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Title :Forecasting with Vector Autoregressions by Bayesian Model Averaging
Authors :Sugita, Katsuhiro
Issue Date :25-Jun-2019
Abstract :This paper examines how vector autoregression model by Bayesian model averaging method can improve forecasting performance. Bayesian model averaging selects significant variables in vector autoregression model that contains many insignificant variables, and thus alleviates over-parameterization problem. For empirical application, macroeconomic data for three countries - US, UK and Japan - are examined. I find that the Bayesian model averaging method can improve forecasting performance.
Type Local :プレプリント
Publisher :琉球大学国際地域創造学部経済学プログラム
URI :http://hdl.handle.net/20.500.12000/44627
Citation :琉球大学経済学ワーキングペーパーシリーズ = Ryukyu Economics Working Paper Series no.REWP#03 p.1 -13
Appears in Collections:Ryukyu Economics Working Paper Series

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