1. Labour Market Expectations and Unemployment in Europe. (2023). Bryson, Alex ; Blanchflower, David.
   In: IZA Discussion Papers.
   RePEc:iza:izadps:dp15905.

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2. The Economics of Walking About and Predicting Unemployment. (2021). Bryson, Alex ; Blanchflower, David.
   In: GLO Discussion Paper Series.
   RePEc:zbw:glodps:922.

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3. The Economics of Walking About and Predicting Unemployment. (2021). Bryson, Alex ; Blanchflower, David.
   In: DoQSS Working Papers.
   RePEc:qss:dqsswp:2124.

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4. Spectral analysis of business and consumer survey data. (2020). Claveria, Oscar ; Torra, Salvador ; Monte, Enric.
   In: IREA Working Papers.
   RePEc:ira:wpaper:202006.

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5. Unemployment expectations: A socio-demographic analysis of the effect of news. (2019). Sorić, Petar ; Lolić, Ivana ; Claveria, Oscar ; Torra, Salvador ; Monte, Enric.
   In: Labour Economics.
   RePEc:eee:labeco:v:60:y:2019:i:c:p:64-74.

   Full description at Econpapers || Download paper

1. Abberger K (2007) Qualitative business surveys and the assessment of employment—a case study for Germany. Int J Forecast 23(2):249–258.

2. Acosta-González E, Fernández F (2014) Forecasting financial failure of firms via genetic algorithms. Comput Econ 43(2):133–157.
   Paper not yet in RePEc: Add citation now
   

3. Acosta-González E, Fernández F, Sosvilla S (2012) On factors explaining the 2008 financial crisis. Econ Lett 115(2):215–217.

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):21–47.

5. Altug S, Çakmakli C (2016) Forecasting inflation using survey expectations and target inflation: evidence from Brazil and Turkey. Int J Forecast 32(1):138–153.

6. Álvarez-Díaz M, Álvarez A (2005) Genetic multi-model composite forecast for non-linear prediction of exchange rates. Empir Econ 30(3):643–663.
   Paper not yet in RePEc: Add citation now
   
7. Anderson O (1952) The business test of the IFO-Institute for Economic Research, Munich, and its theoretical model. Rev l’Inst Int Stat 20:1–17.
   Paper not yet in RePEc: Add citation now
   
8. Łyziak T, Mackiewicz-Łyziak J (2014) Do consumers in Europe anticipate future inflation? Eastern Eur Econ 52(3):5–32.
   Paper not yet in RePEc: Add citation now
   

9. Balcombe K (1996) The Carlson–Parkin method applied to NZ price expectations using QSBO survey data. Econ Lett 51(1):51–57.

10. 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, CA.
    Paper not yet in RePEc: Add citation now
    
11. 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):75–82.
    Paper not yet in RePEc: Add citation now
    

12. Batchelor R, Dua P (1992) Survey expectations in the time series consumption function. Rev Econ Stat 74(4):598–606.

13. Batchelor R, Dua P (1998) Improving macro-economic forecasts. Int J Forecast 14(1):71–81.

14. Batchelor R, Orr AB (1988) Inflation expectations revisited. Economica 55(2019):317–331.

15. Batchelor RA (1981) Aggregate expectations under the stable laws. J Econom 16(2):199–210.

16. Batchelor RA (1982) Expectations, output and inflation: the European experience. Eur Econ Rev 17(1):1–25.

17. Bennett A (1984) Output expectations of manufacturing industry. Appl Econ 16(6):869–879.
    Paper not yet in RePEc: Add citation now
    
18. Bergström R (1995) The relationship between manufacturing production and different business survey series in Sweden 1968–1992. Int J Forecast 11(3):379–393.
    Paper not yet in RePEc: Add citation now
    

19. Berk JM (1999) Measuring inflation expectations: a survey data approach. Appl Econ 31(11):1467–1480.

20. Białowolski P (2016) The influence of negative response style on survey-based household inflation expectations. Qual Quant 50(2):509–528.
    Paper not yet in RePEc: Add citation now
    

21. Bovi M (2013) Are the representative agent’s beliefs based on efficient econometric models? J Econ Dyn Control 37(3):633–648.

