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Robust Estimation of Heckman Model Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Quantile Forecasting of PM10 Data in Korea Based on Time Series Models

Authors : Yingshi Xu, Sangyeol Lee

Published in: Robustness in Econometrics

Publisher: Springer International Publishing

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Abstract

In this chapter, we analyze the particulate matter PM10 data in Korea using time series models. For this task, we use the log-transformed data of the daily averages of the PM10 values collected from Korea Meteorological Administration and obtain an optimal ARMA model. We then conduct the entropy-based goodness of fit test for the obtained residuals to check the departure from the normal and skew-t distributions. Based on the selected skew-t ARMA model, we obtain conditional quantile forecasts using the parametric and quantile regression methods. The obtained result has a potential usage as a guideline for the patients with some respiratory disease to pay more attention to health care when the conditional quantile forecast is beyond the limit values of severe health hazards.

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Metadata
Title
Quantile Forecasting of PM10 Data in Korea Based on Time Series Models
Authors
Yingshi Xu
Sangyeol Lee
Copyright Year
2017
Book
Robustness in Econometrics

Print ISBN: 978-3-319-50741-5

Electronic ISBN: 978-3-319-50742-2

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-50742-2_36

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