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http://dx.doi.org/10.5351/KJAS.2013.26.6.987

Time Series Modelling of Air Quality in Korea: Long Range Dependence or Changes in Mean?  

Baek, Changryong (Department of Statistics, Sungkyunkwan University)
Publication Information
The Korean Journal of Applied Statistics / v.26, no.6, 2013 , pp. 987-998 More about this Journal
Abstract
This paper considers the statistical characteristics on the air quality (PM10) of Korea collected hourly in 2011. PM10 in Korea exhibits very strong correlations even for higher lags, namely, long range dependence. It is power-law tailed in marginal distribution, and generalized Pareto distribution successfully captures the thicker tail than log-normal distribution. However, slowly decaying autocorrelations may confuse practitioners since a non-stationary model (such as changes in mean) can produce spurious long term correlations for finite samples. We conduct a statistical testing procedure to distinguish two models and argue that the high persistency can be explained by non-stationary changes in mean model rather than long range dependent time series models.
Keywords
Long range dependence; power-law tail distribution; changes in mean;
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