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Title :Evaluation of Forecasting Performance Using Bayesian Stochastic Search Variable Selection in a Vector Autoregression
Authors :Sugita, Katsuhiro
Issue Date :21-Sep-2018
Abstract :This paper examines forecasting performance of a vector autoregressive (VAR) model by a Bayesian stochastic search variable selection (SSVS) method. We use several artificially generated data sets to evaluate forecasting performance using a direct multiperiod forecasting method with a recursive forecasting exercise. We find that implementing SSVS prior in a VAR improves forecasting performance over unrestricted VAR models for either non-stationary or stationary data. As an illustration of a VAR model with SSVS prior, we investigate US macroeconomic data sets with three variables using a VAR with lag length of ten, and find that the SSVS restrictions on insignificant parameters alleviates over-parameterized problem of VAR and thus offers an appreciable improvement in forecast 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/20.500.12000/42446
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#01 p.1 -19
Appears in Collections:Ryukyu Economics Working Paper Series

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