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Representation of I(1) and I(2) autoregressive Hilbertian processes

Brendan K. Beare, Won-Ki Seo

arXiv 27 Jan 2017 · Mathematics — Statistics Theory

arXiv:1701.08149 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We extend the Granger-Johansen representation theorems for I(1) and I(2) vector autoregressive processes to accommodate processes that take values in an arbitrary complex separable Hilbert space. This more general setting is of central relevance for statistical applications involving functional time series. We first obtain a range of necessary and sufficient conditions for a pole in the inverse of a holomorphic index-zero Fredholm operator pencil to be of first or second order. Those conditions form the basis for our development of I(1) and I(2) representations of autoregressive Hilbertian processes. Cointegrating and attractor subspaces are characterized in terms of the behavior of the autoregressive operator pencil in a neighborhood of one.

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Beare, B.\ K., Seo, J.\ and Seo, W.\ -K (2017) Cointegrated linear processes in Hilbert space self1.00084100%
2Franchi, M.\ and Paruolo, P (2018) Cointegration in functional autoregressive processes1.00063100%
3Johansen, S (1992) A representation of vector autoregressive processes integrated of order 20.92843100%
4Hu, B.\ and Park, J.\ -Y (2016) Econometric analysis of functional dynamics in the presence of persistence0.84333100%
5Johansen, S (2009) Representation of cointegrated autoregressive processes with application to fractional processes0.73732100%
6Johansen, S (1991) Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models0.73732100%
7Gohberg, I., Goldberg, S.\ and Kaashoek, M.\ A (1990) Classes of Linear Operators, Vol.\ 10.6936333%
8Granger, C.\ W.\ J (1983) Cointegrated variables and error-correcting models0.64441100%
9Bosq, D (2000) Linear Processes in Function Spaces0.64422100%
10Chang, Y., Hu, B.\ and Park, J.\ -Y (2016) On the error correction model for functional time series with unit roots0.64422100%

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Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Cointegration in functional autoregressive processes1.00094
21cm Inference on common trends in functional time series0.64422
32102.106260.40511
4The general solution to an autoregressive law of motion0.40511
5Functional Regression with Nonstationarity and Error Contamination: Application to the Economic Impact of Climate Change0.40511
6Large-Scale Curve Time Series with Common Stochastic Trends0.40511