22. Bovi M (2016) The tale of two expectations. Qual Quant 50(6):2677–2705.

23. Breitung J, Schmeling M (2013) Quantifying survey expectations: What’s wrong with the probability approach? Int J Forecast 29(1):142–154.

24. Bruestle S, Crain WM (2015) A mean-variance approach to forecasting with the consumer confidence index. Appl Econ 47(23):2430–2444.

25. Bruno G (2014) Consumer confidence and consumption forecast: a non-parametric approach. Empirica 41(1):37–52.

26. Carlson JA, Parkin M (1975) Inflation expectations. Economica 42(166):123–138.

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):7061–7067.
    Paper not yet in RePEc: Add citation now
    
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 419–455.
    Paper not yet in RePEc: Add citation now
    

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 99–154.

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 1–15.
    Paper not yet in RePEc: Add citation now
    

31. Christiansen C, Eriksen J, Moller S (2014) Forecasting US recessions: the role of sentiment. J Bank Finance 49:459–468.

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 181–190.
    Paper not yet in RePEc: Add citation now
    
33. Claveria O, Monte E, Torra S (2015) A new forecasting approach for the hospitality industry. Int J Contemp Hosp Manage 27(7):1520–1538.
    Paper not yet in RePEc: Add citation now
    

34. 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):177–189.

35. Claveria O, Monte E, Torra S (2017) A new approach for the quantification of qualitative measures of economic expectations. Qual Quant 51(6):2685–2706.

36. Claveria O, Pons E, Ramos R (2007) Business and consumer expectations and macroeconomic forecasts. Int J Forecast 23(1):47–69.

37. Claveria O, Pons E, Suriñach J (2006) Quantification of expectations. Are they useful for forecasting inflation? Economic Issues 11(2):19–38.

38. Common M (1985) Testing for rational expectations with qualitative survey data. Manch Sch Econ Soc Stat 53(2):138–148.

39. 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.
    Paper not yet in RePEc: Add citation now
    
40. Dabhi VK, Chaudhary S (2015) Empirical modeling using genetic programming: a survey of issues and approaches. Nat Comput 14(2):303–330.
    Paper not yet in RePEc: Add citation now
    

41. Dees S, Brinca PS (2013) Consumer confidence as a predictor of consumption spending: evidence for the United States and the Euro area. Int Econ 134:1–14.

42. 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 29–54.
    Paper not yet in RePEc: Add citation now
    

43. Dreger C, Kholodilin D (2013) Forecasting private consumption by consumer surveys. J Forecast 32(1):10–18.

44. Driver C, Urga G (2004) Transforming qualitative survey data: performance comparisons for the UK. Oxf Bull Econ Stat 66(1):71–89.

45. 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 445–454.
    Paper not yet in RePEc: Add citation now
    
46. Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129.
    Paper not yet in RePEc: Add citation now
    
47. Fogel DB (2006) Evolutionary computation. Toward a new philosophy of machine intelligence, 3rd edn. Wiley, Hoboken.
    Paper not yet in RePEc: Add citation now
    
48. Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution. John Wiley, New York.
    Paper not yet in RePEc: Add citation now
    
49. Fortin FA, De Rainville FM, Gardner MA, Parizeau M, Gagné C (2012) DEAP: evolutionary algorithms made easy. J Mach Learn Res 13(1):2171–2175.
    Paper not yet in RePEc: Add citation now
    

50. Franses PH, Kranendonk HC, Lanser D (2011) One model and various experts: evaluating Dutch macroeconomic forecasts. Int J Forecast 27(2):482–495.

51. Gandomi AH, Roke D (2015) Assessment of artificial neural network and genetic programming as predictive tools. Adv Eng Softw 88:63–72.
    Paper not yet in RePEc: Add citation now
    

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

53. Ghonghadze J, Lux T (2012) Modelling the dynamics of EU economic sentiment indicators: an interaction-based approach. Appl Econ 44(24):3065–3088.

