Forecasting of Time Series based on Vector Autoregression Model and Maximum Cross-correlation
碩士 === 國立政治大學 === 統計研究所 === 101 === The selection of methods plays an important role in the prediction based on time-series data. In most literature reviews, the vector autoregression model(VAR) has been a popular choice for prediction for many years. There are some disadvantages of this method: (i)...
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| Format: | Others |
| Language: | zh-TW |
| Online Access: | http://ndltd.ncl.edu.tw/handle/84006675447842943855 |
| Summary: | 碩士 === 國立政治大學 === 統計研究所 === 101 === The selection of methods plays an important role in the prediction based on time-series data. In most literature reviews, the vector autoregression model(VAR) has been a popular choice for prediction for many years. There are some disadvantages of this method: (i) the model selection procedure can be really complex; (ii) the model assumptions are difficult to validate; (iii) it requires a large amount of data for model building. The objective of this thesis is to provide an new multivariate-time series prediction method based on the concept of maximum cross-correlation. It requires merely the assumption of “fair linearity” between two time series under investigation. This thesis also compares the proposed method to the vector autoregressive (VAR) model which is widely used in time series analysis with the expectation to provide a new prediction method in practical data analysis. We use data from the Taiwan equity funds and the portfolio of those funds to compare the prediction performances of these two methods. Using the mean prediction squared errors (MPSE) as assessment criterion, the prediction method based on the maximum cross-correlation best performs under all prediction periods.
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