Forecasting Performance Comparison by Using Power Transformation between VAR and Bayesian VAR Models

Syang Ke Kung; Chi Hsiu Wang

doi:10.4028/www.scientific.net/AMM.529.621

Paper Titles

Research on the Gerschgorin Radii Estimator Based on Real-Valued Decomposition Technique in Picosatellites
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Study on Optimization of Numerical Simulation for Smooth Blasting Parameters in Xishimen Iron Mine
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Optimization of Malachite Green by KOH-Modified Grapefruit Peel Activated Carbon: Application of Response Surface Methodology
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Comparison of Prognostic Algorithms Based on PF and EKF for Estimating Remaining Useful Life of Li-Ion Batteries
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Forecasting Performance Comparison by Using Power Transformation between VAR and Bayesian VAR Models
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Efficiency Analysis of Series Connection Ground Source Heat Pump Water Chiller-Heater Unit
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Wear Analysis of Warm Extrusion Dies Using DEFORM
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Car Suspension Simulation and Optimization
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Research on the Optimization of the Lower Arm of WEDM Taper Machine
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 529Forecasting Performance Comparison by Using Power...

Forecasting Performance Comparison by Using Power Transformation between VAR and Bayesian VAR Models

Abstract:

This article is devoted to examine the performance of power transformation in VAR and Bayesian VAR (BVAR) forecasts, in comparison with log-transformation. The effect of power transformation in multivariate time series model forecasts is still untouched in the literature. We examined the U.S. macroeconomic data from 1960 to 1987 and the Taiwan’s technology industrial production from 1990 to 2000. Our results showed that the power transformation provides outperforming forecasts in both VAR and BVAR models. Moreover, the non-informative prior BAVR with power transformation is the best predictive model and is recommendable to forecasting practice.

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Periodical:

Applied Mechanics and Materials (Volume 529)

Pages:

621-624

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.529.621

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Online since:

June 2014

Authors:

Syang Ke Kung*, Chi Hsiu Wang

Keywords:

BVAR, Power Transformation, VAR

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