Forecasting Using Nonlinear Long Memory Models with Artificial Neural Network Expansion

Kongcharoen, Chaleampong

doi:10.1007/978-3-642-35443-4_17

Cited by 2 publications

(2 citation statements)

References 34 publications

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“…); non-linearities and structural breaks, which is of particular interest given the fact that these are both strongly related to long memory and fractional integration (see, e.g. Diebold and Inoue 2001;Granger and Hyung 2004;Ohanissian et al 2008;Kongcharoen 2013;etc. ); and the forecasting performance of alternative specifications; all these topics are left for future work.…”

Section: Discussionmentioning

confidence: 99%

Particulate matter 10 (PM10): persistence and trends in eight European capitals

Caporale

Gil-Alana

Carmona-González

2021

Air Qual Atmos Health

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This paper examines the statistical properties of daily PM10 in eight European capitals (Amsterdam, Berlin, Brussels, Helsinki, London, Luxembourg, Madrid and Paris) over the period 2014–2020 by applying a fractional integration framework; this is more general than the standard approach based on the classical dichotomy between I(0) stationary and I(1) non-stationary series used in most other studies on air pollutants. All series are found to be characterised by long memory and fractional integration, with orders of integration in the range (0, 1), which implies that mean reversion occurs and shocks do not have permanent effects. Persistence is the highest in the case of Brussels, Amsterdam and London. The presence of negative trends in Brussels, Paris and Berlin indicates some degree of success in reducing pollution in these capitals.

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Section: Discussionmentioning

confidence: 99%

Particulate matter 10 (PM10): persistence and trends in eight European capitals

Caporale

Gil-Alana

Carmona-González

2021

Air Qual Atmos Health

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

Particulate Matter 10 (PM10): Persistence and Trends in Eight European Capitals

2020

View full text Add to dashboard Cite

This paper examines the statistical properties of daily PM 10 in eight European capitals (Amsterdam, Berlin, Brussels, Helsinki, London, Luxembourg, Madrid and Paris) over the period 2014-2020 by applying a fractional integration framework; this is more general than the standard approach based on the classical dichotomy between I(0) stationary and I(1) non-stationary series used in most other studies on air pollutants. All series are found to be characterised by long memory and fractional integration, with orders of integration in the range (0, 1), which implies that mean reversion occurs and shocks do not have permanent effects. Persistence is the highest in the case of Brussels, Amsterdam and London. The presence of negative trends in Brussels, Paris and Berlin indicates some degree of success in reducing pollution in these capitals.

show abstract

Forecasting Using Nonlinear Long Memory Models with Artificial Neural Network Expansion

Cited by 2 publications

References 34 publications

Particulate matter 10 (PM10): persistence and trends in eight European capitals

Particulate matter 10 (PM10): persistence and trends in eight European capitals

Particulate Matter 10 (PM10): Persistence and Trends in Eight European Capitals

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