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Sinusoidal Modeling Applied to Spatially Variant Tropospheric Ozone Air Pollution



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SINUSOIDAL MODELING APPLIED TO SPATIALLY VARIANT TROPOSPHERIC OZONE AIR POLLUTION By Nicholas Z Muller and Peter C B Phillips January 2006 COWLES FOUNDATION DISCUSSION PAPER NO 1548 COWLES FOUNDATION FOR RESEARCH IN ECONOMICS YALE UNIVERSITY Box 208281 New Haven Connecticut 06520 8281 http cowles econ yale edu Sinusoidal Modeling Applied to Spatially Variant Tropospheric Ozone Air Pollution1 Nicholas Z Muller School of Forestry and Environmental Studies Yale University 1773 Huntington Tpke Trumbull CT 06611 USA 203 386 9314 nicholas muller yale edu Peter C B Phillips Cowles Foundation Yale University University of York University of Auckland December 4th 2005 1 Phillips acknowledges support from the NSF under Grant No SES 04 142254 Mr Muller would like to thank the Glaser Progress Foundation for supporting this research Abstract This paper demonstrates how parsimonious models of sinusoidal functions can be used to fit spatially variant time series in which there is considerable variation of a periodic type A typical shortcoming of such tools relates to the di culty in capturing idiosyncratic variation in periodic models The strategy developed here addresses this deficiency While previous work has sought to overcome the shortcoming by augmenting sinusoids with other techniques the present approach employs station specific sinusoids to supplement a common regional component which succeeds in capturing local idiosyncratic behavior in a parsimonious manner The experiments conducted herein reveal that a semi parametric approach enables such models to fit spatially varying time series with periodic behavior in a remarkably tight fashion The methods are applied to a panel data set consisting of hourly air pollution measurements The augmented sinusoidal models produce an excellent fit to these data at three di erent levels of spatial detail JEL Classification C22 C23 Key words and phrases Air Pollution Idiosyncratic component Regional variation Semiparametric model



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