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Trends and Cycles in Real-Time

Author

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  • Rómulo A. Chumacero
  • Francisco A. Gallego

Abstract

This paper compares the results of applying several detrending methods to the Chilean monthly economic activity index (IMACEC) that arise from using real-time data sets. We show that data revisions are extremely important and that they can lead to systematically inconsistent estimates of the trend component. Furthermore, most of the filters commonly used to detrend time series in practice, are highly unstable and unreliable for end-of-sample estimation.

Suggested Citation

  • Rómulo A. Chumacero & Francisco A. Gallego, 2001. "Trends and Cycles in Real-Time," Working Papers Central Bank of Chile 130, Central Bank of Chile.
    • Handle: RePEc:chb:bcchwp:130
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    References listed on IDEAS

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    12. Raimundo Soto, 2002. "Ajuste Estacional e Integración en Variables Macroeconómicas," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 39(116), pages 135-155.
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    Cited by:

    1. Camila Figueroa & Jorge Fornero & Pablo García, 2019. "Hindsight vs. Real time measurement of the output gap: Implications for the Phillips curve in the Chilean Case," Working Papers Central Bank of Chile 854, Central Bank of Chile.
    2. Kishor, N. Kundan, 2011. "Data revisions in India: Implications for monetary policy," Journal of Asian Economics, Elsevier, vol. 22(2), pages 164-173, April.
    3. Pablo Pincheira & Hernán Rubio, 2010. "The Low Predictive Power of Simple Phillips Curves in Chile: A Real-Time Evaluation," Working Papers Central Bank of Chile 559, Central Bank of Chile.
    4. Gabriela Contreras M. & Pablo García S., 2002. "Estimating Gaps and Trends for the Chilean Economy," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 5(2), pages 37-55, August.
    5. Rodrigo Fuentes & Fabián Gredig & Mauricio Larraín, 2007. "Estimating the Output Gap for Chile," Working Papers Central Bank of Chile 455, Central Bank of Chile.
    6. Jorge Fornero & Juan Guerra-Salas & Camilo Pérez N., 2019. "Multiplicadores fiscales en Chile," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 22(1), pages 058-080, April.
    7. Carlos A. Medel V. & Michael Pedersen, 2010. "Incertidumbre en las Series Desestacionalizadas de Actividad y Demanda en Chile," Notas de Investigación Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 13(1), pages 63-72, April.
    8. Kishor, N. Kundan, 2009. "Data Revisions in India and its Implications for Monetary Policy," MPRA Paper 16099, University Library of Munich, Germany.
    9. Roberto Luis Olinto Ramos & Patrice T. Robitaille & Rebeca de la Rocque Palis, 2004. "News or noise? an analysis of Brazilian GDP announcements," International Finance Discussion Papers 776, Board of Governors of the Federal Reserve System (U.S.).
    10. Rodrigo Fuentes S. & Fabián Gredig U. & Mauricio Larraín E., 2008. "The output Gap in chile: Measurement and Evaluation," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 11(2), pages 7-30, August.
    11. Michael Pedersen, 2013. "Extracting GDP signals from the monthly indicator of economic activity: Evidence from Chilean real-time data," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2013(1), pages 1-16.
    12. Camila Figueroa & Pablo García, 2017. "Desafíos de la Política Monetaria Sistemática," Economic Policy Papers Central Bank of Chile 64, Central Bank of Chile.
    13. Pablo Pincheira, 2010. "A Real Time Evaluation of the Central Bank of Chile GDP Growth Forecasts," Money Affairs, CEMLA, vol. 0(1), pages 37-73, January-J.

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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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