54. Girardi A (2014) Expectations and macroeconomic fluctuations in the Euro area. Econ Lett 125(2):315–318.

55. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Boston.
    Paper not yet in RePEc: Add citation now
    
56. 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 34:286–300.
    Paper not yet in RePEc: Add citation now
    

57. Guizzardi A, Stacchini A (2015) Real-time forecasting regional tourism with business sentiment surveys. Tour Manag 47:213–223.

58. Hansson J, Jansson P, Löf M (2005) Business survey data: Do they help in forecasting GDP growth? Int J Forecast 30(1):65–77.

59. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor.
    Paper not yet in RePEc: Add citation now
    

60. Hutson M, Joutz F, Stekler H (2014) Interpreting and evaluating CESIfo’s World Economic Survey directional forecasts. Econ Model 38:6–11.

61. Hyndman RJ, Koehler AB (2006) Another look at measures of forecast accuracy. Int J Forecast 22(4):679–688.

62. Ivaldi M (1992) Survey evidence on the rationality of expectations. J Appl Econom 7(3):225–241.

63. Jean-Baptiste F (2012) Forecasting with the new Keynesian Phillips curve: evidence from survey data. Econ Lett 117(3):811–813.

64. Jonsson T, Österholm P (2011) The forecasting properties of survey-based wage-growth expectations. Econ Lett 113(3):276–281.

65. Jonsson T, Österholm P (2012) The properties of survey-based inflation expectations in Sweden. Empir Econ 42(1):79–94.

66. Kłopocka K (2017) Does consumer confidence forecast household saving and borrowing behavior? Evidence for Poland. Soc Indic Res 133(2):693–717.
    Paper not yet in RePEc: Add citation now
    
67. Kaboudan MA (2000) Genetic programing prediction of stock prices. Comput Econ 16(3):207–236.
    Paper not yet in RePEc: Add citation now
    
68. Klúčik M (2012) Estimates of foreign trade using genetic programming. In: Proceedings of the 46 the scientific meeting of the Italian Statistical Society.
    Paper not yet in RePEc: Add citation now
    

69. Klein LR, Özmucur S (2010) The use of consumer and business surveys in forecasting. Econ Model 27(6):1453–1462.

70. 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 55–72.
    Paper not yet in RePEc: Add citation now
    
71. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge.
    Paper not yet in RePEc: Add citation now
    
72. 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 101–110.
    Paper not yet in RePEc: Add citation now
    

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

74. Kumar V, Leone R, Gaskins J (1995) Aggregate and disaggregate sector fore-casting using consumer confidence measures. Int J Forecast 11(3):361–377.

75. Löffler G (1999) Refining the Carlson–Parkin method. Econ Lett 64(2):167–171.
    Paper not yet in RePEc: Add citation now
    
76. Lacová Ž, Král P (2015) Measurement and characteristics of enterprise inflation expectations in Slovakia. Proc Econ Finance 30:505–512.
    Paper not yet in RePEc: Add citation now
    

77. 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):1254–1275.

78. Lahiri K, Teigland C (1987) On the normality of probability distributions of inflation and GNP forecasts. Int J Forecast 3(2):269–279.

79. Lahiri K, Zhao Y (2015) Quantifying survey expectations: a critical review and generalization of the Carlson–Parkin method. Int J Forecast 31(1):51–62.

80. 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 49–60.
    Paper not yet in RePEc: Add citation now
    

81. Lawrenz C, Westerhoff F (2003) Modeling exchange rate behaviour with a genetic algorithm. Comput Econ 21(3):209–229.

82. Leduc S, Sill K (2013) Expectations and economic fluctuations: an analysis using survey data. Rev Econ Stat 95(4):1352–1367.

83. Lee KC (1994) Formation of price and cost inflation expectations in British manufacturing industries: a multi-sectoral analysis. Econ J 104(423):372–385.

