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A dynamic multivariate model for use in formulating policy

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  • Tao Zha

Abstract

A policy action by the Federal Reserve consists of using any one of various instruments, such as the federal funds rate and different measures of money, to pursue its multiple objectives. Because of long and variable lags in the effects of policy actions, the process of anticipating the future is indispensable in formulating sound monetary policy. For the same reason, projecting policy effects accurately is a challenging task. An essential step is to develop good forecasting models. ; This article presents a forecasting model that seems to overcome conceptual and empirical difficulties encountered in other models and promises to provide policymakers with a more useful tool for anticipating policy effects. For clarity, the author concentrates on changes in the federal funds rate and on only one of the Fed's objectives - low and stable inflation. The model introduces new techniques that offer two distinctive advantages. One is the ability to forecast the values of key macroeconomic variables such as inflation beyond a period over which these values are known. The other is the model's explicit structure that allows empirically coherent ways to assess the uncertainty of forecasts.

Suggested Citation

  • Tao Zha, 1998. "A dynamic multivariate model for use in formulating policy," Economic Review, Federal Reserve Bank of Atlanta, vol. 83(Q 1), pages 16-29.
    • Handle: RePEc:fip:fedaer:y:1998:i:q1:p:16-29:n:v.83no.1
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    Cited by:

    1. Kilian, Lutz & Zha, Tao, 1999. "Quantifying the Half-Life of Deviations from PPP: The Role of Economic Priors," CEPR Discussion Papers 2334, C.E.P.R. Discussion Papers.
    2. Crompton, Paul & Wu, Yanrui, 2005. "Energy consumption in China: past trends and future directions," Energy Economics, Elsevier, vol. 27(1), pages 195-208, January.
    3. 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.
    4. Higgins, Patrick & Zha, Tao & Zhong, Wenna, 2016. "Forecasting China's economic growth and inflation," China Economic Review, Elsevier, vol. 41(C), pages 46-61.
    5. Marco Del Negro & Frank Schorfheide, 2003. "Take your model bowling: forecasting with general equilibrium models," Economic Review, Federal Reserve Bank of Atlanta, vol. 88(Q4), pages 35-50.
    6. Francis, Brian M. & Moseley, Leo & Iyare, Sunday Osaretin, 2007. "Energy consumption and projected growth in selected Caribbean countries," Energy Economics, Elsevier, vol. 29(6), pages 1224-1232, November.
    7. Del Negro, Marco & Otrok, Christopher, 2007. "99 Luftballons: Monetary policy and the house price boom across U.S. states," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 1962-1985, October.
    8. Lutz Kilian & Tao Zha, 2002. "Quantifying the uncertainty about the half-life of deviations from PPP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(2), pages 107-125.
    9. Carriero, Andrea & Mumtaz, Haroon & Theophilopoulou, Angeliki, 2015. "Macroeconomic information, structural change, and the prediction of fiscal aggregates," International Journal of Forecasting, Elsevier, vol. 31(2), pages 325-348.
    10. Marco Del Negro, 2001. "Turn, turn, turn: Predicting turning points in economic activity," Economic Review, Federal Reserve Bank of Atlanta, vol. 86(Q2), pages 1-12.
    11. Marco Del Negro & Christopher Otrok, 2005. "Monetary policy and the house price boom across U.S. states," FRB Atlanta Working Paper 2005-24, Federal Reserve Bank of Atlanta.
    12. John C. Robertson & Ellis W. Tallman, 1999. "Improving forecasts of the federal funds rate in a policy model," FRB Atlanta Working Paper 99-3, Federal Reserve Bank of Atlanta.
    13. Enrique M. Quilis(1), "undated". "Modelos Bvar: Especificación, Estimación E Inferencia," Working Papers 8-02 Classification-JEL :, Instituto de Estudios Fiscales.

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