IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v53y2019i2d10.1007_s10614-017-9767-4.html
   My bibliography  Save this article

Evolutionary Computation for Macroeconomic Forecasting

Author

Listed:
  • Oscar Claveria

    (University of Barcelona (UB)
    University of Barcelona)

  • Enric Monte

    (Polytechnic University of Catalunya (UPC))

  • Salvador Torra

    (University of Barcelona (UB))

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.

Suggested Citation

  • 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.
    • Handle: RePEc:kap:compec:v:53:y:2019:i:2:d:10.1007_s10614-017-9767-4
    DOI: 10.1007/s10614-017-9767-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-017-9767-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-017-9767-4?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. Stephane Dees & Pedro Soares Brinca, 2013. "Consumer confidence as a predictor of consumption spending: Evidence for the United States and the Euro area," International Economics, CEPII research center, issue 134, pages 1-14.
    2. Silvia Lui & James Mitchell & Martin Weale, 2011. "Qualitative business surveys: signal or noise?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(2), pages 327-348, April.
    3. 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.
    4. Thomas Jonsson & Pär Österholm, 2012. "The properties of survey-based inflation expectations in Sweden," Empirical Economics, Springer, vol. 42(1), pages 79-94, February.
    5. Jan-Egbert Sturm & Timo Wollmershäuser (ed.), 2005. "Ifo Survey Data in Business Cycle and Monetary Policy Analysis," Contributions to Economics, Springer, number 978-3-7908-1605-1.
    6. Smith, Jeremy & McAleer, Michael, 1995. "Alternative Procedures for Converting Qualitative Response Data to Quantitative Expectations: An Application to Australian Manufacturing," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(2), pages 165-185, April-Jun.
    7. Altug, Sumru & Çakmaklı, Cem, 2016. "Forecasting inflation using survey expectations and target inflation: Evidence for Brazil and Turkey," International Journal of Forecasting, Elsevier, vol. 32(1), pages 138-153.
    8. James Mitchell & Richard J. Smith & Martin R. Weale, 2002. "Quantification of Qualitative Firm-Level Survey Data," Economic Journal, Royal Economic Society, vol. 112(478), pages 117-135, March.
    9. Eduardo Acosta-Gonz�lez & Fernando Fern�ndez-Rodr�guez & Sim�n Sosvilla-Rivero, 2014. "An empirical examination of the determinants of the shadow economy," Applied Economics Letters, Taylor & Francis Journals, vol. 21(5), pages 304-307, March.
    10. Acosta-González, Eduardo & Fernández-Rodríguez, Fernando & Sosvilla-Rivero, Simón, 2012. "On factors explaining the 2008 financial crisis," Economics Letters, Elsevier, vol. 115(2), pages 215-217.
    11. Bovi, Maurizio, 2013. "Are the representative agent’s beliefs based on efficient econometric models?," Journal of Economic Dynamics and Control, Elsevier, vol. 37(3), pages 633-648.
    12. Milutinović, Biljana & Stefanović, Gordana & Đekić, Petar S. & Mijailović, Ivan & Tomić, Mladen, 2017. "Environmental assessment of waste management scenarios with energy recovery using life cycle assessment and multi-criteria analysis," Energy, Elsevier, vol. 137(C), pages 917-926.
    13. Ludo Waltman & Nees Eck & Rommert Dekker & Uzay Kaymak, 2011. "Economic modeling using evolutionary algorithms: the effect of a binary encoding of strategies," Journal of Evolutionary Economics, Springer, vol. 21(5), pages 737-756, December.
    14. Michael Maschek, 2010. "Intelligent Mutation Rate Control in an Economic Application of Genetic Algorithms," Computational Economics, Springer;Society for Computational Economics, vol. 35(1), pages 25-49, January.
    15. Claveria, Oscar & Pons, Ernest & Ramos, Raul, 2007. "Business and consumer expectations and macroeconomic forecasts," International Journal of Forecasting, Elsevier, vol. 23(1), pages 47-69.
    16. Anna Stangl, 2007. "World Economic Survey," Chapters, in: Georg Goldrian (ed.), Handbook of Survey-Based Business Cycle Analysis, chapter 5, Edward Elgar Publishing.
    17. Common, Michael S, 1985. "Testing for Rational Expectations with Qualitative Survey Data," The Manchester School of Economic & Social Studies, University of Manchester, vol. 53(2), pages 138-148, June.
