IDEAS home Printed from https://ideas.repec.org/a/adr/anecst/y1987i6-7p125-160.html
   My bibliography  Save this article

The Limits of Counter-Cyclical Monetary Policy: an Analysis Based on Optimal Control Theory and Vector Autoregressions

Author

Listed:
  • Robert Litterman

Abstract

Optimal control theory can be combined with the probability structure of a vector autoregression to investigate the tradeoffs available to policy-makers. Such an approach obtains results based on a minimal set of assumptions about the economy and the structure of policy actions. This paper takes this approach to analyze the potential effectiveness of countercyclical monetary policy.

Suggested Citation

  • Robert Litterman, 1987. "The Limits of Counter-Cyclical Monetary Policy: an Analysis Based on Optimal Control Theory and Vector Autoregressions," Annals of Economics and Statistics, GENES, issue 6-7, pages 125-160.
  • Handle: RePEc:adr:anecst:y:1987:i:6-7:p:125-160
    as

    Download full text from publisher

    File URL: http://www.jstor.org/stable/20075651
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Robert E. Lucas & Thomas J. Sargent, 1979. "After Keynesian macroeconomics," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 3(Spr).
    2. Paul A. Anderson, 1979. "Help for the regional economic forecaster: vector autoregression," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 3(Sum).
    3. Lucas, Robert Jr, 1976. "Econometric policy evaluation: A critique," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 1(1), pages 19-46, January.
    4. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    5. Robert B. Litterman, 1984. "Above-average national growth in 1985 and 1986," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 8(Fall).
    6. Thomas J. Sargent, 1979. "Estimating vector autoregressions using methods not based on explicit economic theories," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 3(Sum).
    7. Robert B. Litterman, 1984. "Forecasting and policy analysis with Bayesian vector autoregression models," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 8(Fall).
    8. Litterman, Robert, 1986. "Forecasting with Bayesian vector autoregressions -- Five years of experience : Robert B. Litterman, Journal of Business and Economic Statistics 4 (1986) 25-38," International Journal of Forecasting, Elsevier, vol. 2(4), pages 497-498.
    9. Cooley, Thomas F. & Leroy, Stephen F., 1985. "Atheoretical macroeconometrics: A critique," Journal of Monetary Economics, Elsevier, vol. 16(3), pages 283-308, November.
    10. Sargent, Thomas J, 1984. "Autoregressions, Expectations, and Advice," American Economic Review, American Economic Association, vol. 74(2), pages 408-415, May.
    11. Robert B. Litterman, 1984. "The costs of intermediate targeting," Working Papers 254, Federal Reserve Bank of Minneapolis.
    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. Enrique M. Quilis(1), "undated". "Modelos Bvar: Especificación, Estimación E Inferencia," Working Papers 8-02 Classification-JEL :, Instituto de Estudios Fiscales.
    2. Robert Marti, 1995. "Spécification des préférences implicites en matière de politique économique française, 1981-1991," Économie et Prévision, Programme National Persée, vol. 119(3), pages 1-11.

    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. Ford, Stephen A., 1986. "A Beginner'S Guide To Vector Autoregression," Staff Papers 13527, University of Minnesota, Department of Applied Economics.
    2. Leeper, Eric M. & Zha, Tao, 2003. "Modest policy interventions," Journal of Monetary Economics, Elsevier, vol. 50(8), pages 1673-1700, November.
    3. Enrique M. Quilis(1), "undated". "Modelos Bvar: Especificación, Estimación E Inferencia," Working Papers 8-02 Classification-JEL :, Instituto de Estudios Fiscales.
    4. Gary L. Shoesmith, 1990. "The Forecasting Accuracy of Regional Bayesian VAR Models with Alternative National Variable Choices," International Regional Science Review, , vol. 13(3), pages 257-269, December.
    5. Terrence Kinal & Jonathan Ratner, 1986. "A VAR Forecasting Model of a Regional Economy: Its Construction and Comparative Accuracy," International Regional Science Review, , vol. 10(2), pages 113-126, August.
    6. Pami Dua & Anirvan Banerji & Stephen M. Miller, 2006. "Performance evaluation of the New Connecticut Leading Employment Index using lead profiles and BVAR models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(6), pages 415-437.
    7. Pami Dua & Stephen M. Miller & David J. Smyth, 1996. "Using Leading Indicators to Forecast US Home Sales in a Bayesian VAR Framework," Working papers 1996-08, University of Connecticut, Department of Economics.
    8. Garratt, Anthony & Lee, Kevin C & Pesaran, M. Hashem & Shin, Yongcheol, 1998. "A Structural Cointegrating VAR Approach to Macroeconometric Modelling," Cambridge Working Papers in Economics 9823, Faculty of Economics, University of Cambridge.
    9. Yochanan Shachmurove, 2001. "Dynamic Co-movements of Stock Indices: The Emerging Middle Eastern and the United States Markets," Penn CARESS Working Papers ddffc4204cf90a8523fb64134, Penn Economics Department.
    10. Starck, Christian, 1991. "Specifying a Bayesian vector autoregression for short-run macroeconomic forecasting with an application to Finland," Bank of Finland Research Discussion Papers 4/1991, Bank of Finland.
    11. Pami Dua, 2023. "Macroeconomic Modelling and Bayesian Methods," Springer Books, in: Pami Dua (ed.), Macroeconometric Methods, chapter 0, pages 19-37, Springer.
    12. Marco Del Negro & Frank Schorfheide, 2004. "Priors from General Equilibrium Models for VARS," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(2), pages 643-673, May.
    13. Stefan Laséen & Andrea Pescatori, 2020. "Financial stability and interest‐rate policy: A quantitative assessment of costs and benefit," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 53(3), pages 1246-1273, August.
    14. Gossé, Jean-Baptiste & Guillaumin, Cyriac, 2013. "L’apport de la représentation VAR de Christopher A. Sims à la science économique," L'Actualité Economique, Société Canadienne de Science Economique, vol. 89(4), pages 309-319, Décembre.
    15. Rangan Gupta & Stephen Miller, 2012. "“Ripple effects” and forecasting home prices in Los Angeles, Las Vegas, and Phoenix," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 48(3), pages 763-782, June.
    16. Hafer, R. W. & Sheehan, Richard G., 1989. "The sensitivity of VAR forecasts to alternative lag structures," International Journal of Forecasting, Elsevier, vol. 5(3), pages 399-408.
    17. Starck, Christian, 1991. "Specifying a Bayesian vector autoregression for short-run macroeconomic forecasting with an application to Finland," Research Discussion Papers 4/1991, Bank of Finland.
    18. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    19. Kenny, Geoff & Meyler, Aidan & Quinn, Terry, 1998. "Bayesian VAR Models for Forecasting Irish Inflation," Research Technical Papers 4/RT/98, Central Bank of Ireland.
    20. Francisco F. R. Ramos, 1996. "Forecasting market shares using VAR and BVAR models: A comparison of their forecasting performance," Econometrics 9601003, University Library of Munich, Germany.

    More about this item

    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:adr:anecst:y:1987:i:6-7:p:125-160. 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: Secretariat General or Laurent Linnemer (email available below). General contact details of provider: https://edirc.repec.org/data/ensaefr.html .

    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.