IDEAS home Printed from https://ideas.repec.org/a/eee/labeco/v60y2019icp64-74.html
   My bibliography  Save this article

Unemployment expectations: A socio-demographic analysis of the effect of news

Author

Listed:
  • Sorić, Petar
  • Lolić, Ivana
  • Claveria, Oscar
  • Monte, Enric
  • Torra, Salvador

Abstract

In this study, we evaluate the effect of news on consumer unemployment expectations for sixteen socio-demographic groups. To this end, we construct an unemployment sentiment indicator and extract news about several economic variables. By means of genetic programming we estimate symbolic regressions that link unemployment rates in the Euro Area to qualitative expectations about a wide range of economic variables. We then use the evolved expressions to compute unemployment expectations for each consumer group. We first assess the out-of-sample forecast accuracy of the evolved indicators, obtaining better forecasts for the leading unemployment sentiment indicator than for the coincident one. Results are similar across the different socio-demographic groups. The best forecast results are obtained for respondents between 30 and 49 years. The group where we observe the bigger differences among categories is the occupation, where the lowest forecast errors are obtained for the unemployed respondents. Next, we link news about inflation, industrial production, and stock markets to unemployment expectations. With this aim we match positive and negative news with consumers’ unemployment sentiment using a distributed lag regression model for each news item. We find asymmetries in the responses of consumers’ unemployment expectations to economic news: they tend to be stronger in the case of negative news, especially in the case of inflation.