    18. Steffen Henzel & Timo Wollmershäuser, 2005. "An Alternative to the Carlson-Parkin Method for the Quantification of Qualitative Inflation Expectations: Evidence from the Ifo World Economic Survey," ifo Working Paper Series 9, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    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. Johanna Garnitz & Gernot Nerb & Klaus Wohlrabe, 2015. "CESifo World Economic Survey November 2015," ifo World Economic Survey, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 14(04), pages 1-28, November.
    21. Girardi, Alessandro, 2014. "Expectations and macroeconomic fluctuations in the euro area," Economics Letters, Elsevier, vol. 125(2), pages 315-318.
    22. 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.
    23. Nikolay Robinzonov & Gerhard Tutz & Torsten Hothorn, 2012. "Boosting techniques for nonlinear time series models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(1), pages 99-122, January.
    24. Kapetanios, George & Marcellino, Massimiliano & Papailias, Fotis, 2016. "Forecasting inflation and GDP growth using heuristic optimisation of information criteria and variable reduction methods," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 369-382.
    25. repec:ces:ifowes:v:14:y:2015:i:1:p:1-26 is not listed on IDEAS
    26. Pesaran, M Hashem, 1985. "Formation of Inflation Expectations in British Manufacturing Industries," Economic Journal, Royal Economic Society, vol. 95(380), pages 948-975, December.
    27. Hutson, Mark & Joutz, Fred & Stekler, Herman, 2014. "Interpreting and evaluating CESIfo's World Economic Survey directional forecasts," Economic Modelling, Elsevier, vol. 38(C), pages 6-11.
    28. 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.
    29. 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.
    30. Michela Nardo, 2003. "The Quantification of Qualitative Survey Data: A Critical Assessment," Journal of Economic Surveys, Wiley Blackwell, vol. 17(5), pages 645-668, December.
    31. 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.
    32. 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.
    33. 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.
    34. 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.
    35. Schmeling, Maik & Schrimpf, Andreas, 2011. "Expected inflation, expected stock returns, and money illusion: What can we learn from survey expectations?," European Economic Review, Elsevier, vol. 55(5), pages 702-719, June.
    36. Klein, Lawrence R. & Özmucur, Süleyman, 2010. "The use of consumer and business surveys in forecasting," Economic Modelling, Elsevier, vol. 27(6), pages 1453-1462, November.
    37. Jan Marc Berk, 1999. "Measuring inflation expectations: a survey data approach," Applied Economics, Taylor & Francis Journals, vol. 31(11), pages 1467-1480.
    38. Lahiri, Kajal & Zhao, Yongchen, 2015. "Quantifying survey expectations: A critical review and generalization of the Carlson–Parkin method," International Journal of Forecasting, Elsevier, vol. 31(1), pages 51-62.
    39. 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.
    40. Ciaran Driver & Giovanni Urga, 2004. "Transforming Qualitative Survey Data: Performance Comparisons for the UK," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(1), pages 71-89, February.
    41. Stangl, Anna, 2009. "Essays on the Measurement of Economic Expectations," Munich Dissertations in Economics 9823, University of Munich, Department of Economics.
    42. Guizzardi, Andrea & Stacchini, Annalisa, 2015. "Real-time forecasting regional tourism with business sentiment surveys," Tourism Management, Elsevier, vol. 47(C), pages 213-223.
    43. 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.
    44. Stefan Mittnik & Peter Zadrozny, 2005. "Forecasting Quarterly German GDP at Monthly Intervals Using Monthly Ifo Business Conditions Data," Contributions to Economics, in: Jan-Egbert Sturm & Timo Wollmershäuser (ed.), Ifo Survey Data in Business Cycle and Monetary Policy Analysis, pages 19-48, Springer.
    45. 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.
    46. repec:cii:cepiei:2013-q2-134-1 is not listed on IDEAS
    47. Giancarlo Bruno, 2014. "Consumer confidence and consumption forecast: a non-parametric approach," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 41(1), pages 37-52, February.
    48. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    49. Kauppi, Eija & Lassila, Jukka & Teräsvirta, Timo, 1996. "Short-Term Forecasting of Industrial Production with Business Survey Data: Experience from Finland's Great Depression," Discussion Papers 546, The Research Institute of the Finnish Economy.
    50. 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.
    51. 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.
