Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/145077
|
| Title: | 含外生多變量TAR模型分析及其應用在黃金價格的預測
Multivariate TAR Model with Exogenous Variables Analysis and its Applications to the Gold Price Forecasting |
| Authors: | 侯博耀
Hou, Bo-Yao |
| Contributors: | 曾正男
侯博耀
Hou, Bo-Yao |
| Keywords: | 外生變數
ARIMA
TAR
門檻值
黃金價格
Exogenous variables
ARIMA
TAR
Threshold
Gold price |
| Date: | 2023 |
| Issue Date: | 2023-06-02 11:44:26 (UTC+8) |
| Abstract: | 本研究利用含外生多變量門檻自迴歸(TAR)模型，分析並預測110年至112年的黃金價格。相較傳統的ARIMA模型，含外生多變量TAR模型更能有效反映時間數列結構改變的過程與趨勢，對於預測上具有更大的優勢。此外，TAR模型的適用範圍很廣，因為時間數列通常為非線性，而且容易受到多個變數影響，因此加入多個外生變數，可以更準確的分析資料並進行預測。我們以黃金價格為例，提出之多變量TAR模型，較傳統預測模型有更高的預測精準度。研究目標:含外生多變量TAR模型分析及其預測。研究方法:找出含外生多變量門檻函數，計算含外生多變量TAR門檻值並進行模式架構分析及其預測。研究發現:含外生多變量TAR模型預測能力較傳統預測方法更佳。研究創新:提出外生變數門檻模式演算法。研究價值:財務實證分析上預測策略。
In this research, we use a multivariate threshold autoregressive (TAR) model with exogenous variables to analyze and predict the gold price from 110 to 112 years. Compared with the traditional ARIMA model, the multivariate TAR model with exogenous variables can more effectively reflect the process and trend of time series structure changes, and has greater advantages in prediction. In addition, the TAR model has a wide range of applications, because the time series generally has nonlinear phenomena and is easily affected by multiple variables. Therefore, adding multiple exogenous variables as a consideration can analyze the data and make predictions more accurately. Taking the gold price as an example, the multivariate TAR model proposed has higher prediction accuracy than traditional forecasting models. Research Objectives: Analysis and prediction of multivariate TAR model with exogenous factors. Research Methods: Find out the exogenous multivariate threshold function, calculate the multivariate TAR threshold with exogenous variables, and conduct model architecture analysis and prediction. Research Findings: The multivariate TAR model with exogenous variables predictive ability is better than traditional forecasting methods. Research Innovation: Proposed exogenous variable threshold model algorithm. Research Value: Forecasting Strategies in Financial Empirical Analysis. |
| Reference: | 1.經濟部能源局https://www.moeaboe.gov.tw/ECW/populace/home/Home.aspx
2.吳柏林(1995)時間數列分析導論。台北：華泰書局。
3.楊奕農(2009)時間序列分析：經濟與財務上之應用。台北，雙葉書廊。
4.Tong H. and Lim K. S. (1980). Threshold Autoregressive, Limit Cycles and Cyclical Data (with Discussion), Journal of the Royal Statistical Society. Series B, Vol.42, No.3, pp.245-292.
5.Subba Rao T. and Gabr M. (1980). A test for linearity of stationary time series analysis, Journal of Time Series Analysis , Vol.1, No.1, pp145-158.
6.Haggan V. and Ozaki T. (1980). Amplitude-dependent Exponential AR Model Fitting for Non-linear Random Vibrations, in Time Series, (O. D. Anderson ed.), North-Holland, Amsterdam.
7.J. D. Byers and D.A. Peel (1995). Evidence on volatility spillovers in the interwar floating exchange rate period based on high/low prices, Applied Economics Letters, Taylor and Francis Journals, Vol.2, No.10, pp394-396.
8.Chow, G. C. (1960). Tests of equality between sets of coefficients in two linear regressions. Econometrica: Journal of the Econometric Society, 28(3), 591-605.
9.Liu Y, Garceau NY, Loros JJ and Dunlap JC (1997). Thermally regulated translational control of FRQ mediates aspects of temperature responses in the Neurospora circadian clock, Cell, Vol.89, pp477–486 .
10.Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica: Journal of the Econometric Society, 47-78.
11.Andrews, D. W., & Ploberger, W. (1994). Optimal tests when a nuisance parameter is present only under the alternative. Econometrica: Journal of the Econometric Society, 1383-1414.
12.Kumar K and Wu B (2001). Detection of change points in time series analysis with fuzzy statistics, International Journal of Systems Science, Vol.32, No.9, pp1185-1192.
13.Zhou, H. (2005). A Bayesian approach to integrating stochastic search variable selection and change point detection. Journal of Econometrics, 126(1), 57-77.
14.Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A k-means clustering algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28(1), 100–108.
15.Shen, Y., & Hakes, D. R. (1995). A modified threshold regression approach to measuring discrimination. Journal of Human Resources, 30(3), 457-477.
16.Hansen, B. E. (1999). Threshold effects in non-dynamic panels: Estimation, testing, and inference. Journal of econometrics, 93(2), 345-368.
|
| Description: | 碩士
國立政治大學
應用數學系
106751009 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0106751009 |
| Data Type: | thesis |
| Appears in Collections: | [應用數學系] 學位論文
|
Files in This Item:
| File |
Description |
Size | Format | |
|---|
| 100901.pdf | | 1545Kb | Adobe PDF2 | 0 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|