Suggested Citation

  • Sorić, Petar & Lolić, Ivana & Claveria, Oscar & Monte, Enric & Torra, Salvador, 2019. "Unemployment expectations: A socio-demographic analysis of the effect of news," Labour Economics, Elsevier, vol. 60(C), pages 64-74.
    • Handle: RePEc:eee:labeco:v:60:y:2019:i:c:p:64-74
    DOI: 10.1016/j.labeco.2019.06.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0927537119300582
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.labeco.2019.06.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hansson, Jesper & Jansson, Per & Lof, Marten, 2005. "Business survey data: Do they help in forecasting GDP growth?," International Journal of Forecasting, Elsevier, vol. 21(2), pages 377-389.
    2. Oscar Claveria, 2019. "Forecasting the unemployment rate using the degree of agreement in consumer unemployment expectations," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 53(1), pages 1-10, December.
    3. Christian Hutter & Enzo Weber, 2015. "Constructing a new leading indicator for unemployment from a survey among German employment agencies," Applied Economics, Taylor & Francis Journals, vol. 47(33), pages 3540-3558, July.
    4. Christiansen, Charlotte & Eriksen, Jonas Nygaard & Møller, Stig Vinther, 2014. "Forecasting US recessions: The role of sentiment," Journal of Banking & Finance, Elsevier, vol. 49(C), pages 459-468.
    5. Reis, Ricardo, 2006. "Inattentive consumers," Journal of Monetary Economics, Elsevier, vol. 53(8), pages 1761-1800, November.
    6. Alexandridis, Antonis K. & Kampouridis, Michael & Cramer, Sam, 2017. "A comparison of wavelet networks and genetic programming in the context of temperature derivatives," International Journal of Forecasting, Elsevier, vol. 33(1), pages 21-47.
    7. Nguyen, Viet Hoang & Claus, Edda, 2013. "Good news, bad news, consumer sentiment and consumption behavior," Journal of Economic Psychology, Elsevier, vol. 39(C), pages 426-438.
    8. Paul C. Henry, 2005. "Social Class, Market Situation, and Consumers' Metaphors of (Dis)Empowerment," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 31(4), pages 766-778, March.
    9. Charles F. Manski, 2018. "Survey Measurement of Probabilistic Macroeconomic Expectations: Progress and Promise," NBER Macroeconomics Annual, University of Chicago Press, vol. 32(1), pages 411-471.
    10. Eickmeier, Sandra & Ng, Tim, 2011. "Forecasting national activity using lots of international predictors: An application to New Zealand," International Journal of Forecasting, Elsevier, vol. 27(2), pages 496-511, April.
    11. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "Using Survey Data to Forecast Real Activity with Evolutionary Algorithms. a Cross-Country Analysis," Journal of Applied Economics, Taylor & Francis Journals, vol. 20(2), pages 329-349, November.
    12. Bas Aarle & Cindy Moons, 2017. "Sentiment and Uncertainty Fluctuations and Their Effects on the Euro Area Business Cycle," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 13(2), pages 225-251, November.
    13. Marcos Álvarez-Díaz & Alberto Álvarez, 2005. "Genetic multi-model composite forecast for non-linear prediction of exchange rates," Empirical Economics, Springer, vol. 30(3), pages 643-663, October.
    14. Krzysztof Zagórski & John McDonnell, 1995. "“Consumer Confidence” Indexes as social indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 36(3), pages 227-246, November.
    15. Eichenbaum, Martin & Parker, Jonathan A. (ed.), . "NBER Macroeconomics Annual 2017," National Bureau of Economic Research Books, University of Chicago Press, number 9780226577838, July.
    16. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Evolutionary Computation for Macroeconomic Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 833-849, February.
    17. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    18. Easaw, Joshy & Golinelli, Roberto & Malgarini, Marco, 2013. "What determines households inflation expectations? Theory and evidence from a household survey," European Economic Review, Elsevier, vol. 61(C), pages 1-13.
    19. Martinsen, Kjetil & Ravazzolo, Francesco & Wulfsberg, Fredrik, 2014. "Forecasting macroeconomic variables using disaggregate survey data," International Journal of Forecasting, Elsevier, vol. 30(1), pages 65-77.
    20. Beenstock, Michael & Szpiro, George, 2002. "Specification search in nonlinear time-series models using the genetic algorithm," Journal of Economic Dynamics and Control, Elsevier, vol. 26(5), pages 811-835, May.
    21. Kajal Lahiri & Yongchen Zhao, 2016. "Determinants of Consumer Sentiment Over Business Cycles: Evidence from the US Surveys of Consumers," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(2), pages 187-215, December.
    22. C. Lawrenz & F. Westerhoff, 2003. "Modeling Exchange Rate Behavior with a Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 21(3), pages 209-229, June.
    23. Marković, Dušan & Petković, Dalibor & Nikolić, Vlastimir & Milovančević, Miloš & Petković, Biljana, 2017. "Soft computing prediction of economic growth based in science and technology factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 217-220.
    24. Batchelor, R. A., 1986. "The psychophysics of inflation," Journal of Economic Psychology, Elsevier, vol. 7(3), pages 269-290, September.
    25. Sznajderska, Anna, 2014. "Asymmetric effects in the Polish monetary policy rule," Economic Modelling, Elsevier, vol. 36(C), pages 547-556.