    52. Sylvain Leduc & Keith Sill, 2013. "Expectations and Economic Fluctuations: An Analysis Using Survey Data," The Review of Economics and Statistics, MIT Press, vol. 95(4), pages 1352-1367, October.
    53. Batchelor, Roy & Dua, Pami, 1992. "Survey Expectations in the Time Series Consumption Function," The Review of Economics and Statistics, MIT Press, vol. 74(4), pages 598-606, November.
    54. 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.
    55. Frieder Mokinski & Xuguang (Simon) Sheng & Jingyun Yang, 2015. "Measuring Disagreement in Qualitative Expectations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(5), pages 405-426, August.
    56. Qiao, Zhuo & McAleer, Michael & Wong, Wing-Keung, 2009. "Linear and nonlinear causality between changes in consumption and consumer attitudes," Economics Letters, Elsevier, vol. 102(3), pages 161-164, March.
    57. 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.
    58. repec:ces:ifowes:v:14:y:2015:i:4:p:1-28 is not listed on IDEAS
    59. Jean-Baptiste, Frédo, 2012. "Forecasting with the New Keynesian Phillips curve: Evidence from survey data," Economics Letters, Elsevier, vol. 117(3), pages 811-813.
    60. Jaba Ghonghadze & Thomas Lux, 2012. "Modelling the dynamics of EU economic sentiment indicators: an interaction-based approach," Applied Economics, Taylor & Francis Journals, vol. 44(24), pages 3065-3088, August.
    61. Batchelor, Roy & Dua, Pami, 1998. "Improving macro-economic forecasts: The role of consumer confidence," International Journal of Forecasting, Elsevier, vol. 14(1), pages 71-81, March.
    62. 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.
    63. Nolte, Ingmar & Pohlmeier, Winfried, 2007. "Using forecasts of forecasters to forecast," International Journal of Forecasting, Elsevier, vol. 23(1), pages 15-28.
    64. Carlson, John A & Parkin, J Michael, 1975. "Inflation Expectations," Economica, London School of Economics and Political Science, vol. 42(166), pages 123-138, May.
    65. Lee, Kevin C, 1994. "Formation of Price and Cost Inflation Expectations in British Manufacturing Industries: A Multi-Sectoral Analysis," Economic Journal, Royal Economic Society, vol. 104(423), pages 372-385, March.
    66. Kauppi, Eija & Lassila, Jukka & Terasvirta, Timo, 1996. "Short-term forecasting of industrial production with business survey data: experience from Finland's great depression 1990-1993," International Journal of Forecasting, Elsevier, vol. 12(3), pages 373-381, September.
    67. Jaba Ghonghadze & Thomas Lux, 2012. "Modelling the dynamics of EU economic sentiment indicators: an interaction-based approach," Applied Economics, Taylor & Francis Journals, vol. 44(24), pages 3065-3088, August.
    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. David Alaminos & M. Belén Salas & Manuel A. Fernández-Gámez, 2022. "Quantum Computing and Deep Learning Methods for GDP Growth Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 803-829, February.
    2. David G. Blanchflower & Alex Bryson, 2021. "The Economics of Walking About and Predicting Unemployment," NBER Working Papers 29172, National Bureau of Economic Research, Inc.
    3. 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.
    4. Oscar Claveria, 2021. "Forecasting with Business and Consumer Survey Data," Forecasting, MDPI, vol. 3(1), pages 1-22, February.
    5. José A. Tenreiro Machado & Maria Eugénia Mata & António M. Lopes, 2020. "Fractional Dynamics and Pseudo-Phase Space of Country Economic Processes," Mathematics, MDPI, vol. 8(1), pages 1-17, January.
    6. Christos Alexakis & Michael Dowling & Konstantinos Eleftheriou & Michael Polemis, 2021. "Textual Machine Learning: An Application to Computational Economics Research," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 369-385, January.
    7. Blanchflower, David G. & Bryson, Alex, 2023. "Labour Market Expectations and Unemployment in Europe," IZA Discussion Papers 15905, Institute of Labor Economics (IZA).
    8. Mustafa Ozguven & Chong Yan Gao & Mohamed Yacine Si Tayeb, 2021. "The Utilization of Autoregressive Forecasting Models in Strategic Management," International Journal of Science and Business, IJSAB International, vol. 5(7), pages 170-185.
    9. 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.
    10. Claveria, Oscar & Monte, Enric & Torra, Salvador, 2020. "Economic forecasting with evolved confidence indicators," Economic Modelling, Elsevier, vol. 93(C), pages 576-585.
    11. Oscar Claveria & Enric Monte & Salvador Torra, 2021. "“Nowcasting and forecasting GDP growth with machine-learning sentiment indicators”," AQR Working Papers 202101, University of Barcelona, Regional Quantitative Analysis Group, revised Feb 2021.

