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Conditional Forecasting With a Bayesian Vector Autoregression: Working Paper 2023-08

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  • Byoung Hark Yoo

Abstract

This paper describes how the Congressional Budget Office uses a Bayesian vector autoregression (BVAR) method to generate alternative economic projections to the agency’s baseline. The BVAR includes a wide range of key economic variables that are needed to approximate budget outcomes. Its estimation methods avoid overfitting, a situation in which a model fits historical data well while having a poor ability to project future values.Given targets of future values of some variables such as inflation, the BVAR generates economic projections consistent with

Suggested Citation

  • Byoung Hark Yoo, 2023. "Conditional Forecasting With a Bayesian Vector Autoregression: Working Paper 2023-08," Working Papers 59629, Congressional Budget Office.
    • Handle: RePEc:cbo:wpaper:59629
    as

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    File URL: https://www.cbo.gov/system/files/2023-11/59629-BVAR.pdf
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    References listed on IDEAS

    as
    1. Antolín-Díaz, Juan & Petrella, Ivan & Rubio-Ramírez, Juan F., 2021. "Structural scenario analysis with SVARs," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 798-815.
    2. Michele Lenza & Giorgio E. Primiceri, 2022. "How to estimate a vector autoregression after March 2020," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 688-699, June.
    3. Bańbura, Marta & Giannone, Domenico & Lenza, Michele, 2015. "Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections," International Journal of Forecasting, Elsevier, vol. 31(3), pages 739-756.
    4. Koop, Gary & Korobilis, Dimitris, 2013. "Large time-varying parameter VARs," Journal of Econometrics, Elsevier, vol. 177(2), pages 185-198.
    5. Christopher A. Sims & Tao Zha, 2006. "Were There Regime Switches in U.S. Monetary Policy?," American Economic Review, American Economic Association, vol. 96(1), pages 54-81, March.
    6. Angelini, Elena & Lalik, Magdalena & Lenza, Michele & Paredes, Joan, 2019. "Mind the gap: A multi-country BVAR benchmark for the Eurosystem projections," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1658-1668.
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    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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