The objective of the paper is to create a composite leading indicator (CLI) for monitoring and predicting Hungarian business cycles. We compare the existing CLI applied by the OECD and Eurostat with our own CLI. According to our findings, our CLI forecasts the evolution of a referential series more precisely than the CLIs developed by the OECD and Eurostat. Nevertheless, from our point of view, the application of all existing CLIs at the same time can be appropriate. Consequently, the number of false signals should be reduced. The CLIs allow us to receive the first rough preliminary estimations of an economic cycle, in our case, the Hungarian one.
Beneš, J. – N’Diaye, P. (2004): A Multivariate Filter for Measuring Potential Output and the NAIRU: Application to the Czech Republic. IMF Working Paper, 04(45): 1–30.
Bezděk, J. – Dybczak, K. – Krejdl, A. (2003): Cyclically Adjusted Fiscal Balance – OECD and ESCB Methods. Czech Journal of Economics and Finance, 53(11–12): 477–509.
Boschan, C. – Ebanks, W. W. (1978): The Phase Average Trend: A New Way of Measuring Economic Growth, Proceeding of the Business and Economic Statistics Section. Washinghton, D.C.:American Statistical Association.
Burns, A. F. – Mitchell, W. C. (1946): Measuring Business Cycles. Cambridge: NBER.
Conference Board (2001): Business Cycle Indicator Handbook. Economic Research. The Conference Board.
Czesaný, S. (2006): Hospodářsky cyklus (Business Cycle). Praha: Linde.
Czesaný, S. – Jeřábková, Z. (2009): Metóda konstrukce kompozitních indikátorú hospodářského cyklu pro českou ekonomiku (Methodology of the Composite Indicators Construction of the Czech Republic Business Cycle). Statistika, 1:21
Economic Cycle Research Institute (2011): Turning Points & Leading Indicators. Available at http://www.businesscycle.com/business_cycles/turning_points_leading_indicators.
Everts, M. P. (2006): Measuring Business Cycles. Berlin: Verlag im Internet GmbH.
Fabiani, S. – Mestre, R. (2000): Alternative Measures of the NAIRU in the Euro Area: Estimates and Assessment. ECB Working Paper, 17: 1–47.
Gyomai, G. – Guedette, E. (2012): OECD System of Composite Leading Indicator. Paris: Organization for Economic Cooperation and Development.
Győrffy, D. (2009): Structural Change without Trust: Reform Cycles in Hungary and Slovakia. Acta Oeconomica, 59(2): 147–177.
Hodric, R. J. – Prescott, E. C. (1997): Postwar U.S. Business Cycles: An Empirical Investigation. Journal of Money Credit and Banking, 29(1): 1–16.
Kerényi, Á. (2015): Budapest Economic Forum about the Hungarian Monetary Policy. Conference Report. Acta Oeconomica, 65(1): 143–152.
Macháčková, L. – Czesaný, S. – Sedláček, P. (2007): Monitorování a analýza hospodářského cyklu (Business Cycle Monitoring and Analysis). Praha: Český statistický úrad.
Marek, L. (2007): Statistika pro economy (Statistics for Economists). Praha: Professional Publishing.
Mester, I. T. (2007): Indicator Approach to Business Cycle Analysis. Fascicle of Management andTechnological Engineering, 6(16): 2250–2256.
Mihályi, P. (1988): Cycles or Shocks: East European Investments, 1950–1985. Economics of Planning, 22(1–2): 41–56.
Moore, G. H. (1961): Business Cycle Indicators, Contributions to the Analysis of Current Business Conditions. Princeton University Press.
Nardo, M. – Saisana, M. (2005): Handbook on Constructing Composite Indicators: Methodology and User Guide. Paris: OECD Statistics Working Papers, 3.
NBS (2006): Krátkodobá prognóza ekonomického rastu (Short-Term Economic Growth Prediction). Available at http://www.nbs.sk/_img/Documents/PUBLIK/MU/06_01-2.pdf.
Nilsson, R. (2000): Confidence Indicators and Composite Indicators. Paris: OECD.
Nilsson, R. – Gyomai, G. (2007): Cycle Extraction: A Comparison of The PAT Method, the Hodrick-Prescott and Christiano-Fitzgerald Filters. Paris: OECD.
Nilsson, R. – Gyomai, G. (2011): Cycle Extraction: A Comparison of the Phase Average Trend Method, the Hodrick-Prescott and Christiano-Fitzgerald Filters. Paris: OECD Statistics WorkingPapers, 4.
OECD (1993): Cyclical Indicators and Business Tendency Survey. Paris.
OECD (1998): OECD Composite Leading Indicators: A Tool For Short-Term Analysis. Paris.
OECD (2004): The OECD–JRC Handbook on Practices for Developing Composite Indicators. Paris: OECD Committee on Statistics.
OECD (2008): Handbook on Constructing Composite Indicators: Methodology and User Guide. Paris.
OECD (2012a): Glossary for OECD Composite Leading Indicators. Paris.
OECD (2012b): Main Economic Indicator: Composite Leading Indicators For Countries. Paris.
OECD (2013): Composite Leading Indicator for Hungary. Paris.
Ozyildirim, A. – Schaitkin, B. – Zarnowitz, V. (2009): Business Cycles in the Euro Area Defined with Coincident Economic Indicators and Predicted with Leading Economic Indicators. Luxembourg:5th Eurostat Colloqium on Modern Tools for Business Cycle Analysis.
Saltelli, A. (2007): Composite Indicators between Analysis and Advocacy. Social Indicators Research, 81: 65–77.
Schilcht, E. (2005): Estimating the Smoothing Parameter in the So-Called Hodrick-Prescott Filter. Journal of the Japanese Statistical Society, 35(1): 99–119.
Sullivan, A. – Sheffrin, S. M. (2003): Economics: Principles in Action. New Jersey: Pearson Prentice Hall.
Tkáčová, A. (2012): Kompozitný predstihový indikátor hospodárskeho cyklu českej ekonomiky (Composite Leading Indicator of Czech Business Cycle). Politická ekonomie, 60: 590–613.
Trimbur, T. M. (2006): Detrending Economic Time Series: A Bayesian Generalization of the Hodrick-Prescott Filter. Journal of Forecasting, 4: 247–273.
Tuveri, J. P. (1997): National Accounts Central and Eastern Europe. Paris.
Zimková, E. – Barochovský, J. (2007): Odhad potenciálného produktu a produkčnej medzery v slovenských podmienkach (Estimation of Potential Output and Output Gap in Slovak Conditions). Politická ekonomie, 4: 473–489.