Temporal Aggregation in First Order Cointegrated Vector Autoregressive models

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Standard

Temporal Aggregation in First Order Cointegrated Vector Autoregressive models. / Milhøj, Anders; la Cour, Lisbeth Funding.

I: Advances and Applications in Statistical Sciences, Bind 6, Nr. 4, 2011, s. 207-227.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Milhøj, A & la Cour, LF 2011, 'Temporal Aggregation in First Order Cointegrated Vector Autoregressive models', Advances and Applications in Statistical Sciences, bind 6, nr. 4, s. 207-227.

APA

Milhøj, A., & la Cour, L. F. (2011). Temporal Aggregation in First Order Cointegrated Vector Autoregressive models. Advances and Applications in Statistical Sciences, 6(4), 207-227.

Vancouver

Milhøj A, la Cour LF. Temporal Aggregation in First Order Cointegrated Vector Autoregressive models. Advances and Applications in Statistical Sciences. 2011;6(4):207-227.

Author

Milhøj, Anders ; la Cour, Lisbeth Funding. / Temporal Aggregation in First Order Cointegrated Vector Autoregressive models. I: Advances and Applications in Statistical Sciences. 2011 ; Bind 6, Nr. 4. s. 207-227.

Bibtex

@article{3ad9e11622de4f9c8550cd02bb0b86ff,
title = "Temporal Aggregation in First Order Cointegrated Vector Autoregressive models",
abstract = "Many time series can be observed at different, but equally relevant sampling frequencies.This makes it important to study aggregation from e.g. daily or weekly to monthly series.Aggregation of course gives shorter time series and thereby reduced information, but spuriousphenomena, in e.g. daily observations, can on the other hand be avoided such that more importantfeatures become clearer. In the present study we contribute to the literature on temporalaggregation of cointegrated time series by giving a theorem for how the speed-of-adjustmentcoefficients in a n-dimensional VAR(1) process changes with the frequency of the data. We alsointroduce a graphical representation that will prove useful as an additional informational tool fordeciding the appropriate cointegration rank of a model. In two examples based on models of timeseries of different grades of gasoline, we demonstrate the usefulness of our results in practice.",
author = "Anders Milh{\o}j and {la Cour}, {Lisbeth Funding}",
year = "2011",
language = "English",
volume = "6",
pages = "207--227",
journal = "Advances and Applictions in Statistical Science",
issn = "0974-6811",
publisher = "Mili Publications",
number = "4",

}

RIS

TY - JOUR

T1 - Temporal Aggregation in First Order Cointegrated Vector Autoregressive models

AU - Milhøj, Anders

AU - la Cour, Lisbeth Funding

PY - 2011

Y1 - 2011

N2 - Many time series can be observed at different, but equally relevant sampling frequencies.This makes it important to study aggregation from e.g. daily or weekly to monthly series.Aggregation of course gives shorter time series and thereby reduced information, but spuriousphenomena, in e.g. daily observations, can on the other hand be avoided such that more importantfeatures become clearer. In the present study we contribute to the literature on temporalaggregation of cointegrated time series by giving a theorem for how the speed-of-adjustmentcoefficients in a n-dimensional VAR(1) process changes with the frequency of the data. We alsointroduce a graphical representation that will prove useful as an additional informational tool fordeciding the appropriate cointegration rank of a model. In two examples based on models of timeseries of different grades of gasoline, we demonstrate the usefulness of our results in practice.

AB - Many time series can be observed at different, but equally relevant sampling frequencies.This makes it important to study aggregation from e.g. daily or weekly to monthly series.Aggregation of course gives shorter time series and thereby reduced information, but spuriousphenomena, in e.g. daily observations, can on the other hand be avoided such that more importantfeatures become clearer. In the present study we contribute to the literature on temporalaggregation of cointegrated time series by giving a theorem for how the speed-of-adjustmentcoefficients in a n-dimensional VAR(1) process changes with the frequency of the data. We alsointroduce a graphical representation that will prove useful as an additional informational tool fordeciding the appropriate cointegration rank of a model. In two examples based on models of timeseries of different grades of gasoline, we demonstrate the usefulness of our results in practice.

M3 - Journal article

VL - 6

SP - 207

EP - 227

JO - Advances and Applictions in Statistical Science

JF - Advances and Applictions in Statistical Science

SN - 0974-6811

IS - 4

ER -

ID: 37431977