S, Mitchell J, Weale M (2011a) The utility of expectational data: firm-level evidence using matched qualitative-quantitative UK surveys. Int J Forecast 27(4):1128–1146Google Scholar
  89. Lui S, Mitchell J, Weale M (2011b) Qualitative business surveys: signal or noise? J R Stat Soc Ser A (Stat Soc) 174(2):327–348Google Scholar
  90. Łyziak T, Mackiewicz-Łyziak J (2014) Do consumers in Europe anticipate future inflation? Eastern Eur Econ 52(3):5–32Google Scholar
  91. Maag T (2009) On the accuracy of the probability method for quantifying beliefs about inflation. KOF Working Papers, No. 230, KOF Swiss Economic Institute, ZurichGoogle Scholar
  92. Makridakis S, Hibon M (2000) The M3-competition: results, conclusions and implications. Int J Forecast 16(4):451–476Google Scholar
  93. Martinsen K, Ravazzolo F, Wulfsberg F (2014) Forecasting macroeconomic variables using disaggregate survey data. Int J Forecast 30(1):65–77Google Scholar
  94. Maschek MK (2010) Intelligent mutation rate control in an economic application of genetic algorithms. Comput Econ 35(1):25–49Google Scholar
  95. Miah F, Rahman MS, Albinali K (2016) Rationality of survey based inflation expectations: a study of 18 emerging economies’ inflation forecasts. Res Int Bus Finance 36:158–166Google Scholar
  96. Mitchell J, Smith R, Weale M (2002) Quantification of qualitative firm-level survey data. Econ J 112(478):117–135Google Scholar
  97. Mitchell J, Smith R, Weale M (2005a) Forecasting manufacturing output growth using firm-level survey data. Manch Sch 73(4):479–499Google Scholar
  98. Mitchell J, Smith R, Weale M (2005b) An indicator of monthly GDP and an early estimate of quarterly GDP growth. Econ J 115(501):F108–F129Google Scholar
  99. Mittnik S, Zadrozny P (2005) Forecasting quarterly German GDP at monthly intervals using monthly IFO business conditions data. In: Sturm JE, Wollmershäuser T (eds) IFO survey data in business cycle analysis and monetary policy analysis. Physica-Verlag, Heidelberg, pp 19–48Google Scholar
  100. Mokinski F, Sheng X, Yang J (2015) Measuring disagreement in qualitative expectations. J Forecast 34(5):405–426Google Scholar
  101. Müller C (2010) You CAN Carlson–Parkin. Econ Lett 108(1):33–35Google Scholar
  102. Muth J (1961) Rational expectations and the theory of price movements. Econometrica 29(3):315–335Google Scholar
  103. Nardo M (2003) The quantification of qualitative data: a critical assessment. J Econ Surveys 17(5):645–668Google Scholar
  104. Nardo M, Cabeza-Gutés M (1999) The role of measurement error in rational expectations testing. UAB Working Paper 451, Universitat Autònoma de Barcelona, BarcelonaGoogle Scholar
  105. Nolte I, Pohlmeier W (2007) Using forecasts of forecasters to forecast. Int J Forecast 23(1):15–28Google Scholar
  106. Paloviita M (2006) Inflation dynamics in the euro area and the role of expectations. Empir Econ 31:847–860Google Scholar
  107. Peng Y, Yuan C, Qin X, Huang J, Shi Y (2014) An improved gene expression programming approach for symbolic regression problems. Neurocomputing 137:293–301Google Scholar
  108. Pesaran MH (1985) Formation of inflation expectations in British manufacturing industries. Econ J 95(380):948–975Google Scholar
  109. Pesaran MH (1987) The limits to rational expectations. Basil Blackwell, OxfordGoogle Scholar
  110. Pesaran MH, Weale M (2006) Survey expectations. In: Elliott G, Granger CWJ, Timmermann A (eds) Handbook of economic forecasting, vol 1. Elsevier North-Holland, Amsterdam, pp 715–776Google Scholar
  111. Poli R, Vanneschi L, Langdon WB, Mcphee NF (2010) Theoretical results in genetic programming: the next ten years? Genet Program Evolvable Mach 11(3):285–320Google Scholar
  112. Qiao Z, McAleer M, Wong WK (2009) Linear and nonlinear causality between changes in consumption and consumer attitudes. Econ Lett 102(3):161–164Google Scholar
  113. Robinzonov N, Tutz G, Hothorn T (2012) Boosting techniques for nonlinear time series models. AStA Adv Stat Anal 96(1):99–122Google Scholar