    26. Santoro, Emiliano & Petrella, Ivan & Pfajfar, Damjan & Gaffeo, Edoardo, 2014. "Loss aversion and the asymmetric transmission of monetary policy," Journal of Monetary Economics, Elsevier, vol. 68(C), pages 19-36.
    27. Maria Billstam & Kristina Frändén & Johan Samuelsson & Pär Österholm, 2017. "Quasi-Real-Time Data of the Economic Tendency Survey," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 13(1), pages 105-138, May.
    28. Christopher A. Pissarides, 2013. "Unemployment in the Great Recession," Economica, London School of Economics and Political Science, vol. 80(319), pages 385-403, July.
    29. Dolado, Juan J. & Maria-Dolores, Ramon & Naveira, Manuel, 2005. "Are monetary-policy reaction functions asymmetric?: The role of nonlinearity in the Phillips curve," European Economic Review, Elsevier, vol. 49(2), pages 485-503, February.
    30. Coleman, Richard P, 1983. "The Continuing Significance of Social Class to Marketing," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 10(3), pages 265-280, December.
    31. Georgios Vasilakis & Konstantinos Theofilatos & Efstratios Georgopoulos & Andreas Karathanasopoulos & Spiros Likothanassis, 2013. "A Genetic Programming Approach for EUR/USD Exchange Rate Forecasting and Trading," Computational Economics, Springer;Society for Computational Economics, vol. 42(4), pages 415-431, December.
    32. repec:bla:jfinan:v:59:y:2004:i:3:p:1201-1234 is not listed on IDEAS
    33. Oscar Claveria, 2019. "Forecasting the unemployment rate using the degree of agreement in consumer unemployment expectations," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 53(1), pages 1-10, December.
    34. Stephen Bruestle & W. Mark Crain, 2015. "A mean-variance approach to forecasting with the consumer confidence index," Applied Economics, Taylor & Francis Journals, vol. 47(23), pages 2430-2444, May.
    35. Bas Aarle & Marcus Kappler, 2012. "Economic sentiment shocks and fluctuations in economic activity in the euro area and the USA," Intereconomics: Review of European Economic Policy, Springer;ZBW - Leibniz Information Centre for Economics;Centre for European Policy Studies (CEPS), vol. 47(1), pages 44-51, January.
    36. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    37. Schaling, Eric, 2004. "The Nonlinear Phillips Curve and Inflation Forecast Targeting: Symmetric versus Asymmetric Monetary Policy Rules," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 36(3), pages 361-386, June.
    38. Marcos Alvarez-Diaz & Josep Mateu-Sbert & Jaume Rossello-Nadal, 2009. "Forecasting tourist arrivals to Balearic Islands using genetic programming," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 1(1), pages 64-75.
    39. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Empirical modelling of survey-based expectations for the design of economic indicators in five European regions," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 46(2), pages 205-227, May.
    40. Vermeulen, Philip, 2014. "An evaluation of business survey indices for short-term forecasting: Balance method versus Carlson–Parkin method," International Journal of Forecasting, Elsevier, vol. 30(4), pages 882-897.
    41. Sydney C. Ludvigson, 2004. "Consumer Confidence and Consumer Spending," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 29-50, Spring.
    42. Jacobsen, Ben & Lee, John B. & Marquering, Wessel & Zhang, Cherry Y., 2014. "Gender differences in optimism and asset allocation," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PB), pages 630-651.
    43. Peter Clark & Douglas Laxton & David Rose, 1996. "Asymmetry in the U.S. Output-Inflation Nexus," IMF Staff Papers, Palgrave Macmillan, vol. 43(1), pages 216-251, March.
    44. Fornari, Fabio & Monticelli, Carlo & Pericoli, Marcello & Tivegna, Massimo, 2002. "The impact of news on the exchange rate of the lira and long-term interest rates," Economic Modelling, Elsevier, vol. 19(4), pages 611-639, August.
    45. A. W. Phillips, 1958. "The Relation Between Unemployment and the Rate of Change of Money Wage Rates in the United Kingdom, 1861–1957," Economica, London School of Economics and Political Science, vol. 25(100), pages 283-299, November.
    46. Bill Lucarelli, 2011. "German neomercantilism and the European sovereign debt crisis," Journal of Post Keynesian Economics, Taylor & Francis Journals, vol. 34(2), pages 205-224.
    47. Koutmos, Gregory & Booth, G Geoffrey, 1995. "Asymmetric volatility transmission in international stock markets," Journal of International Money and Finance, Elsevier, vol. 14(6), pages 747-762, December.
    48. De Bondt, Werner F M & Thaler, Richard, 1985. "Does the Stock Market Overreact?," Journal of Finance, American Finance Association, vol. 40(3), pages 793-805, July.
    49. Yang, Guangfei & Li, Xianneng & Wang, Jianliang & Lian, Lian & Ma, Tieju, 2015. "Modeling oil production based on symbolic regression," Energy Policy, Elsevier, vol. 82(C), pages 48-61.
    50. Aragón, Fernando M. & Rud, Juan Pablo & Toews, Gerhard, 2018. "Resource shocks, employment, and gender: Evidence from the collapse of the UK coal industry," Labour Economics, Elsevier, vol. 52(C), pages 54-67.
    51. Eichenbaum, Martin & Parker, Jonathan A. (ed.), 2018. "NBER Macroeconomics Annual 2017," National Bureau of Economic Research Books, University of Chicago Press, number 9780226577661, July.
