Forecasting Multiple Time Series With One-Sided Dynamic Principal Components

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We define one-sided dynamic principal components (ODPC) for time series as linear combinations of the present and past values of the series that minimize the reconstruction mean squared error. Usually dynamic principal components have been defined as functions of past and future values of the series...

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Autor principal: Peña, D.
Otros Autores: Smucler, E., Yohai, V.J
Formato: Capítulo de libro
Lenguaje:Inglés
Publicado: American Statistical Association 2019
Acceso en línea:Registro en Scopus
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100 1 |a Peña, D. 
245 1 0 |a Forecasting Multiple Time Series With One-Sided Dynamic Principal Components 
260 |b American Statistical Association  |c 2019 
270 1 0 |m Smucler, E.; Department of Mathematics and Statistics, Universidad Torcuato Di Tella, Avenida Figueroa Alcorta 7350, Argentina; email: ezequiels.90@gmail.com 
506 |2 openaire  |e Política editorial 
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520 3 |a We define one-sided dynamic principal components (ODPC) for time series as linear combinations of the present and past values of the series that minimize the reconstruction mean squared error. Usually dynamic principal components have been defined as functions of past and future values of the series and therefore they are not appropriate for forecasting purposes. On the contrary, it is shown that the ODPC introduced in this article can be successfully used for forecasting high-dimensional multiple time series. An alternating least-squares algorithm to compute the proposed ODPC is presented. We prove that for stationary and ergodic time series the estimated values converge to their population analogs. We also prove that asymptotically, when both the number of series and the sample size go to infinity, if the data follow a dynamic factor model, the reconstruction obtained with ODPC converges in mean square to the common part of the factor model. The results of a simulation study show that the forecasts obtained with ODPC compare favorably with those obtained using other forecasting methods based on dynamic factor models. Supplementary materials for this article are available online. © 2019, © 2019 American Statistical Association.  |l eng 
593 |a Department of Statistics and Institute of Financial Big Data, Universidad Carlos III de Madrid, Getafe, Spain 
593 |a Department of Mathematics and Statistics, Universidad Torcuato Di Tella, Buenos Aires, Argentina 
593 |a Department of Mathematics, Instituto de Calculo, Universidad de Buenos Aires, Buenos Aires, Argentina 
593 |a School of Exact and Natural Sciences, Universidad de Buenos Aires, Argentina 
593 |a CONICET, Buenos Aires, Argentina 
690 1 0 |a DIMENSIONALITY REDUCTION 
690 1 0 |a DYNAMIC FACTOR MODELS 
690 1 0 |a HIGH-DIMENSIONAL TIME SERIES 
700 1 |a Smucler, E. 
700 1 |a Yohai, V.J. 
773 0 |d American Statistical Association, 2019  |p J. Am. Stat. Assoc.  |x 01621459  |w (AR-BaUEN)CENRE-264  |t Journal of the American Statistical Association 
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