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@article{Wu:2017:10.1017/S0266466617000068,
author = {Wu, WB and Zaffaroni, P},
doi = {10.1017/S0266466617000068},
journal = {Econometric Theory},
pages = {1--22},
title = {Asymptotic theory for spectral density estimates of general multivariate time series},
url = {http://dx.doi.org/10.1017/S0266466617000068},
volume = {34},
year = {2017}
}

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TY  - JOUR
AB  - We derive uniform convergence results of lag-window spectral density estimates for a general class of multivariate stationary processes represented by an arbitrary measurable function of iid innovations. Optimal rates of convergence, that hold as both the time series and the cross section dimensions diverge, are obtained under mild and easily verifiable conditions. Our theory complements earlier results, most of which are univariate, which primarily concern in-probability, weak or distributional convergence, yet under a much stronger set of regularity conditions, such as linearity in iid innovations. Based on cross spectral density functions, we then propose a new test for independence between two stationary time series. We also explain the extent to which our results provide the foundation to derive the double asymptotic results for estimation of generalized dynamic factor models.
AU  - Wu,WB
AU  - Zaffaroni,P
DO  - 10.1017/S0266466617000068
EP  - 22
PY  - 2017///
SN  - 0266-4666
SP  - 1
TI  - Asymptotic theory for spectral density estimates of general multivariate time series
T2  - Econometric Theory
UR  - http://dx.doi.org/10.1017/S0266466617000068
UR  - https://www.cambridge.org/core/journals/econometric-theory/article/asymptotic-theory-for-spectral-density-estimates-of-general-multivariate-time-series/8FCD3DFFD9A30DEF356EEE8DD203030D
UR  - http://hdl.handle.net/10044/1/74345
VL  - 34
ER  -

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