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Extracting GDP Signals From the Monthly Indicator of Economic Activity: Evidence From Chilean Real-Time Data

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  • Michael Pedersen

Abstract

With real-time data it is analyzed what information Chile’s monthly indicator of economic activity (IMACEC) contains about the final GDP, defined as the growth rate that has been subject to at least two annual revisions. Data are presented and revisions briefly analyzed. It is argued that when three months of IMACEC data are available, it is possible to extract signals about the final GDP, which are as reliable as those contained in the first release of the growth rate. This result is obtained with the evaluation in-sample as well as out-of-sample. It is then investigated how much extra information IMACEC data provide of the final GDP compared to what is already present in historical data. The in-sample analysis indicates statistically significant improvements when more IMACEC data of the quarter are available. Measured by the root mean square nowcast error (RMSNE) the out-of-sample performance also improves as more monthly data are published, although when only the first IMACEC data of the quarter are available, this is not statistically significant.

Suggested Citation

  • Michael Pedersen, 2010. "Extracting GDP Signals From the Monthly Indicator of Economic Activity: Evidence From Chilean Real-Time Data," Working Papers Central Bank of Chile 595, Central Bank of Chile.
    • Handle: RePEc:chb:bcchwp:595
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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. Pedersen, Michael, 2019. "Anomalies in macroeconomic prediction errors–evidence from Chilean private forecasters," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1100-1107.
    3. 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.
    4. 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.
    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. 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.
    8. Víctor Riquelme & Gabriela Riveros, 2018. "Un Indicador Contemporáneo de Actividad (ICA) para Chile," Notas de Investigación Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 21(1), pages 134-149, April.
    9. Bhattacharya, Rudrani & Pandey, Radhika & Veronese, Giovanni, 2011. "Tracking India Growth in Real Time," Working Papers 11/90, National Institute of Public Finance and Policy.
    10. 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:

    • 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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