84. Lehmann R, Wohlrabe K (2017) Experts, firms, consumers or even hard data? Forecasting employment in Germany. Appl Econ Lett 24(4):279–283.

85. Lemmens A, Croux C, Dekimpe MG (2005) On the predictive content of production surveys: a pan-European study. Int J Forecast 21(2):363–375.

86. Maag T (2009) On the accuracy of the probability method for quantifying beliefs about inflation. KOF Working Papers, No. 230, KOF Swiss Economic Institute, Zurich.

87. Makridakis S, Hibon M (2000) The M3-competition: results, conclusions and implications. Int J Forecast 16(4):451–476.

88. Martinsen K, Ravazzolo F, Wulfsberg F (2014) Forecasting macroeconomic variables using disaggregate survey data. Int J Forecast 30(1):65–77.

89. Maschek MK (2010) Intelligent mutation rate control in an economic application of genetic algorithms. Comput Econ 35(1):25–49.

90. 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 36:158–166.

91. Mitchell J, Smith R, Weale M (2002) Quantification of qualitative firm-level survey data. Econ J 112(478):117–135.

92. 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–F129.
    Paper not yet in RePEc: Add citation now
    

93. 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 19–48.

94. Mokinski F, Sheng X, Yang J (2015) Measuring disagreement in qualitative expectations. J Forecast 34(5):405–426.

95. Muth J (1961) Rational expectations and the theory of price movements. Econometrica 29(3):315–335.
    Paper not yet in RePEc: Add citation now
    

96. Nardo M (2003) The quantification of qualitative data: a critical assessment. J Econ Surveys 17(5):645–668.

97. Nardo M, Cabeza-Gutés M (1999) The role of measurement error in rational expectations testing. UAB Working Paper 451, Universitat Autònoma de Barcelona, Barcelona.
    Paper not yet in RePEc: Add citation now
    

98. Nolte I, Pohlmeier W (2007) Using forecasts of forecasters to forecast. Int J Forecast 23(1):15–28.

99. Paloviita M (2006) Inflation dynamics in the euro area and the role of expectations. Empir Econ 31:847–860.

100. 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.
     Paper not yet in RePEc: Add citation now
     

101. Pesaran MH (1985) Formation of inflation expectations in British manufacturing industries. Econ J 95(380):948–975.

102. Pesaran MH (1987) The limits to rational expectations. Basil Blackwell, Oxford.
     Paper not yet in RePEc: Add citation now
     

103. 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 715–776.

104. Poli R, Vanneschi L, Langdon WB, Mcphee NF (2010) Theoretical results in genetic programming: the next ten years? Genet Program Evolvable Mach 11(3):285–320.
     Paper not yet in RePEc: Add citation now
     

105. Qiao Z, McAleer M, Wong WK (2009) Linear and nonlinear causality between changes in consumption and consumer attitudes. Econ Lett 102(3):161–164.

106. Robinzonov N, Tutz G, Hothorn T (2012) Boosting techniques for nonlinear time series models. AStA Adv Stat Anal 96(1):99–122.

107. Sarradj E, Geyer T (2014) Symbolic regression modeling of noise generation at porous airfoils. J Sound Vib 333(14):3189–3202.
     Paper not yet in RePEc: Add citation now
     

108. 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):702–719.

109. Seitz H (1988) The estimation of inflation forecasts from business survey data. Appl Econ 20(4):427–438.
     Paper not yet in RePEc: Add citation now
     

110. 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):165–185.

111. 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):133–156.
     Paper not yet in RePEc: Add citation now
     
112. Theil H (1952) On the time shape of economic microvariables and the Munich Business Test. Rev l’Inst Int Stat 20:105–120.
     Paper not yet in RePEc: Add citation now
     
113. Thinyane H, Millin J (2011) An investigation into the use of intelligent systems for currency trading. Comput Econ 37(4):363–374.
     Paper not yet in RePEc: Add citation now
     

114. 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):415–431.

