BUILDING EXPORT FORECAST MODEL USING A KERNEL-BASED DIMENSION REDUCTION METHOD

被引：1

作者：

Hai Nguyen Minh ^{[1
]}

Thanh Do Van ^{[2
]}

Dung Nguyen Duc ^{[3
]}

机构：

[1] Ind Univ Ho Chi Minh City, Fac Fundamental Sci, Ho Chi Minh City, Vietnam

[2] Nguyen Tat Thanh Univ, Fac Informat Technol, Ho Chi Minh City, Vietnam

[3] Vietnam Acad Sci & Technol, Inst IT, Hanoi, Vietnam

来源：

ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH | 2022年 / 56卷 / 01期

关键词：

Big data; Time series; Dimensionality reduction; Kernel trick; Factor model; PCA; Export demand model; PRINCIPAL COMPONENTS; LARGE NUMBER;

D O I：

10.24818/18423264/56.1.22.06

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

The purpose of this article is to build Vietnam's export turnover forecast model at the monthly frequency on a large data set of potentially time-series predictors. The model is built based on the Dynamic factor model, where factors are extracted from the data set of non-redundant and relevant variables in potential predictors by a variable dimension reduction method using kernel tricks and based on an RMSE-best model. The results show that the percentage of absolute error of out-of-sample forecasts by the built model is less than 2%, and the out-of-sample forecast accuracy of this model is much higher than that of the model built based on the export demand model.

引用

页码：91 / 106

页数：16

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