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LATCOIN: Determining Medium to Long-Run Tendencies of Economic Growth in Latvia in Real Time

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  • Konstantins Benkovskis

Abstract

This paper presents a method of estimating the current state of Latvia's economy. The evaluation object is medium to long-run growth of real GDP, but not actual GDP itself, which helps to filter out various one-off effects and focus on medium and long-run tendencies. Our indicator, called LATCOIN (Latvia's Business Cycle Coincidence Indicator), could be viewed as a simple adaptation of EUROCOIN for Latvia with some changes in methodology. LATCOIN is a monthly estimate of the medium to long-run growth of Latvia's real GDP, which is produced on the 9th working day of the next month. Using a large panel of macroeconomic variables, few smooth unobservable factors describing the economy are constructed. Further, these factors are used for the estimation of LATCOIN.

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Bibliographic Info

Paper provided by Latvijas Banka in its series Working Papers with number 2010/01.

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Date of creation: 25 Jan 2010
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Handle: RePEc:ltv:wpaper:201001

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Related research

Keywords: Latvia's real GDP; band-pass filter; coincidence indicator; generalised principal components; real-time performance; turning points;

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References

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  1. Lawrence J. Christiano & Terry J. Fitzgerald, 2003. "The Band Pass Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(2), pages 435-465, 05.
  2. Viktors Ajevskis & Gundars Davidsons, 2008. "Dynamic Factor Models in Forecasting Latvia's Gross Domestic Product," Working Papers 2008/02, Latvijas Banka.
  3. Forni M. & Hallin M., 2003. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Computing in Economics and Finance 2003 143, Society for Computational Economics.
  4. 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.
  5. Marianne Baxter & Robert G. King, 1999. "Measuring Business Cycles: Approximate Band-Pass Filters For Economic Time Series," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 575-593, November.
  6. Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Boston College Working Papers in Economics 440, Boston College Department of Economics.
  7. Hallin, Marc & Liska, Roman, 2007. "Determining the Number of Factors in the General Dynamic Factor Model," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 603-617, June.
  8. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  9. Konstantins Benkovskis, 2008. "Short-Term Forecasts of Latvia's Real Gross Domestic Product Growth Using Monthly Indicators," Working Papers 2008/05, Latvijas Banka.
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Cited by:
  1. Ginters Buss, 2012. "Forecasting and Signal Extraction with Regularised Multivariate Direct Filter Approach," Working Papers 2012/06, Latvijas Banka.

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