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Functional Principal Component Analysis for Cointegrated Functional Time Series

Won-Ki Seo

arXiv 25 Nov 2020 · Statistics — Methodology · publishedJournal of Time Series Analysis (2023)

arXiv:2011.12781 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Functional principal component analysis (FPCA) has played an important role in the development of functional time series analysis. This note investigates how FPCA can be used to analyze cointegrated functional time series and proposes a modification of FPCA as a novel statistical tool. Our modified FPCA not only provides an asymptotically more efficient estimator of the cointegrating vectors, but also leads to novel FPCA-based tests for examining essential properties of cointegrated functional time series.

Citation extraction

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appendix boundary found by appendix_command · 27% of the source is main text. Read the extracted text to check this.

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
1Li D, Robinson PM, Shang HL (2022) Nonstationary fractionally integrated functional time series0.92843100%
2Chang Y, Kim CS, and Park JY (2016) Nonstationarity in time series of state densities0.87418667%
3Nielsen, M, Seo WK, Seong D (2022) Inference on the dimension of the nonstationary subspace in functional time series0.78429748%
4Nyblom J, Harvey A (2000) Tests of common stochastic trends0.6445240%
5Horváth L, Kokoszka P, and Rice G (2014) Testing stationarity of functional time series0.6443267%
6Kokoszka P, Young G (2016) KPSS test for functional time series0.5853333%
7Harris D (1997) Principal components analysis of cointegrated time series0.56117718%
8Horváth L, Kokoszka P, Reeder R (2013) Estimation of the mean of functional time series and a two-sample problem0.5114225%
9Kwiatkowski D, Phillips PCB, Schmidt P, Shin Y (1992) Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series ha…0.5114225%
10Saikkonen P (1991) Asymptotically efficient estimation of cointegration regressions0.5113233%

Showing the top 10 of 97 scored citations.

Cited by, within the corpus

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

Citing paperIntensityMentionsSections
11cm Inference on common trends in functional time series1.00053
2Functional Regression with Nonstationarity and Error Contamination: Application to the Economic Impact of Climate Change0.794185
3Functional instrumental variable regression with an application to estimating the impact of immigration on native wages0.40511
4Nonlinear Temperature Sensitivity of Residential Electricity Demand: Evidence from a Distributional Regression Approach0.00011
5Functional Linear Projection and Impulse Response Analysis0.00011