  114. Sarradj E, Geyer T (2014) Symbolic regression modeling of noise generation at porous airfoils. J Sound Vib 333(14):3189–3202Google Scholar
  115. Schmeling M, Schrimpf A (2011) Expected inflation, expected stock returns, and money illusion: what can we learn from survey expectations. Eur Econ Rev 55(5):702–719Google Scholar
  116. Seitz H (1988) The estimation of inflation forecasts from business survey data. Appl Econ 20(4):427–438Google Scholar
  117. Smith J, McAleer M (1995) Alternative procedures for converting qualitative response data to quantitative expectations: an application to Australian manufacturing. J Appl Econom 10(2):165–185Google Scholar
  118. Terai A (2009) Measurement error in estimating inflation expectations from survey data: an evaluation by Monte Carlo simulations. J Bus Cycle Meas Anal 8(2):133–156Google Scholar
  119. Theil H (1952) On the time shape of economic microvariables and the Munich Business Test. Rev l’Inst Int Stat 20:105–120Google Scholar
  120. Thinyane H, Millin J (2011) An investigation into the use of intelligent systems for currency trading. Comput Econ 37(4):363–374Google Scholar
  121. Vasilakis GA, Theofilatos KA, Georgopoulos EF, Karathanasopoulos A, Likothanassis SD (2013) A genetic programming approach for EUR/USD exchange rate forecasting and trading. Comput Econ 42(4):415–431Google Scholar
  122. Vermeulen P (2014) An evaluation of business survey indices for short-term forecasting: balance method versus Carlson–Parkin method. Int J Forecast 30(4):882–897Google Scholar
  123. Visco I (1984) Price expectations in rising inflation. North-Holland, AmsterdamGoogle Scholar
  124. Vladislavleva E, Smits G, den Hertog D (2010) On the importance of data balancing for symbolic regression. IEEE Trans Evol Comput 14(2):252–277Google Scholar
  125. Wei LY (2013) A hybrid model based on ANFIS and adaptive expectation genetic algorithm to forecast TAIEX. Econ Model 33:893–899Google Scholar
  126. Wilms I, Gelper S, Croux C (2016) The predictive power of the business and bank sentiment of firms: a high-dimensional Granger Causality approach. Eur J Oper Res 254(1):138–147Google Scholar
  127. Wilson G, Banzhaf W (2009) Prediction of interday stock prices using developmental and linear genetic programming. In: Giacobini M et al (eds) Applications of evolutionary computing. Springer, Berlin, pp 172–181Google Scholar
  128. Wren-Lewis S (1986) An econometric model of U.K. manufacturing employment using survey data on expected output. J Appl Econom 10(2):165–185Google Scholar
  129. Wu CH, Chou HJ, Su WH (2008) Direct transformation of coordinates for GPS positioning using the techniques of genetic programming and symbolic regression. Eng Appl Artif Intell 21(8):1347–1359Google Scholar
  130. Yang G, Li X, Wang J, Lian L, Ma T (2015) Modeling oil production based on symbolic regression. Energy Policy 82(1):48–61Google Scholar
  131. Yao L, Lin CC (2009) Identification of nonlinear systems by the genetic programming-based volterra filter. IET Signal Proc 3(2):93–105Google Scholar
  132. Yu T, Chen S, Kuo TW (2004) A genetic programming approach to model international short-term capital flow. Appl Artif Intell Finance Econ 19:45–70Google Scholar
  133. Zameer A, Arshad J, Khan A, Raja MAZ (2017) Intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks. Energy Convers Manag 134:361–372Google Scholar
  134. Zelinka I, Oplatkova Z, Nolle L (2005) Analytic programming: symbolic regression by means of arbitrary evolutionary algorithms. Int J Simul Syst Sci Technol 6(9):44–56Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.AQR-IREAUniversity of BarcelonaBarcelonaSpain
  2. 2.Department of Signal Theory and CommunicationsPolytechnic University of CatalunyaBarcelonaSpain
  3. 3.Riskcenter-IREA (Institute of Applied Economics Research)University of BarcelonaBarcelonaSpain

Personalised recommendations