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AUTOCORRELATION IN SHORT TIME SERIES WITH TRENDS: A SIMULATION STUDY OF ESTIMATION AND SIGNIFICANCE TESTING WITH APPLICATION TO AIR QUALITY DATA

RAYMOND K. W. WONG

Air Policy Section, Alberta Environment and Sustainable Resource Development, 9th floor, Oxbridge Place, 9820 106 St. Edmonton, Alberta T5K 2J6, Canada

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https://doi.org/10.1142/S1793536914500071Cited by:0 (Source: Crossref)

Abstract

The estimation and significance testing of the first-order autoregressive (AR1) coefficient in short time series with trends are examined. The purpose is to identify the difficulties to which analysis procedures need to adjust for better results. The delta recursive AR1 estimator r_δ and the Sen–Theil trend estimator are viable for short sequence application. Significance testing for r_δ has low power. But the existence of trend has negligible influence in estimation and testing. The common practice of trend removal before AR1 estimation gives poorer results. Application to air quality data showed this could greatly change conclusions. Implication to analysis is discussed.

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