Autocorrelation and Bias in Short Time Series: An Alternative Estimator

Autocorrelation and Bias in Short Time Series: An Alternative Estimator The conventional first-order autocorrelationcoefficient r1 generates an empiricalbias when it is applied to short time series.The properties of this estimator have beenexamined with a Monte Carlo simulation studyusing the MATLAB program (version5.2). This study also analyzes the functionof the empirical bias with the polynomicregression and derives a polynomic fittingmodel for different sample sizes. In thisway, a new estimator that has been correctedby the absolute value of the fitting model(r1') is proposed. Having analyzed thestatistical properties of the estimator r1',it is shown that the empirical bias generatedby r1' is less in relationship to r1 andr1+. The results of the study make itpossible to verify that the mean squared errorassociated to the estimator r1 isless than that of r1. Thus, the coefficient r1'is recommended to estimate the lag-oneautocorrelation coefficient in samples under 50observations. Quality & Quantity Springer Journals

Autocorrelation and Bias in Short Time Series: An Alternative Estimator

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Kluwer Academic Publishers
Copyright © 2001 by Kluwer Academic Publishers
Social Sciences; Methodology of the Social Sciences; Social Sciences, general
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