115. Vermeulen P (2014) An evaluation of business survey indices for short-term forecasting: balance method versus Carlson–Parkin method. Int J Forecast 30(4):882–897.

116. Visco I (1984) Price expectations in rising inflation. North-Holland, Amsterdam.
     Paper not yet in RePEc: Add citation now
     
117. Vladislavleva E, Smits G, den Hertog D (2010) On the importance of data balancing for symbolic regression. IEEE Trans Evol Comput 14(2):252–277.
     Paper not yet in RePEc: Add citation now
     

118. Wei LY (2013) A hybrid model based on ANFIS and adaptive expectation genetic algorithm to forecast TAIEX. Econ Model 33:893–899.

119. 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):138–147.

120. 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 172–181.
     Paper not yet in RePEc: Add citation now
     

121. Wren-Lewis S (1986) An econometric model of U.K. manufacturing employment using survey data on expected output. J Appl Econom 10(2):165–185.

122. 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):1347–1359.
     Paper not yet in RePEc: Add citation now
     

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

124. Yao L, Lin CC (2009) Identification of nonlinear systems by the genetic programming-based volterra filter. IET Signal Proc 3(2):93–105.
     Paper not yet in RePEc: Add citation now
     
125. Yu T, Chen S, Kuo TW (2004) A genetic programming approach to model international short-term capital flow. Appl Artif Intell Finance Econ 19:45–70.
     Paper not yet in RePEc: Add citation now
     
126. 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 134:361–372.
     Paper not yet in RePEc: Add citation now
     
127. 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):44–56.
     Paper not yet in RePEc: Add citation now
     

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   In: Labour Economics.
   RePEc:eee:labeco:v:74:y:2022:i:c:s0927537121001329.

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2. Are low frequency macroeconomic variables important for high frequency electricity prices?. (2022). Rossini, Luca ; Ravazzolo, Francesco ; Foroni, Claudia.
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   RePEc:arx:papers:2007.13566.

   Full description at Econpapers || Download paper

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   In: GLO Discussion Paper Series.
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4. The Economics of Walking About and Predicting Unemployment. (2021). Bryson, Alex ; Blanchflower, David.
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   RePEc:qss:dqsswp:2124.

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5. Employment uncertainty a year after the irruption of the covid-19 pandemic.. (2021). Sorić, Petar ; Claveria, Oscar ; Soric, Petar.
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   RePEc:ira:wpaper:202112.

   Full description at Econpapers || Download paper

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   In: AQR Working Papers.
   RePEc:aqr:wpaper:202104.

   Full description at Econpapers || Download paper

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   Full description at Econpapers || Download paper

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   Full description at Econpapers || Download paper

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    RePEc:iab:iabjlr:v:53:p:art.03.

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    In: Journal for Labour Market Research.
    RePEc:iab:iabjlr:v:53:i:1:p:art.3,10.

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    In: Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement.
    RePEc:spr:soinre:v:135:y:2018:i:1:d:10.1007_s11205-016-1490-3.

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    In: Journal of Business Cycle Research.
    RePEc:spr:jbuscr:v:14:y:2018:i:1:d:10.1007_s41549-017-0022-9.

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18. “Tracking economic growth by evolving expectations via genetic programming: A two-step approach”. (2018). Claveria, Oscar ; Torra, Salvador ; Monte, Enric.
    In: IREA Working Papers.
    RePEc:ira:wpaper:201801.

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19. Das neue ifo Beschäftigungsbarometer. (2018). Wohlrabe, Klaus.
    In: ifo Schnelldienst.
    RePEc:ces:ifosdt:v:71:y:2018:i:09:p:34-36.

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20. “Tracking economic growth by evolving expectations via genetic programming: A two-step approach”. (2018). Claveria, Oscar ; Torra, Salvador ; Monte, Enric.
    In: AQR Working Papers.
    RePEc:aqr:wpaper:201801.

