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

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Author Info
Konstantins Benkovskis

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Abstract

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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Related research
Keywords: bridge equations; state space model; out-of-sample forecasting; real-time database; interpolation;

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Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. 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. [Downloadable!] (restricted)
  2. Gerhard Rünstler & Franck Sédillot, 2003. "Short-term estimates of euro area real GDP by means of monthly data," Working Paper Series 276, European Central Bank. [Downloadable!]
  3. 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. [Downloadable!]
  4. Marie Diron, 2006. "Short-term forecasts of euro area real GDP growth - an assessment of real-time performance based on vintage data," Working Paper Series 622, European Central Bank. [Downloadable!]
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  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. Viktors Ajevskis & Gundars Davidsons, 2008. "Dynamic Factor Models in Forecasting Latvia's Gross Domestic Product," Working Papers 2008/02, Latvijas Banka. [Downloadable!]
  7. Evans, Martin D, 2005. "Where Are We Now? Real-Time Estimates of the Macroeconomy," MPRA Paper 831, University Library of Munich, Germany. [Downloadable!]
    Other versions:
  8. 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.
    Other versions:
  9. Marta Banbura & Gerhard Rünstler, 2007. "A look into the factor model black box - publication lags and the role of hard and soft data in forecasting GDP," Working Paper Series 751, European Central Bank. [Downloadable!]
  10. 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. [Downloadable!] (restricted)
  11. 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. [Downloadable!]
  12. Gerhard Fenz & Martin Spitzer, 2006. "An Unobserved Components Model to forecast Austrian GDP," Working Papers 119, Oesterreichische Nationalbank (Austrian Central Bank). [Downloadable!]
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Bušs, Ginters, 2009. "Comparing forecasts of Latvia's GDP using simple seasonal ARIMA models and direct versus indirect approach," MPRA Paper 16684, University Library of Munich, Germany. [Downloadable!]
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