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The g3+ Model: An Upgrade of the Czech National Bank's Core Forecasting Framework

Author

Listed:
  • Frantisek Brazdik
  • Tibor Hledik
  • Zuzana Humplova
  • Iva Martonosi
  • Karel Musil
  • Jakub Rysanek
  • Tomas Sestorad
  • Jaromir Tonner
  • Stanislav Tvrz
  • Jan Zacek

Abstract

This paper introduces g3+, the new core forecasting model of the Czech National Bank (CNB), which replaced the previous g3 model in July 2019. We present the features of the new core forecasting model together with our motivation for adopting them. The new structural features and extensions were motivated by our experience with using the g3 model for more than a decade as the core forecasting tool at the CNB. The new g3+ model features a novel structural foreign economy block, oil as a production factor, heterogeneous households, and other adjustments. Also, we present a new simulation approach that allows us to emulate limited information for the simulation of conditional forecasts. Furthermore, the introduction of the g3+ model on average preserves the forecasting performance of the CNBs DSGE modeling framework.

Suggested Citation

  • Frantisek Brazdik & Tibor Hledik & Zuzana Humplova & Iva Martonosi & Karel Musil & Jakub Rysanek & Tomas Sestorad & Jaromir Tonner & Stanislav Tvrz & Jan Zacek, 2020. "The g3+ Model: An Upgrade of the Czech National Bank's Core Forecasting Framework," Working Papers 2020/7, Czech National Bank, Research and Statistics Department.
    • Handle: RePEc:cnb:wpaper:2020/7
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    File URL: https://www.cnb.cz/export/sites/cnb/en/economic-research/.galleries/research_publications/cnb_wp/cnbwp_2020_07.pdf
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    References listed on IDEAS

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    1. Xiaohong Chen & Sydney C. Ludvigson, 2009. "Land of addicts? an empirical investigation of habit‐based asset pricing models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(7), pages 1057-1093, November.
    2. Hantzsche, Arno & Lopresto, Marta & Young, Garry, 2018. "Using NiGEM in uncertain times: Introduction and overview of NiGEM," National Institute Economic Review, National Institute of Economic and Social Research, vol. 244, pages 1-14, May.
    3. Michal Andrle & Tibor Hledik & Ondra Kamenik & Jan Vlcek, 2009. "Implementing the New Structural Model of the Czech National Bank," Working Papers 2009/2, Czech National Bank.
    4. Heaton, John, 1995. "An Empirical Investigation of Asset Pricing with Temporally Dependent Preference Specifications," Econometrica, Econometric Society, vol. 63(3), pages 681-717, May.
    5. Constantinides, George M, 1990. "Habit Formation: A Resolution of the Equity Premium Puzzle," Journal of Political Economy, University of Chicago Press, vol. 98(3), pages 519-543, June.
    6. John Y. Campbell & N. Gregory Mankiw, 1989. "Consumption, Income, and Interest Rates: Reinterpreting the Time Series Evidence," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 185-246, National Bureau of Economic Research, Inc.
    7. Gert Peersman & Ine van Robays, 2009. "Oil and the Euro area economy [Labour market implications of EU product market integration]," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 24(60), pages 603-651.
    8. repec:fth:harver:1435 is not listed on IDEAS
    9. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
    10. Jeffrey C. Fuhrer, 2000. "Habit Formation in Consumption and Its Implications for Monetary-Policy Models," American Economic Review, American Economic Association, vol. 90(3), pages 367-390, June.
    11. Sungbae An & Heedon Kang, 2011. "Oil Shocks in a DSGE Model for the Korean Economy," NBER Chapters, in: Commodity Prices and Markets, pages 295-321, National Bureau of Economic Research, Inc.
    12. Cavallari, Lilia, 2010. "Exports and foreign direct investments in an endogenous-entry model with real and nominal uncertainty," Journal of Macroeconomics, Elsevier, vol. 32(1), pages 300-313, March.
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    Cited by:

    1. Karel Musil & Stanislav Tvrz & Jan Vlcek, 2021. "News versus Surprise in Structural Forecasting Models: Central Bankers' Practical Perspective," Research and Policy Notes 2021/02, Czech National Bank, Research and Statistics Department.
    2. Jaromir Tonner, 2022. "Current trends in macroeconomic modelling in central banks in light of the turbulent nature of recent events," Occasional Publications - Chapters in Edited Volumes, in: CNB Global Economic Outlook - July 2022, pages 14-20, Czech National Bank, Research and Statistics Department.
    3. Fasolo, Angelo M. & Araujo, Eurilton & Jorge, Marcos Valli & Kornelius, Alexandre & Marinho, Leonardo Sousa Gomes, 2024. "Brazilian macroeconomic dynamics redux: Shocks, frictions, and unemployment in SAMBA model," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 5(2).
    4. Miroslav Plasil, 2021. "Designing Macro-Financial Scenarios: The New CNB Framework and Satellite Models for Property Prices and Credit," Research and Policy Notes 2021/01, Czech National Bank, Research and Statistics Department.
    5. Jiri Gregor, 2024. "A Stress Test Approach to the Calibration of Borrower-Based Measures: A Case Study of the Czech Republic," Working Papers 2024/2, Czech National Bank, Research and Statistics Department.

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    More about this item

    Keywords

    Conditional forecast; DSGE; g3 model; oil; small open economy; two country model;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • F41 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Open Economy Macroeconomics

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