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Approximate State Space Modelling of Unobserved Fractional Components

Tobias Hartl, Roland Weigand

arXiv 21 Dec 2018 · Econometrics

arXiv:1812.09142 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We propose convenient inferential methods for potentially nonstationary multivariate unobserved components models with fractional integration and cointegration. Based on finite-order ARMA approximations in the state space representation, maximum likelihood estimation can make use of the EM algorithm and related techniques. The approximation outperforms the frequently used autoregressive or moving average truncation, both in terms of computational costs and with respect to approximation quality. Monte Carlo simulations reveal good estimation properties of the proposed methods for processes of different complexity and dimension.

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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
1Hartl, Tschernig \ Weber (2020) Fractional trends in unobserved components models, Papers, arXiv.org self1.00063100%
2Hartl \ Weigand (2019) Multivariate fractional components analysis, Papers, arXiv.org1.00063100%
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6Grassi \ de Magistris (2012) When long memory meets the Kalman filter: A comparative study, Computational Statistics & Data Analysis (in press)0.73732100%
7Harvey (1991) Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge Books, Cambridge University Press0.73732100%
8Mesters, Koopman \ Ooms (2016) Monte Carlo maximum likelihood estimation for generalized long-memory time series models, Econometric Reviews 35(4): 659–6870.73732100%
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10Palma (2007) Long-Memory Time Series: Theory and Methods, Wiley0.64422100%

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