    52. Claes H. De Vreese & Hajo G. Boomgaarden, 2006. "Media Effects on Public Opinion about the Enlargement of the European Union," Journal of Common Market Studies, Wiley Blackwell, vol. 44(2), pages 419-436, June.
    53. repec:iab:iabjlr:v:53:i:1:p:art.3 is not listed on IDEAS
    54. James Mitchell & Richard J. Smith & Martin R. Weale, 2005. "Forecasting Manufacturing Output Growth Using Firm‐Level Survey Data," Manchester School, University of Manchester, vol. 73(4), pages 479-499, July.
    55. Abberger, Klaus, 2007. "Qualitative business surveys and the assessment of employment -- A case study for Germany," International Journal of Forecasting, Elsevier, vol. 23(2), pages 249-258.
    56. Michael Graff, 2010. "Does a multi-sectoral design improve indicator-based forecasts of the GDP growth rate? Evidence from Switzerland," Applied Economics, Taylor & Francis Journals, vol. 42(21), pages 2759-2781.
    57. Bhattarai, Keshab, 2016. "Unemployment–inflation trade-offs in OECD countries," Economic Modelling, Elsevier, vol. 58(C), pages 93-103.
    58. Dias, João, 2017. "Unemployment and sovereign debt crisis in the Eurozone: A k-means-r analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 108-117.
    59. James Mitchell & Richard J. Smith & Martin R. Weale, 2005. "Forecasting Manufacturing Output Growth Using Firm‐Level Survey Data," Manchester School, University of Manchester, vol. 73(4), pages 479-499, July.
    60. Wei, Liang-Ying, 2013. "A hybrid model based on ANFIS and adaptive expectation genetic algorithm to forecast TAIEX," Economic Modelling, Elsevier, vol. 33(C), pages 893-899.
    61. Leslie McGranahan & Maude Toussaint-Comeau, 2006. "Variations in consumer sentiment across demographic groups," Economic Perspectives, Federal Reserve Bank of Chicago, vol. 30(Q I), pages 19-38.
    62. Shu-Heng Chen & Tzu-Wen Kuo & Kong-Mui Hoi, 2008. "Genetic Programming and Financial Trading: How Much About "What We Know"," Springer Optimization and Its Applications, in: Constantin Zopounidis & Michael Doumpos & Panos M. Pardalos (ed.), Handbook of Financial Engineering, pages 99-154, Springer.
    63. James Mitchell & Richard J. Smith & Martin R. Weale & Stephen Wright & Eduardo L. Salazar, 2005. "An Indicator of Monthly GDP and an Early Estimate of Quarterly GDP Growth," Economic Journal, Royal Economic Society, vol. 115(501), pages 108-129, February.
    64. R. Lehmann & K. Wohlrabe, 2017. "Experts, firms, consumers or even hard data? Forecasting employment in Germany," Applied Economics Letters, Taylor & Francis Journals, vol. 24(4), pages 279-283, February.
    65. Wilms, Ines & Gelper, Sarah & Croux, Christophe, 2016. "The predictive power of the business and bank sentiment of firms: A high-dimensional Granger Causality approach," European Journal of Operational Research, Elsevier, vol. 254(1), pages 138-147.
    66. Batchelor, R. A., 1982. "Expectations, output and inflation : The European experience," European Economic Review, Elsevier, vol. 17(1), pages 1-25.
    67. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "A Data-Driven Approach to Construct Survey-Based Indicators by Means of Evolutionary Algorithms," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(1), pages 1-14, January.
    68. Allen, Franklin & Karjalainen, Risto, 1999. "Using genetic algorithms to find technical trading rules," Journal of Financial Economics, Elsevier, vol. 51(2), pages 245-271, February.
    69. Philip R. Lane, 2012. "The European Sovereign Debt Crisis," Journal of Economic Perspectives, American Economic Association, vol. 26(3), pages 49-68, Summer.
    70. Chen, Shiu-Sheng, 2011. "Lack of consumer confidence and stock returns," Journal of Empirical Finance, Elsevier, vol. 18(2), pages 225-236, March.
    71. Dr Martin Weale & Dr. James Mitchell, 2005. "Forecasting manufacturing output growth using firm-level survey data," National Institute of Economic and Social Research (NIESR) Discussion Papers 251, National Institute of Economic and Social Research.
    72. Surico, Paolo, 2007. "The Fed's monetary policy rule and U.S. inflation: The case of asymmetric preferences," Journal of Economic Dynamics and Control, Elsevier, vol. 31(1), pages 305-324, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Petar Soric & Mateo Zokalj & Marija Logarusic, 2020. "Economic determinants of Croatian consumer confidence: real estate prices vs. macroeconomy," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 18(2B), pages 240-257.
    2. Blanchflower, David G. & Bryson, Alex, 2021. "The Economics of Walking About and Predicting Unemployment," GLO Discussion Paper Series 922, Global Labor Organization (GLO).
    3. Oscar Claveria & Enric Monte & Salvador Torra, 2020. "Spectral analysis of business and consumer survey data," IREA Working Papers 202006, University of Barcelona, Research Institute of Applied Economics, revised May 2020.
    4. Oscar Claveria & Enric Monte & Salvador Torra, 2021. "Frequency domain analysis and filtering of business and consumer survey expectations," International Economics, CEPII research center, issue 166, pages 42-57.
    5. Petar Soric & Oscar Claveria, 2021. ""Employment uncertainty a year after the irruption of the covid-19 pandemic"," IREA Working Papers 202112, University of Barcelona, Research Institute of Applied Economics, revised May 2021.
    6. Lenka Mynaříková & Vít Pošta, 2023. "The Effect of Consumer Confidence and Subjective Well-being on Consumers’ Spending Behavior," Journal of Happiness Studies, Springer, vol. 24(2), pages 429-453, February.