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21. Experts, firms, consumers or even hard data? Forecasting employment in Germany. (2017). Wohlrabe, Klaus ; Lehmann, Robert.
    In: Applied Economics Letters.
    RePEc:taf:apeclt:v:24:y:2017:i:4:p:279-283.

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22. The Predictive Content of Business Survey Indicators: Evidence from SIGE. (2017). Iezzi, Stefano ; Cesaroni, Tatiana.
    In: Journal of Business Cycle Research.
    RePEc:spr:jbuscr:v:13:y:2017:i:1:d:10.1007_s41549-017-0015-8.

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23. Nowcasting real economic activity in the euro area : Assessing the impact of qualitative surveys. (2017). de Antonio Liedo, David ; Basselier, Raisa ; Langenus, Geert.
    In: Working Paper Research.
    RePEc:nbb:reswpp:201712-331.

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24. Let the data do the talking: Empirical modelling of survey-based expectations by means of genetic programming. (2017). Claveria, Oscar ; Torra, Salvador ; Monte, Enric.
    In: IREA Working Papers.
    RePEc:ira:wpaper:201711.

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25. “Let the data do the talking: Empirical modelling of survey-based expectations by means of genetic programming”. (2017). Claveria, Oscar ; Torra, Salvador ; Monte, Enric.
    In: AQR Working Papers.
    RePEc:aqr:wpaper:201706.

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26. Forecasting Employment in Europe: Are Survey Results Helpful?. (2016). Lehmann, Robert ; Weyh, Antje.
    In: Journal of Business Cycle Research.
    RePEc:spr:jbuscr:v:12:y:2016:i:1:d:10.1007_s41549-016-0002-5.

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27. Experts, firms, consumers or even hard data? Forecasting employment in Germany. (2016). Wohlrabe, Klaus ; Lehmann, Robert.
    In: MPRA Paper.
    RePEc:pra:mprapa:69611.

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28. Identification and real-time forecasting of Norwegian business cycles. (2016). Ravazzolo, Francesco ; Jore, Anne Sofie ; Aastveit, Knut Are.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:32:y:2016:i:2:p:283-292.

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29. Forecast of Employment in Switzerland: The Macroeconomic View. (2016). Maitah, Mansoor ; Anova, Petra ; Redl, Karel ; Rezbova, Helena ; Kuzmenko, Elena ; Toth, Daniel.
    In: International Journal of Economics and Financial Issues.
    RePEc:eco:journ1:2016-01-17.

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30. ifo Konjunkturumfragen und Konjunkturanalyse: Band II. (2016). Nierhaus, Wolfgang ; Wollmershauser, Timo.
    In: ifo Forschungsberichte.
    RePEc:ces:ifofob:72.

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31. The Predictive Content of Business Survey Indicators: evidence from SIGE. (2015). Iezzi, Stefano ; Cesaroni, Tatiana.
    In: Working Papers LuissLab.
    RePEc:lui:lleewp:15118.

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32. Forecasting employment in Europe: Are survey results helpful?. (2015). Lehmann, Robert ; Weyh, Antje.
    In: IAB-Discussion Paper.
    RePEc:iab:iabdpa:201530.

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33. The predictive content of business survey indicators: evidence from SIGE. (2015). Iezzi, Stefano ; Cesaroni, Tatiana.
    In: Temi di discussione (Economic working papers).
    RePEc:bdi:wptemi:td_1031_15.

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34. Forecasting macroeconomic variables using disaggregate survey data. (2014). Wulfsberg, Fredrik ; Ravazzolo, Francesco ; Martinsen, Kjetil .
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:30:y:2014:i:1:p:65-77.

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35. Forecasting employment in Europe: Are survey results helpful?. (2014). Lehmann, Robert ; Weyh, Antje.
    In: ifo Working Paper Series.
    RePEc:ces:ifowps:_182.

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36. Das ifo Beschäftigungsbarometer und der deutsche Arbeitsmarkt. (2014). Wohlrabe, Klaus ; Henzel, Steffen.
    In: ifo Schnelldienst.
    RePEc:ces:ifosdt:v:67:y:2014:i:15:p:35-40.

