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Revisions to the Czech National Accounts: Properties and Predictability

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  • Marek RUSNAK

    () (Czech National Bank and Charles University, Prague)

Abstract

Frequent revisions to GDP and its components cause policymakers to face considerable uncertainty about the current state of the economy. In this paper, we provide stylized facts about the magnitude of revisions to the Czech national accounts. Using data over the 2003–2012 period, we find that the revisions are rather large. Revisions to real GDP growth are on average 1.4 for the annualized quarterly growth rate and 0.7 percentage points for the annual growth rate. Revisions to other variables are even larger: the average size of the revisions ranges from 1 to 12 percentage points for annualized quarterly growth rates and from 0.5 to 4 percentage points for annual growth rates. We investigate whether the revisions could have been predicted using the information available at the time of announcement. We find evidence for in-sample predictability for most of the variables, suggesting that the first releases of these variables are not efficient predictors of the actual values. In a real-time out-of-sample exercise, however, we find that the revisions to real GDP, gross fixed capital formation, and government consumption are not predictable. Only revisions to the GDP deflator can be predicted with substantial gains relative to zero-revisions forecasts.

Suggested Citation

  • Marek RUSNAK, 2013. "Revisions to the Czech National Accounts: Properties and Predictability," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 63(3), pages 244-261, July.
  • Handle: RePEc:fau:fauart:v:63:y:2013:i:3:p:244-261
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    References listed on IDEAS

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    1. S. Boragan Aruoba, 2008. "Data Revisions Are Not Well Behaved," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(2-3), pages 319-340, March.
    2. Kugler, Peter & Jordan, Thomas J. & Lenz, Carlos & Savioz, Marcel R., 2005. "GDP data revisions and forward-looking monetary policy in Switzerland," The North American Journal of Economics and Finance, Elsevier, vol. 16(3), pages 351-372, December.
    3. Domenico Giannone & Jérôme Henry & Magdalena Lalik & Michele Modugno, 2012. "An Area-Wide Real-Time Database for the Euro Area," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1000-1013, November.
    4. Stark, Tom & Croushore, Dean, 2002. "Forecasting with a real-time data set for macroeconomists," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 507-531, December.
    5. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November.
    6. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
    7. Christoffersen, Peter & Ghysels, Eric & Swanson, Norman R., 2002. "Let's get "real" about using economic data," Journal of Empirical Finance, Elsevier, vol. 9(3), pages 343-360, August.
    8. Adriana Fernandez & Evan F. Koenig & Alex Nikolsko-Rzhevskyy, 2011. "A real-time historical database for the OECD," Globalization and Monetary Policy Institute Working Paper 96, Federal Reserve Bank of Dallas.
    9. Jacobs, Jan P.A.M. & van Norden, Simon, 2011. "Modeling data revisions: Measurement error and dynamics of "true" values," Journal of Econometrics, Elsevier, vol. 161(2), pages 101-109, April.
    10. Molodtsova, Tanya & Nikolsko-Rzhevskyy, Alex & Papell, David H., 2008. "Taylor rules with real-time data: A tale of two countries and one exchange rate," Journal of Monetary Economics, Elsevier, vol. 55(Supplemen), pages 63-79, October.
    11. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
    12. Sargent, Thomas J, 1989. "Two Models of Measurements and the Investment Accelerator," Journal of Political Economy, University of Chicago Press, vol. 97(2), pages 251-287, April.
    13. George Kapetanios & Tony Yates, 2010. "Estimating time variation in measurement error from data revisions: an application to backcasting and forecasting in dynamic models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(5), pages 869-893.
    14. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    15. Faust, Jon & Rogers, John H & Wright, Jonathan H, 2005. "News and Noise in G-7 GDP Announcements," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 403-419, June.
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    Citations

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    Cited by:

    1. Rusnák, Marek, 2016. "Nowcasting Czech GDP in real time," Economic Modelling, Elsevier, vol. 54(C), pages 26-39.
    2. Jan Capek, 2014. "Historical Analysis of Monetary Policy Reaction Functions: Do Real-Time Data Matter?," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 64(6), pages 457-475, December.
    3. Frantisek Brazdik & Jan Bruha & Michal Franta & David Havrlant & Tibor Hledik & Tomas Holub & Zuzana Humplova & Frantisek Kopriva & Jiri Polansky & Marek Rusnak & Jaromir Tonner, 2015. "Forecasting," Occasional Publications - Edited Volumes, Czech National Bank, Research Department, edition 1, volume 13, number rb13/1 edited by Jan Babecky & Kamil Galuscak.
    4. Michal Andrle & Oxana Babecka Kucharcukova & Jaromir Baxa & Jan Bruha & Peter Claeys & Jan Filacek & Jakub Mateju & Miroslav Plasil & Serhat Solmaz & Borek Vasicek, 2015. "Monetary Policy Challenges in a Low-Inflation Environment," Occasional Publications - Edited Volumes, Czech National Bank, Research Department, edition 2, volume 13, number rb13/2 edited by Jan Babecky & Michal Franta.
    5. Martin Slanicay & Jan Čapek & Miroslav Hloušek, 2016. "Some Notes On Problematic Issues In Dsge Models," Economic Annals, Faculty of Economics, University of Belgrade, vol. 61(210), pages 79-100, July - Se.
    6. Oxana Babecka Kucharcukova & Alexis Derviz & Vaclav Hausenblas & Michal Hlavacek & Mark Joy & Narcisa Kadlcakova & Lubos Komarek & Zlatuse Komarkova & Tomas Konecny & Ivana Kubicova & Jitka Lesanovska, 2014. "Macroprudential Research: Selected Issues," Occasional Publications - Edited Volumes, Czech National Bank, Research Department, edition 2, volume 12, number rb12/2 edited by Jan Babecky & Borek Vasicek.
    7. Robert Ambrisko & Vitezslav Augusta & Jan Babecky & Michal Franta & Dana Hajkova & Petr Kral & Jan Libich & Pavla Netusilova & Milan Rikovsky & Jakub Rysanek & Pavel Soukup & Petr Stehlik & Vilem Vale, 2013. "Macroeconomic Effects of Fiscal Policy," Occasional Publications - Edited Volumes, Czech National Bank, Research Department, edition 2, volume 11, number rb11/2 edited by Jan Babecky & Kamil Galuscak.
    8. Capek Jan, 2015. "Estimating DSGE model parameters in a small open economy: Do real-time data matter?," Review of Economic Perspectives, De Gruyter Open, vol. 15(1), pages 89-114, March.
    9. Kamil Galuscak & Adam Gersl & Marcela Gronychova & Petr Hlavac & Petr Jakubik & Lubos Komarek & Zlatuse Komarkova & Tomas Konecny & Jakub Seidler, 2014. "Stress-Testing Analyses of the Czech Financial System," Occasional Publications - Edited Volumes, Czech National Bank, Research Department, edition 1, volume 12, number rb12/1 edited by Jan Babecky & Roman Horvath.

    More about this item

    Keywords

    national accounts; revisions; vintage data;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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