Abstract Details
Activity Number:
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488
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Type:
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Contributed
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Date/Time:
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Wednesday, August 12, 2015 : 8:30 AM to 10:20 AM
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Sponsor:
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Government Statistics Section
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Abstract #316587
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Title:
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Trend Estimation of Multivariate Time Series with Controlled Smoothness
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Author(s):
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Lilia L. Ramirez Ramirez* and Victor Guerrero and Alejandro Islas-Camargo
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Companies:
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Instituto Tecnológico Autónomo de México and Instituto Tecnológico Autónomo de México and Instituto Tecnológico Autónomo de México
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Keywords:
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Signal extraction ;
Smoothness index ;
Penalized Least Squares ;
Generalized Least Squares
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Abstract:
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We present an extension to filtering techniques to estimate trends of multivariate time series. The proposed method is based on a vector signal-plus-noise representation of Penalized Least Squares that requires only the first two sample moments, and introduces an index of smoothness. This index allows setting in advance a desired amount of smoothness to achieve. It is also a function of the correlation between the noises of the series and the sample size. Our proposal arises from a statistical solution to a multivariate GLS problem. Such a solution leads to an index of smoothness that is applicable in the general multivariate case, but we pay special attention to the bivariate situation. Here we show the closed-form expressions for calculating trend estimates with their corresponding variance-covariance matrices, and present the proposed algorithm for smoothing bivariate time series. We discuss the results on simulated data and a real application (Mexican and U.S. GDP data).
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Authors who are presenting talks have a * after their name.
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