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Vector autoregressions: forecasting and reality

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  • John C. Robertson
  • Ellis W. Tallman

Abstract

Constructing forecasts of the future path for economic series such as real gross domestic product growth, inflation, and unemployment forms a large part of applied economic analysis for business and government. Model-based forecasts are easier to replicate and validate by independent researchers than forecasts based on expert opinion alone. In addition, the forecaster can formally investigate the source of systematic errors in model forecasts, and a forecast model s performance can be established before it is used by a decision maker. ; The authors of this article describe a particular model-based forecasting approach, a vector autoregression comprising six U.S. macroeconomic variables. They focus attention on the technical hurdles that must be addressed in a real-time application and methods for overcoming those hurdles, such as conditional forecasting to handle the staggered release of data and matching quarterly with monthly data. ; By emphasizing the practical problems of forecasting economic data using a statistical model, the authors draw on experience in using such a model at the Federal Reserve Bank of Atlanta. Although the model studied is small and highly aggregated, it provides a convenient framework for illustrating several practical forecasting issues. The focus on a simple model provides potential users with a road map of how one might implement such a forecasting model in specific applications.

Suggested Citation

  • John C. Robertson & Ellis W. Tallman, 1999. "Vector autoregressions: forecasting and reality," Economic Review, Federal Reserve Bank of Atlanta, issue Q1, pages 4-18.
  • Handle: RePEc:fip:fedaer:y:1999:i:q1:p:4-18:n:v.84no.1
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    References listed on IDEAS

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    1. Richard G. Anderson & Kenneth A. Kavajecz, 1994. "A historical perspective on the Federal Reserve's monetary aggregates: definition, construction and targeting," Proceedings, Federal Reserve Bank of St. Louis, issue Mar, pages 1-31.
    2. Robert Ingenito & Bharat Trehan, 1996. "Using monthly data to predict quarterly output," Economic Review, Federal Reserve Bank of San Francisco, pages 3-11.
    3. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1983. "Forecasting and Conditional Projection Using Realistic Prior Distributions," NBER Working Papers 1202, National Bureau of Economic Research, Inc.
    4. Preston J. Miller & Daniel M. Chin, 1996. "Using monthly data to improve quarterly model forecasts," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Spr, pages 16-33.
    5. Christopher A. Sims, 1993. "A Nine-Variable Probabilistic Macroeconomic Forecasting Model," NBER Chapters,in: Business Cycles, Indicators and Forecasting, pages 179-212 National Bureau of Economic Research, Inc.
    6. John C. Robertson & Ellis W. Tallman, 1998. "Data vintages and measuring forecast model performance," Economic Review, Federal Reserve Bank of Atlanta, issue Q 4, pages 4-20.
    7. Litterman, Robert B, 1983. "A Random Walk, Markov Model for the Distribution of Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 169-173, April.
    8. Litterman, Robert B, 1983. "A Random Walk, Markov Model for the Distribution of Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 169-173, April.
    9. Richard G. Anderson & Kenneth A. Kavajecz, 1994. "A historical perspective on the Federal Reserve's monetary aggregates: definition, construction and targeting," Review, Federal Reserve Bank of St. Louis, issue Mar, pages 1-31.
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    Keywords

    Forecasting ; Vector autoregression;

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