Order estimation of high dimensional time series
Much research has focused on the problem of estimating the order of vector autoregressive (VAR) model and multivariate moving average (VMA) model. The most proposed solutions for this problem include Bayesian Information Criterion (BIC) and limiting spectral distribution of sample autocovariance mat...
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sg-ntu-dr.10356-1569312023-02-28T23:19:36Z Order estimation of high dimensional time series Zeng, Shijia Pan Guangming School of Physical and Mathematical Sciences GMPAN@ntu.edu.sg Science::Mathematics Much research has focused on the problem of estimating the order of vector autoregressive (VAR) model and multivariate moving average (VMA) model. The most proposed solutions for this problem include Bayesian Information Criterion (BIC) and limiting spectral distribution of sample autocovariance matrix. In this paper, two new approaches for order determination of VAR and VMA model are proposed. Maximum or sum of eigenvalues of sample autocovariance matrix is found to be able to select the order of VAR or VMA model. Another approach with the use of information criterion is also developed to select the order automatically. Time series data has been generated to examine the performance of two methods in existing work and two approaches proposed by ourselves. Bachelor of Science in Mathematical Sciences 2022-04-29T03:21:06Z 2022-04-29T03:21:06Z 2022 Final Year Project (FYP) Zeng, S. (2022). Order estimation of high dimensional time series. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156931 https://hdl.handle.net/10356/156931 en application/pdf Nanyang Technological University |
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Science::Mathematics Zeng, Shijia Order estimation of high dimensional time series |
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Much research has focused on the problem of estimating the order of vector autoregressive (VAR) model and multivariate moving average (VMA) model. The most proposed solutions for this problem include Bayesian Information Criterion (BIC) and limiting spectral distribution of sample autocovariance matrix. In this paper, two new approaches for order determination of VAR and VMA model are proposed. Maximum or sum of eigenvalues of sample autocovariance matrix is found to be able to select the order of VAR or VMA model. Another approach with the use of information criterion is also developed to select the order automatically. Time series data has been generated to examine the performance of two methods in existing work and two approaches proposed by ourselves. |
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Pan Guangming |
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Pan Guangming Zeng, Shijia |
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Final Year Project |
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Zeng, Shijia |
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Zeng, Shijia |
title |
Order estimation of high dimensional time series |
title_short |
Order estimation of high dimensional time series |
title_full |
Order estimation of high dimensional time series |
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Order estimation of high dimensional time series |
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Order estimation of high dimensional time series |
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order estimation of high dimensional time series |
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Nanyang Technological University |
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2022 |
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https://hdl.handle.net/10356/156931 |
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