IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v108y2010i3p257-259.html

Conditional forecasts and uncertainty about forecast revisions in vector autoregressions

Author

Listed:
  • Jarocinski, Marek

Abstract

This note simplifies the Waggoner and Zha (1999) formula for the conditional distribution of shocks, discusses its linear algebraic intuition, and shows how to account for the dependence between the conditional and unconditional predictive densities when comparing them.

Suggested Citation

  • Jarocinski, Marek, 2010. "Conditional forecasts and uncertainty about forecast revisions in vector autoregressions," Economics Letters, Elsevier, vol. 108(3), pages 257-259, September.
    • Handle: RePEc:eee:ecolet:v:108:y:2010:i:3:p:257-259
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165-1765(10)00215-6
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Christiano, Lawrence J. & Eichenbaum, Martin & Evans, Charles L., 1999. "Monetary policy shocks: What have we learned and to what end?," Handbook of Macroeconomics, in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 2, pages 65-148, Elsevier.
    2. Marek Jarocinski & Frank Smets, 2008. "House prices and the stance of monetary policy," Review, Federal Reserve Bank of St. Louis, vol. 90(Jul), pages 339-366.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Karlsson, Sune, 2013. "Forecasting with Bayesian Vector Autoregression," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 791-897, Elsevier.
    2. 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.
    3. Michael W. McCracken & Joseph T. McGillicuddy, 2019. "An empirical investigation of direct and iterated multistep conditional forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(2), pages 181-204, March.
    4. Bobeica, Elena & Hartwig, Benny, 2023. "The COVID-19 shock and challenges for inflation modelling," International Journal of Forecasting, Elsevier, vol. 39(1), pages 519-539.
    5. Todd E. Clark & Michael W. McCracken, 2014. "Evaluating Conditional Forecasts from Vector Autoregressions," Working Papers (Old Series) 1413, Federal Reserve Bank of Cleveland.
    6. Camba-Mendez, Gonzalo, 2012. "Conditional forecasts on SVAR models using the Kalman filter," Economics Letters, Elsevier, vol. 115(3), pages 376-378.
    7. De Santis, Roberto A., 2015. "A measure of redenomination risk," Working Paper Series 1785, European Central Bank.
    8. De Backer, Bruno & Dewachter, Hans & Iania, Leonardo, 2021. "Macrofinancial information on the post-COVID-19 economic recovery: Will it be V, U or L-shaped?," Finance Research Letters, Elsevier, vol. 43(C).
    9. Nicolas Audet & Joe Ning & Adam Epp & Jeffrey Gao, 2025. "The Dynamic Canadian Debt Strategy Model," Technical Reports 127, Bank of Canada.
    10. Chan, Joshua C.C. & Pettenuzzo, Davide & Poon, Aubrey & Zhu, Dan, 2025. "Conditional forecasts in large Bayesian VARs with multiple equality and inequality constraints," Journal of Economic Dynamics and Control, Elsevier, vol. 173(C).
    11. Cafiso, Gianluca & Missale, Alessandro & Rivolta, Giulia, 2025. "The credit channel of the sovereign spread: A Bayesian SVAR analysis," Economic Modelling, Elsevier, vol. 144(C).
    12. Dieppe, Alistair & van Roye, Björn & Legrand, Romain, 2016. "The BEAR toolbox," Working Paper Series 1934, European Central Bank.
    13. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hanck, Christoph & Prüser, Jan, 2016. "House prices and interest rates: Bayesian evidence from Germany," Ruhr Economic Papers 620, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    2. 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.
    3. Misha van Beek, 2020. "Consistent Calibration of Economic Scenario Generators: The Case for Conditional Simulation," Papers 2004.09042, arXiv.org.
    4. Punzi, Maria Teresa, 2016. "Financial cycles and co-movements between the real economy, finance and asset price dynamics in large-scale crises," FinMaP-Working Papers 61, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    5. Anastasios Evgenidis & Stephanos Papadamou, 2021. "The impact of unconventional monetary policy in the euro area. Structural and scenario analysis from a Bayesian VAR," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5684-5703, October.
    6. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2018. "Bayesian Vector Autoregressions," The Warwick Economics Research Paper Series (TWERPS) 1159, University of Warwick, Department of Economics.
    7. Richard K. Crump & Stefano Eusepi & Domenico Giannone & Eric Qian & Argia M. Sbordone, 2021. "A Large Bayesian VAR of the United States Economy," Staff Reports 976, Federal Reserve Bank of New York.
    8. Summers, Peter M., 2001. "Forecasting Australia's economic performance during the Asian crisis," International Journal of Forecasting, Elsevier, vol. 17(3), pages 499-515.
    9. Esteban Prieto & Sandra Eickmeier & Massimiliano Marcellino, 2016. "Time Variation in Macro‐Financial Linkages," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1215-1233, November.
    10. Spånberg, Erik & Shahnazarian, Hovick, 2019. "The importance of the financial system for the current account in Sweden: A sectoral approach," International Economics, Elsevier, vol. 158(C), pages 91-103.
    11. Higgins, Patrick & Zha, Tao & Zhong, Wenna, 2016. "Forecasting China's economic growth and inflation," China Economic Review, Elsevier, vol. 41(C), pages 46-61.
    12. Olga Korotkikh, 2020. "A Multi-Country BVAR Model for the External Sector," Russian Journal of Money and Finance, Bank of Russia, vol. 79(4), pages 98-112, December.
    13. Sebastian Ankargren & Mårten Bjellerup & Hovick Shahnazarian, 2017. "The importance of the financial system for the real economy," Empirical Economics, Springer, vol. 53(4), pages 1553-1586, December.
    14. Elena Bobeica & Marek Jarociński, 2019. "Missing Disinflation and Missing Inflation: A VAR Perspective," International Journal of Central Banking, International Journal of Central Banking, vol. 15(1), pages 199-232, March.
    15. Bruno Albuquerque & Martin Iseringhausen & Frederic Opitz, 2024. "The Housing Supply Channel of Monetary Policy," IMF Working Papers 2024/023, International Monetary Fund.
    16. Philippe Goulet Coulombe & Maximilian Gobel, 2020. "Arctic Amplification of Anthropogenic Forcing: A Vector Autoregressive Analysis," Papers 2005.02535, arXiv.org, revised Mar 2021.
    17. Filipa Sa & Tomasz Wieladek, 2010. "Monetary policy, capital inflows and the housing boom," Bank of England working papers 405, Bank of England.
    18. Chan, Joshua C.C. & Pettenuzzo, Davide & Poon, Aubrey & Zhu, Dan, 2025. "Conditional forecasts in large Bayesian VARs with multiple equality and inequality constraints," Journal of Economic Dynamics and Control, Elsevier, vol. 173(C).
    19. Mário Jorge Mendonça & Luis Alberto Medrano & Adolfo Sachsida, 2009. "Avaliando a Condição da Política Fiscal no Brasil," Discussion Papers 1409, Instituto de Pesquisa Econômica Aplicada - IPEA.
    20. Victor Pontines, 2021. "The real effects of loan-to-value limits: empirical evidence from Korea," Empirical Economics, Springer, vol. 61(3), pages 1311-1350, September.

    More about this item

    Keywords

    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecolet:v:108:y:2010:i:3:p:257-259. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.