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A Bayesian vector error corrections model of the U.S. economy

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  • Tom Stark
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    Abstract

    This paper presents a small-scale macroeconometric time-series model that can be used to generate short-term forecasts for U.S. output, inflation, and the rate of unemployment. Drawing on both the Bayesian VAR and vector error corrections (VEC) literature, the author specifies the baseline model as a Bayesian VEC. The author documents the model's forecasting ability over various periods, examines its impulse responses, and considers several reasonable alternative specifications. Based on a root-mean-square-error criterion, the baseline model works best, and the author concludes that this model holds promise as a workhorse forecasting tool.

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    File URL: http://www.philadelphiafed.org/research-and-data/publications/working-papers/1998/wp98-12.pdf
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    Bibliographic Info

    Paper provided by Federal Reserve Bank of Philadelphia in its series Working Papers with number 98-12.

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    Date of creation: 1998
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    Handle: RePEc:fip:fedpwp:98-12

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    Related research

    Keywords: Forecasting ; Time-series analysis;

    References

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    Citations

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    Cited by:
    1. Tom Stark & Dean Croushore, 2001. "Forecasting with a real-time data set for macroeconomists," Working Papers 01-10, Federal Reserve Bank of Philadelphia.
    2. Tom Stark, 2000. "Does current-quarter information improve quarterly forecasts for the U.S. economy?," Working Papers 00-2, Federal Reserve Bank of Philadelphia.
    3. Dean Croushore & Tom Stark, 2000. "A real-time data set for macroeconomists: does data vintage matter for forecasting?," Working Papers 00-6, Federal Reserve Bank of Philadelphia.
    4. Dean Croushore & Tom Stark, 1999. "A real-time data set for macroeconomists," Working Papers 99-4, Federal Reserve Bank of Philadelphia.
    5. Dean Croushore & Tom Stark, 1999. "Does data vintage matter for forecasting?," Working Papers 99-15, Federal Reserve Bank of Philadelphia.

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