    7. Silveira, Jaylson Jair da & Lima, Gilberto Tadeu, 2021. "Can workers’ increased pessimism about the labor market conditions raise unemployment?," International Review of Economics & Finance, Elsevier, vol. 72(C), pages 125-134.
    8. Claveria, Oscar & Monte, Enric & Torra, Salvador, 2020. "Economic forecasting with evolved confidence indicators," Economic Modelling, Elsevier, vol. 93(C), pages 576-585.
    9. Zaremba, Adam & Cakici, Nusret & Bianchi, Robert J. & Long, Huaigang, 2023. "Interest rate changes and the cross-section of global equity returns," Journal of Economic Dynamics and Control, Elsevier, vol. 147(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "“Tracking economic growth by evolving expectations via genetic programming: A two-step approach”," AQR Working Papers 201801, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2018.
    2. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Evolutionary Computation for Macroeconomic Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 833-849, February.
    3. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Empirical modelling of survey-based expectations for the design of economic indicators in five European regions," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 46(2), pages 205-227, May.
    4. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "Let the data do the talking: Empirical modelling of survey-based expectations by means of genetic programming," IREA Working Papers 201711, University of Barcelona, Research Institute of Applied Economics, revised May 2017.
    5. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "A Data-Driven Approach to Construct Survey-Based Indicators by Means of Evolutionary Algorithms," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(1), pages 1-14, January.
    6. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "A new approach for the quantification of qualitative measures of economic expectations," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(6), pages 2685-2706, November.
    7. Petar Sorić & Blanka Škrabić Perić & Marina Matošec, 2022. "Breaking new grounds: a fresh insight into the leading properties of business and consumer survey indicators," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4511-4535, December.
    8. Oscar Claveria, 2021. "Forecasting with Business and Consumer Survey Data," Forecasting, MDPI, vol. 3(1), pages 1-22, February.
    9. Oscar Claveria & Enric Monte & Salvador Torra, 2020. "“Spectral analysis of business and consumer survey data”," AQR Working Papers 2012002, University of Barcelona, Regional Quantitative Analysis Group, revised May 2020.
    10. Robert Lehmann & Antje Weyh, 2016. "Forecasting Employment in Europe: Are Survey Results Helpful?," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 81-117, September.
    11. Blanchflower, David G. & Bryson, Alex, 2021. "The Economics of Walking About and Predicting Unemployment," GLO Discussion Paper Series 922, Global Labor Organization (GLO).
    12. Lenka Mynaříková & Vít Pošta, 2023. "The Effect of Consumer Confidence and Subjective Well-being on Consumers’ Spending Behavior," Journal of Happiness Studies, Springer, vol. 24(2), pages 429-453, February.
    13. Kevin Moran & Simplice Aimé Nono & Imad Rherrad, 2018. "Forecasting with Many Predictors: How Useful are National and International Confidence Data?," Cahiers de recherche 1814, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
    14. Lui, Silvia & Mitchell, James & Weale, Martin, 2011. "The utility of expectational data: Firm-level evidence using matched qualitative-quantitative UK surveys," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1128-1146, October.
    15. P�r Österholm, 2014. "Survey data and short-term forecasts of Swedish GDP growth," Applied Economics Letters, Taylor & Francis Journals, vol. 21(2), pages 135-139, January.
    16. Oscar Claveria, 2019. "Forecasting the unemployment rate using the degree of agreement in consumer unemployment expectations," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 53(1), pages 1-10, December.
    17. Oscar Claveria & Enric Monte & Salvador Torra, 2021. ""Nowcasting and forecasting GDP growth with machine-learning sentiment indicators"," IREA Working Papers 202103, University of Barcelona, Research Institute of Applied Economics, revised Feb 2021.
    18. Claveria, Oscar & Monte, Enric & Torra, Salvador, 2020. "Economic forecasting with evolved confidence indicators," Economic Modelling, Elsevier, vol. 93(C), pages 576-585.
    19. Breitung, Jörg & Schmeling, Maik, 2013. "Quantifying survey expectations: What’s wrong with the probability approach?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 142-154.
    20. Lui, Silvia & Mitchell, James & Weale, Martin, 2011. "The utility of expectational data: Firm-level evidence using matched qualitative-quantitative UK surveys," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1128-1146, October.

    More about this item

    Keywords

    Unemployment; Expectations; News; Consumer behaviour; Forecasting; Genetic programming;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • J10 - Labor and Demographic Economics - - Demographic Economics - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:labeco:v:60:y:2019:i:c:p:64-74. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/labeco .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.