A vector error-correction forecasting model of the US economy

Richard G. Anderson, Dennis Hoffman, Robert H. Rasche

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


Any research or policy analysis in economics must be consistent with the time-series properties of observed macroeconomic data. Numerous previous studies reinforce the need to specify correctly a model's multivariate stochastic structure. This paper discusses in detail the specification of a vector error correction forecasting model that is anchored by long-run equilibrium relationships suggested by economic theory. The model includes six variables--the CPI, the GDP price index, real money balances (M1), the federal funds rate, the yield on long-term (10-year) government bonds, and real GDP--and four cointegrating vectors. The accuracy of VECM model forecasts for individual, univariate time series during for the 1990s is comparable to forecasts made by government agencies and private forecasters, perhaps because many forecasters share a similar implicit, long-run steady-state growth model of the economy. Judged by multivariate statistics that account for forecast-error covariance, VECM forecasts are found to be somewhat more accurate than a naïve random-walk alternative.

Original languageEnglish (US)
Pages (from-to)569-598
Number of pages30
JournalJournal of Macroeconomics
Issue number4
StatePublished - Dec 2002


  • Cointegration
  • Forecasting
  • Vector error-correction models

ASJC Scopus subject areas

  • Economics and Econometrics


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