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Conditional forecasts on SVAR models using the Kalman filter

Author

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  • Camba-Mendez, Gonzalo

Abstract

This note shows how conditional forecasts from identified VAR models can be computed using Kalman filtering techniques. These techniques are nowadays routine for applied macroeconomists, and hence the computation of conditional forecasts using these methods are simple to implement.

Suggested Citation

  • Camba-Mendez, Gonzalo, 2012. "Conditional forecasts on SVAR models using the Kalman filter," Economics Letters, Elsevier, vol. 115(3), pages 376-378.
    • Handle: RePEc:eee:ecolet:v:115:y:2012:i:3:p:376-378
    DOI: 10.1016/j.econlet.2011.12.087
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    References listed on IDEAS

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    1. Daniel F. Waggoner & Tao Zha, 1999. "Conditional Forecasts In Dynamic Multivariate Models," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 639-651, November.
    2. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    3. Jarocinski, Marek, 2010. "Conditional forecasts and uncertainty about forecast revisions in vector autoregressions," Economics Letters, Elsevier, vol. 108(3), pages 257-259, September.
    4. Hamilton, James D., 1986. "A standard error for the estimated state vector of a state-space model," Journal of Econometrics, Elsevier, vol. 33(3), pages 387-397, December.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

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    2. SIMIONESCU, Mihaela, 2014. "Assessing The Forecasts Accuracy Of The Weight Of Fiscal Revenues In Gdp For Romania," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", vol. 18(3), pages 8-24.
    3. Metiu, Norbert, 2021. "Anticipation effects of protectionist U.S. trade policies," Journal of International Economics, Elsevier, vol. 133(C).
    4. Fokin, Nikita, 2021. "The importance of modeling structural breaks in forecasting Russian GDP," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 63, pages 5-29.
    5. Nataliya Barasinska & Philipp Haenle & Anne Koban & Alexander Schmidt, 2023. "No Reason to Worry About German Mortgages? An Analysis of Macroeconomic and Individual Drivers of Credit Risk," Journal of Financial Services Research, Springer;Western Finance Association, vol. 64(3), pages 369-399, December.
    6. Barasinska, Nataliya & Haenle, Philipp & Koban, Anne & Schmidt, Alexander, 2019. "Stress testing the German mortgage market," Discussion Papers 17/2019, Deutsche Bundesbank.
    7. Oliver Hülsewig & Horst Rottmann, 2022. "Euro Area Periphery Countries' Fiscal Policy and Monetary Policy Surprises," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(3), pages 544-568, June.
    8. Hristov, Nikolay & Hülsewig, Oliver & Wollmershäuser, Timo, 2020. "Capital flows in the euro area and TARGET2 balances," Journal of Banking & Finance, Elsevier, vol. 113(C).
    9. Simionescu Mihaela, 2015. "Kalman Filter or VAR Models to Predict Unemployment Rate in Romania?," Naše gospodarstvo/Our economy, Sciendo, vol. 61(3), pages 3-21, June.
    10. Hristov, Nikolay & Roth, Markus, 2022. "Uncertainty shocks and systemic-risk indicators," Journal of International Money and Finance, Elsevier, vol. 122(C).
    11. Roth, Markus, 2020. "Partial pooling with cross-country priors: An application to house price shocks," Discussion Papers 06/2020, Deutsche Bundesbank.
    12. Mokinski, Frieder, 2017. "A severity function approach to scenario selection," Discussion Papers 34/2017, Deutsche Bundesbank.
    13. Bertrand Gruss, 2014. "After the Boom–Commodity Prices and Economic Growth in Latin America and the Caribbean," IMF Working Papers 2014/154, International Monetary Fund.

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    More about this item

    Keywords

    Conditional forecasting; Vector autoregression; Kalman filter;
    All these keywords.

    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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