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Conditional Projection by Means of Kalman Filtering

Author

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  • Richard H. Clarida
  • Diane Coyle
Abstract
We establish that the recursive, state-space methods of Kalman filtering and smoothing can be used to implement the Doan, Litterman, and Sims (1983) approach to econometric forecast and policy evaluation. Compared with the methods outlined in Doan, Litterman, and Sims, the Kalman algorithms are more easily programmed and modified to incorporate different linear constraints, avoid cumbersome matrix inversions, and provide estimates of the full variance covariance matrix of the constrained projection errors which can be used directly, under standard normality assumptions, to test statistically the likelihood and internal consistency of the forecast under study.

Suggested Citation

  • Richard H. Clarida & Diane Coyle, 1984. "Conditional Projection by Means of Kalman Filtering," NBER Technical Working Papers 0036, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberte:0036
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    References listed on IDEAS

    as
    1. Engle, Robert F., 1984. "Wald, likelihood ratio, and Lagrange multiplier tests in econometrics," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 13, pages 775-826, Elsevier.
    2. Christopher A. Sims, 1982. "Policy Analysis with Econometric Models," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 13(1), pages 107-164.
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    Cited by:

    1. 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.
    2. Richard H. Clarida & Benjamin M. Friedman, 1986. "The Behavior of U.S. Short-Term Interest Rates Since 1979-10," Cowles Foundation Discussion Papers 695, Cowles Foundation for Research in Economics, Yale University.
    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. Misha van Beek, 2020. "Consistent Calibration of Economic Scenario Generators: The Case for Conditional Simulation," Papers 2004.09042, arXiv.org.
    5. 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.
    6. repec:wrk:wrkemf:32 is not listed on IDEAS
    7. Todd E. Clark & Michael W. McCracken, 2014. "Evaluating Conditional Forecasts from Vector Autoregressions," Working Papers (Old Series) 1413, Federal Reserve Bank of Cleveland.
    8. Knut Are Aastveit & Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2017. "Have Standard VARS Remained Stable Since the Crisis?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(5), pages 931-951, August.
    9. Clarida, Richard H & Friedman, Benjamin M, 1984. "The Behavior of U.S. Short-Term Interest Rates since October 1979," Journal of Finance, American Finance Association, vol. 39(3), pages 671-682, July.
    10. Jane K. Dokko & Brian M. Doyle & Skander J. van den Heuvel & Michael T. Kiley & Jinill Kim & Shane M. Sherlund & Jae W. Sim, 2009. "Monetary policy and the housing bubble," Finance and Economics Discussion Series 2009-49, Board of Governors of the Federal Reserve System (U.S.).
    11. Jan Bruha & Tibor Hledik & Tomas Holub & Jiri Polansky & Jaromir Tonner, 2013. "Incorporating Judgments and Dealing with Data Uncertainty in Forecasting at the Czech National Bank," Research and Policy Notes 2013/02, Czech National Bank.

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