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Extracting GDP signals from the monthly indicator of economic activity: Evidence from Chilean real-time data


  • Michael Pedersen


Real-time data are analysed for information on the Chilean monthly economic activity indicator IMACEC and what it indicates of the final GDP, defined as the growth rate that has been subject to at least two annual revisions. Data are presented and revisions analysed briefly. Mincer-Zarnowitz tests suggest that forecast rationality is rejected with respect to the three-month IMACEC growth rate as a nowcast of the first released quarterly GDP, as well as the first published GDP as a nowcast of the final GDP. An out-of-sample nowcasting analysis was conducted using only data which were available in real-time. The results show that small models nowcast better than less parsimonious ones. The evidence from the empirical study suggests no improvement in the nowcasting performance when historical data are supplemented with the first monthly IMACEC of the quarter. On the other hand, when two monthly observations IMACEC are available, the root mean squared nowcast error (RMSNE) decreases by 24%, and a further decline of 33% is obtained when the third monthly observation of the quarter is published. Both of these advances are statistically significant. No further improvement is obtained with the publication of the first release of the quarterly GDP. JEL classifications: C89, E17 Keywords: Real-time data, data revisions, nowcasting

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  • 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.
  • Handle: RePEc:oec:stdkab:5k48345b3lkc

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    References listed on IDEAS

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    Cited by:

    1. Pedersen, Michael, 2015. "What affects the predictions of private forecasters? The role of central bank forecasts in Chile," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1043-1055.
    2. Deicy J. Cristiano & Manuel D. Hernández & José David Pulido, 2012. "Pronósticos de corto plazo en tiempo real para la actividad económica colombiana," Borradores de Economia 724, Banco de la Republica de Colombia.
    3. Yutaka Kurihara, 2016. "Can the Disparity between GDP and GDP Forecast Cause Economic Instability? The Recent Japanese Case," International Journal of Economics and Financial Research, Academic Research Publishing Group, vol. 2(8), pages 155-160, 08-2016.
    4. Porshakov, A. & Ponomarenko, A. & Sinyakov, A., 2016. "Nowcasting and Short-Term Forecasting of Russian GDP with a Dynamic Factor Model," Journal of the New Economic Association, New Economic Association, vol. 30(2), pages 60-76.
    5. 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.
    6. Michael Pedersen, 2013. "What Affects the Predictions of Private Forecasters? The Role of Central Bank Forecasts," Working Papers Central Bank of Chile 686, Central Bank of Chile.
    7. Bhattacharya, Rudrani & Pandey, Radhika & Veronese, Giovanni, 2011. "Tracking India Growth in Real Time," Working Papers 11/90, National Institute of Public Finance and Policy.
    8. Pablo Pincheira, 2010. "A Real Time Evaluation of the Central Bank of Chile GDP Growth Forecasts," Money Affairs, Centro de Estudios Monetarios Latinoamericanos, vol. 0(1), pages 37-73, January-J.

    More about this item

    JEL classification:

    • C89 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications


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