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Short-Term Forecasts of Latvia's Real Gross Domestic Product Growth Using Monthly Indicators

  • Konstantins Benkovskis

    (Bank of Latvia)

The conjunctural information from monthly indicators, e.g. industrial production, retail trade turnover, M3, confidence indicators, etc. could partly replace GDP data before the first official release is published. It is possible to incorporate monthly indicators into short-term forecasting models of GDP using quarterly bridge equations or state space models. In many cases monthly indicators are released with a lag, and GDP forecasts based on actual figures are available only shortly before the official release. To eliminate this drawback, missing observations of monthly indicators could be forecasted using simple univariate time-series models. To perform real-time analysis of the forecasting performance of bridge equations and state space models, a real-time database containing real GDP series with 28 vintages of quarterly real GDP was created. According to calculations, only bridge equations and state space models containing M3 monthly data perform better than the benchmark ARIMA model. Both model types using M3 provide valuable information forecast for the first and final releases of GDP. This does not mean, however, that other conjunctural indicators should not be used in forecasting, as the analysis does not take into account possible future changes in links between monthly indicators and quarterly GDP growth.

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Paper provided by Latvijas Banka in its series Working Papers with number 2008/05.

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Date of creation: 15 Sep 2008
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Handle: RePEc:ltv:wpaper:200805
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  1. Robert Ingenito & Bharat Trehan, 1996. "Using monthly data to predict quarterly output," Economic Review, Federal Reserve Bank of San Francisco, pages 3-11.
  2. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
  3. Martin D. D. Evans(Georgetown University and NBER), 2005. "Where Are We Now? Real-time Estimates of the Macro Economy," Working Papers gueconwpa~05-05-02, Georgetown University, Department of Economics.
  4. Nicolas A. Cuche & Martin K. Hess, 1999. "Estimating Monthly GDP In A General Kalman Filter Framework: Evidence From Switzerland," Working Papers 99.02, Swiss National Bank, Study Center Gerzensee.
  5. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-59, April.
  6. Banbura, Marta & Rünstler, Gerhard, 2011. "A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP," International Journal of Forecasting, Elsevier, vol. 27(2), pages 333-346, April.
  7. Baffigi, Alberto & Golinelli, Roberto & Parigi, Giuseppe, 2004. "Bridge models to forecast the euro area GDP," International Journal of Forecasting, Elsevier, vol. 20(3), pages 447-460.
  8. Rünstler, Gerhard & Sédillot, Franck, 2003. "Short-term estimates of euro area real GDP by means of monthly data," Working Paper Series 0276, European Central Bank.
  9. James Mitchell & Richard J. Smith & Martin R. Weale & Stephen Wright & Eduardo L. Salazar, 2005. "An Indicator of Monthly GDP and an Early Estimate of Quarterly GDP Growth," Economic Journal, Royal Economic Society, vol. 115(501), pages F108-F129, 02.
  10. Viktors Ajevskis & Gundars Davidsons, 2008. "Dynamic Factor Models in Forecasting Latvia's Gross Domestic Product," Working Papers 2008/02, Latvijas Banka.
  11. Gerhard Fenz & Martin Spitzer, 2006. "An Unobserved Components Model to Forecast Austrian GDP," Working Papers 119, Oesterreichische Nationalbank (Austrian Central Bank).
  12. Diron, Marie, 2006. "Short-term forecasts of euro area real GDP growth: an assessment of real-time performance based on vintage data," Working Paper Series 0622, European Central Bank.
  13. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
  14. Aleksejs Melihovs & Svetlana Rusakova, 2005. "Short-Term Forecasting of Economic Development in Latvia Using Business and Consumer Survey Data," Working Papers 2005/04, Latvijas Banka.
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