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Co‐integration, error correction and improved medium‐term regional VAR forecasting
Author(s) -
Shoesmith Gary L
Publication year - 1992
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.3980110202
Subject(s) - bayesian vector autoregression , vector autoregression , term (time) , error correction model , medium term , state variable , econometrics , error detection and correction , bayesian probability , forecast error , series (stratigraphy) , state vector , state (computer science) , computer science , mathematics , statistics , economics , cointegration , algorithm , paleontology , physics , quantum mechanics , classical mechanics , biology , macroeconomics , thermodynamics
This study investigates possible improvements in medium‐term VAR forecasting of state retail sales and personal income when the two series are co‐integrated and represent an error‐correction system. For each of North Carolina and New York, three regional vector autoregression (VAR) models are specified; an unrestricted two‐equation model consisting of the two state variables, a five‐equation unrestricted model with three national variables added and a Bayesian (BVAR) version of the second model. For each state, the co‐integration and error‐correction relationship of the two state variables is verified and an error‐correction version of each model specified. Twelve successive ex ante five‐year forecasts are then generated for each of the state models. The results show that including an error‐correction mechanism when statistically significant improves medium‐term forecasting accuracy in every case.