Temporal Aggregation in First Order Cointegrated Vector Autoregressive models
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Temporal Aggregation in First Order Cointegrated Vector Autoregressive models. / Milhøj, Anders; la Cour, Lisbeth Funding.
In: Advances and Applications in Statistical Sciences, Vol. 6, No. 4, 2011, p. 207-227.Research output: Contribution to journal › Journal article › Research › peer-review
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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