    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," Working Papers XREAP2018-4, Xarxa de Referència en Economia Aplicada (XREAP), revised Oct 2018.
    2. 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.
    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, 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.
    6. 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.
    7. Oscar Claveria, 2021. "Forecasting with Business and Consumer Survey Data," Forecasting, MDPI, vol. 3(1), pages 1-22, February.
    8. 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.
    9. Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Self-organizing map analysis of agents’ expectations. Different patterns of anticipation of the 2008 financial crisis”," AQR Working Papers 201508, University of Barcelona, Regional Quantitative Analysis Group, revised Mar 2015.
    10. Lahiri, Kajal & Zhao, Yongchen, 2015. "Quantifying survey expectations: A critical review and generalization of the Carlson–Parkin method," International Journal of Forecasting, Elsevier, vol. 31(1), pages 51-62.
    11. Kajal Lahiri & Yongchen Zhao, 2013. "Quantifying Heterogeneous Survey Expectations: The Carlson-Parkin Method Revisited," Discussion Papers 13-08, University at Albany, SUNY, Department of Economics.
    12. 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.
    13. Olivier Biau & Hélène Erkel-Rousse & Nicolas Ferrari, 2006. "Réponses individuelles aux enquêtes de conjoncture et prévision de la production manufacturière," Économie et Statistique, Programme National Persée, vol. 395(1), pages 91-116.
    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. Juan G Brida & Bibiana Lanzilotta & Lucia I Rosich, 2021. "On the empirical relations between producers expectations and economic growth," Economics Bulletin, AccessEcon, vol. 41(3), pages 1970-1982.
    16. Claveria, Oscar & Monte, Enric & Torra, Salvador, 2020. "Economic forecasting with evolved confidence indicators," Economic Modelling, Elsevier, vol. 93(C), pages 576-585.
    17. Wolfgang Nierhaus & Timo Wollmershäuser, 2016. "ifo Konjunkturumfragen und Konjunkturanalyse: Band II," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 72.
    18. Kaufmann, Daniel & Scheufele, Rolf, 2017. "Business tendency surveys and macroeconomic fluctuations," International Journal of Forecasting, Elsevier, vol. 33(4), pages 878-893.
    19. Troy D. Matheson & James Mitchell & Brian Silverstone, 2010. "Nowcasting and predicting data revisions using panel survey data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(3), pages 313-330.
    20. Alex Botsis & Kevin Lee, 2022. "Nowcasting Using Firm-Level Survey Data; Tracking UK Output Fluctuations and Recessionary Events," Economic Statistics Centre of Excellence (ESCoE) Technical Reports ESCOE-TR-20, Economic Statistics Centre of Excellence (ESCoE).

    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:kap:compec:v:53:y:2019:i:2:d:10.1007_s10614-017-9767-4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.