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

Author

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  • Konstantins Benkovskis

    (Bank of Latvia)

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.

Suggested Citation

  • Konstantins Benkovskis, 2008. "Short-Term Forecasts of Latvia's Real Gross Domestic Product Growth Using Monthly Indicators," Working Papers 2008/05, Latvijas Banka.
    • Handle: RePEc:ltv:wpaper:200805
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    References listed on IDEAS

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    Cited by:

    1. Shahzad Ahmad & Farooq Pasha, 2015. "A Pragmatic Model for Monetary Policy Analysis I: The Case of Pakistan," SBP Research Bulletin, State Bank of Pakistan, Research Department, vol. 11, pages 1-42.
    2. Ewert Kleynhans & Clive Coetzee, 2021. "Regional Business Confidence as Early Indicator of Regional Economic Growth," Managing Global Transitions, University of Primorska, Faculty of Management Koper, vol. 19(1 (Spring), pages 27-48.
    3. Muhammad Nadim Hanif & Muhammad Jahanzeb Malik, 2015. "Evaluating the Performance of Inflation Forecasting Models of Pakistan," SBP Research Bulletin, State Bank of Pakistan, Research Department, vol. 11, pages 43-78.
    4. Andrejs Bessonovs, 2015. "Suite of Latvia's GDP forecasting models," Working Papers 2015/01, Latvijas Banka.
    5. Konstantīns Beņkovskis, 2010. "LATCOIN: determining medium to long-run tendencies of economic growth in Latvia in real time," Baltic Journal of Economics, Baltic International Centre for Economic Policy Studies, vol. 10(2), pages 27-48, December.
    6. 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.
    7. Martin Feldkircher & Florian Huber & Josef Schreiner & Julia Woerz & Marcel Tirpak & Peter Toth, 2015. "Small-scale nowcasting models of GDP for selected CESEE countries," Working and Discussion Papers WP 4/2015, Research Department, National Bank of Slovakia.
    8. Martin Feldkircher & Florian Huber & Josef Schreiner & Marcel Tirpák & Peter Tóth & Julia Wörz, 2015. "Bridging the information gap: small-scale nowcasting models of GDP growth for selected CESEE countries," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue 2, pages 56-75.

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    More about this item

    Keywords

    bridge equations; state space model; out-of-sample forecasting; real-time database; interpolation;
    All these keywords.

    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
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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