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Smooth and timely business cycle indicators for noisy Swedish data

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  • Oller, Lars-Erik
  • Tallbom, Christer

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  • Oller, Lars-Erik & Tallbom, Christer, 1996. "Smooth and timely business cycle indicators for noisy Swedish data," International Journal of Forecasting, Elsevier, vol. 12(3), pages 389-402, September.
    • Handle: RePEc:eee:intfor:v:12:y:1996:i:3:p:389-402
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    1. Entorf, Horst, 1993. "Constructing leading indicators from non-balanced sectoral business survey series," International Journal of Forecasting, Elsevier, vol. 9(2), pages 211-225, August.
    2. Hendry, David F. & Richard, Jean-Francois, 1982. "On the formulation of empirical models in dynamic econometrics," Journal of Econometrics, Elsevier, vol. 20(1), pages 3-33, October.
    3. Engle, Robert F, 1974. "Band Spectrum Regression," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 15(1), pages 1-11, February.
    4. Brodsky, Noel & Newbold, Paul, 1994. "Late forecasts and early revisions of United States GNP," International Journal of Forecasting, Elsevier, vol. 10(3), pages 455-460, November.
    5. Howrey, E Philip, 1978. "The Use of Preliminary Data in Econometric Forecasting," The Review of Economics and Statistics, MIT Press, vol. 60(2), pages 193-200, May.
    6. Oller, Lars-Erik, 1990. "Forecasting the business cycle using survey data," International Journal of Forecasting, Elsevier, vol. 6(4), pages 453-461, December.
    7. Granger, C. W. J. & Newbold, Paul, 1986. "Forecasting Economic Time Series," Elsevier Monographs, Elsevier, edition 2, number 9780122951831 edited by Shell, Karl.
    8. de Leeuw, Frank, 1990. "The Reliability of U.S. Gross National Product," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 191-203, April.
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    Cited by:

    1. Lemmens, A. & Croux, C. & Dekimpe, M.G., 2004. "Decomposing Granger Causality over the Spectrum," ERIM Report Series Research in Management ERS-2004-102-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    2. Hansson, Jesper & Jansson, Per & Löf, Mårten, 2003. "Business Survey Data: Do They Help in Forecasting the Macro Economy?," Working Papers 84, National Institute of Economic Research.
    3. Lemmens, A. & Croux, C. & Dekimpe, M.G., 2004. "On The Predictive Content Of Production Surveys: A Pan-European Study," ERIM Report Series Research in Management ERS-2004-017-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    4. Bergvall, Anders & Forsfält, Tomas & Hjelm, Göran & Nilsson, Jonny & Vartiainen, Juhana, 2007. "KIMOD 1.0 Documentation of NIER´s Dynamic Macroeconomic General Equilibrium Model of the Swedish Economy," Working Papers 100, National Institute of Economic Research.
    5. Lemmens, A. & Croux, C. & Dekimpe, M.G., 2005. "On the Predictive Content of Production Surveys : a Pan-European Study," Other publications TiSEM adab9f0e-7dfd-4dc4-bd92-b, Tilburg University, School of Economics and Management.
    6. Boriss Siliverstovs, 2013. "Do business tendency surveys help in forecasting employment?: A real-time evidence for Switzerland," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2013(2), pages 129-151.
    7. Garcia-Ferrer, Antonio & Bujosa-Brun, Marcos, 2000. "Forecasting OECD industrial turning points using unobserved components models with business survey data," International Journal of Forecasting, Elsevier, vol. 16(2), pages 207-227.
    8. Bengoechea, Pilar & Camacho, Maximo & Perez-Quiros, Gabriel, 2006. "A useful tool for forecasting the Euro-area business cycle phases," International Journal of Forecasting, Elsevier, vol. 22(4), pages 735-749.
    9. Wei, Yanfeng & Guo, Xiaoying, 2016. "An empirical analysis of the relationship between oil prices and the Chinese macro-economy," Energy Economics, Elsevier, vol. 56(C), pages 88-100.
    10. Lemmens, Aurélie & Croux, Christophe & Dekimpe, Marnik G., 2008. "Measuring and testing Granger causality over the spectrum: An application to European production expectation surveys," International Journal of Forecasting, Elsevier, vol. 24(3), pages 414-431.
    11. 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.
    12. Boriss Siliverstovs, 2010. "Assessing Predictive Content of the KOF Barometer in Real Time," KOF Working papers 10-249, KOF Swiss Economic Institute, ETH Zurich.
    13. Ahmad Jafari-Samimi & Babak Shirazi & Hamed Fazlollahtabar, 2007. "A Comparison Between Time Series, Exponential Smoothing, and Neural Network Methods To Forecast GDPof Iran," Iranian Economic Review (IER), Faculty of Economics,University of Tehran.Tehran,Iran, vol. 12(2), pages 19-35, spring.
    14. Nada Kulendran & Kevin K.F. Wong, 2009. "Predicting Quarterly Hong Kong Tourism Demand Growth Rates, Directional Changes and Turning Points with Composite Leading Indicators," Tourism Economics, , vol. 15(2), pages 307-322, June.
    15. Lars-Erik Öller & Lasse Koskinen, 2004. "A classifying procedure for signalling turning points," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(3), pages 197-214.
    16. Lemmens, Aurelie & Croux, Christophe & Dekimpe, Marnik G., 2005. "On the predictive content of production surveys: A pan-European study," International Journal of Forecasting, Elsevier, vol. 21(2), pages 363-375.
    17. Luis C. Nunes, 2005. "Nowcasting quarterly GDP growth in a monthly coincident indicator model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(8), pages 575-592.
    18. Antonis A Michis, 2011. "Denoised least squars forecasting of GDP changes using indexes of consumer and business sentiment," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Proceedings of the IFC Conference on "Initiatives to address data gaps revealed by the financial crisis", Basel, 25-26 August 2010, volume 34, pages 383-392, Bank for International Settlements.
    19. Ard Reijer & Andreas Johansson, 2019. "Nowcasting Swedish GDP with a large and unbalanced data set," Empirical Economics, Springer, vol. 57(4), pages 1351-1373, October.

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