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Nowcasting real GDP in Tunisia using large datasets and mixed-frequency models

Author

Listed:
  • Hagher Ben Rhomdhane

    (Central Bank of Tunisia)

  • Brahim Mehdi Benlallouna

    (Central Bank of Tunisia)

Abstract

This study aims to construct a new monthly leading indicator for Tunisian economic activity and to forecast Tunisian quarterly real GDP (RGDP) using several mixed-frequency models. These include a mixed dynamic factor model, unrestricted mixed-data sampling (UMIDAS), and a threepass regression filter (3PRF) developed at the Central Bank of Tunisia, based on a monthly/quarterly set of economic and financial indicators as predictors. Our methodology is based on direct and indirect approaches, and the direct approach nowcasts aggregate RGDPs. The indirect approach is a disaggregated approach based on the output side of GDP (manufacturing, non-manufacturing, and services) using a set of available monthly indicators by sector. Furthermore, mixed-frequency dynamic factor models and unrestricted MIDAS perform well in terms of root mean squared errors compared to the benchmark model VAR (2). The forecast errors derived from the disaggregated approach during the recent COVID period are smaller than those derived from classical models such as VAR (2). In our model, we used indicators such as electricity consumption by sector, stock market index detailed by sector, and international economic surveys to capture the pandemic effect. The financial variables improve forecasting for all horizons. Additionally, we find that it is better to employ several UMIDAS-ARs by each component of GDP at constant prices and to pool the results rather than relying on aggregated GDP, specifically in volatile times.

Suggested Citation

  • Hagher Ben Rhomdhane & Brahim Mehdi Benlallouna, 2022. "Nowcasting real GDP in Tunisia using large datasets and mixed-frequency models," IHEID Working Papers 02-2022, Economics Section, The Graduate Institute of International Studies.
    • Handle: RePEc:gii:giihei:heidwp02-2022
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    References listed on IDEAS

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    1. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
    2. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    3. Massimiliano Marcellino & Christian Schumacher, 2010. "Factor MIDAS for Nowcasting and Forecasting with Ragged‐Edge Data: A Model Comparison for German GDP," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 518-550, August.
    4. Dr. Christian Hepenstrick & Massimiliano Marcellino, 2016. "Forecasting with Large Unbalanced Datasets: The Mixed-Frequency Three-Pass Regression Filter," Working Papers 2016-04, Swiss National Bank.
    5. Domenico Giannone & Lucrezia Reichlin & David Small, 2008. "Nowcasting: the real time informational content of macroeconomic data releases," ULB Institutional Repository 2013/6409, ULB -- Universite Libre de Bruxelles.
    6. Roberto S. Mariano & Yasutomo Murasawa, 2010. "A Coincident Index, Common Factors, and Monthly Real GDP," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(1), pages 27-46, February.
    7. Domenico Giannone & Lucrezia Reichlin & David H. Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.).
    8. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    9. Marta Bańbura & Michele Modugno, 2014. "Maximum Likelihood Estimation Of Factor Models On Datasets With Arbitrary Pattern Of Missing Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 133-160, January.
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    More about this item

    Keywords

    Mixed-Frequency Data Sampling; Nowcasting; short-term forecasting;
    All these keywords.

    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • F17 - International Economics - - Trade - - - Trade Forecasting and Simulation
    • O11 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development

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