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37. Forecasting recessions in real time. (2014). Ravazzolo, Francesco ; Aastveit, Knut Are ; Jore, Anne Sofie.
    In: Working Paper.
    RePEc:bno:worpap:2014_02.

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38. Do business tendency surveys help in forecasting employment?: A real-time evidence for Switzerland. (2013). Siliverstovs, Boriss.
    In: OECD Journal: Journal of Business Cycle Measurement and Analysis.
    RePEc:oec:stdkab:5k4bxlxjkd32.

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39. Constructing a new leading indicator for unemployment from a survey among German employment agencies. (2013). Weber, Enzo ; Hutter, Christian.
    In: IAB-Discussion Paper.
    RePEc:iab:iabdpa:201317.

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40. The utility of expectational data: Firm-level evidence using matched qualitative-quantitative UK surveys. (2011). Weale, Martin ; Mitchell, James ; Lui, Silvia Sze Wai.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:27:y:2011:i:4:p:1128-1146.

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41. Seven Leading Indexes of New Zealand Employment. (2011). Claus, Edda.
    In: The Economic Record.
    RePEc:bla:ecorec:v:87:y:2011:i:276:p:76-89.

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42. Der ostdeutsche Arbeitsmarkt: Kann das ifo Beschäftigungsbarometer dessen konjunkturelle Dynamik abbilden?. (2010). Lehmann, Robert.
    In: ifo Dresden berichtet.
    RePEc:ces:ifodre:v:17:y:2010:i:06:p:27-32.

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43. Predicting unemployment in short samples with internet job search query data. (2009). Francesco, D'Amuri.
    In: MPRA Paper.
    RePEc:pra:mprapa:18403.

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44. The utility of expectational data: Firm-level evidence using matched qualitative-quantitative UK surveys. (2009). Lui, Silvia ; Weale, Martin ; Mitchell, James.
    In: National Institute of Economic and Social Research (NIESR) Discussion Papers.
    RePEc:nsr:niesrd:2473.

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45. Das ifo Beschäftigungsbarometer: Ein Druckmesser für den deutschen Arbeitsmarkt. (2008). Abberger, Klaus.
    In: ifo Schnelldienst.
    RePEc:ces:ifosdt:v:61:y:2008:i:09:p:19-22.

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46. Ein Beschäftigungsbarometer für die sächsische Wirtschaft. (2008). Vogt, Gerit.
    In: ifo Dresden berichtet.
    RePEc:ces:ifodre:v:15:y:2008:i:01:p:s.41-43.

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47. Mikrodaten im ifo Institut für Wirtschaftsforschung – Bestand, Verwendung und Zugang. (2007). Wohlrabe, Klaus ; Becker, Sascha ; Abberger, Klaus ; Hofmann, Barbara.
    In: AStA Wirtschafts- und Sozialstatistisches Archiv.
    RePEc:spr:astaws:v:1:y:2007:i:1:p:27-42.

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48. Micro Data at the Ifo Institute for Economic Research – The “Ifo Business Survey”, Usage and Access. (2007). Wohlrabe, Klaus ; Becker, Sascha.
    In: ifo Working Paper Series.
    RePEc:ces:ifowps:_47.

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49. Mikrodaten im ifo Institut für Wirtschaftsforschung: Bestand, Verwendung, Zugang. (2007). Wohlrabe, Klaus ; Becker, Sascha ; Abberger, Klaus ; Hofmann, Barbara.
    In: ifo Working Paper Series.
    RePEc:ces:ifowps:_44.

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50. Timing ist alles : Konsequenzen der Entscheidung über die Ziel-1-Fördergebiete der Europäischen Kohäsions- und Strukturpolitik vom Dezember 2005 für den Freistaat Sachsen. (2007). .
    In: ifo Dresden berichtet.
    RePEc:ces:ifodre:v:14:y:2007:i:06:p:3-11.

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