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http://dx.doi.org/10.5657/kfas.2002.35.3.216

Autocorrelation in Statistical Analyses of Fisheries Time Series Data  

Park Young Cheol (Seikai National Fisheries Research Institute)
Hiyama Yoshiaki (Seikai National Fisheries Research Institute)
Publication Information
Korean Journal of Fisheries and Aquatic Sciences / v.35, no.3, 2002 , pp. 216-222 More about this Journal
Abstract
Autocorrelation in time series data can affect statistical inference in correlation or regression analyses. To improve a regression model from which the residuals are autocorrelated, Yule-Walker method, nonlinear least squares estimation, maximum likelihood method and 'prewhitening' method have been used to estimate the parameters in a regression equation. This study reviewed on the estimation methods of preventing spurious correlation in the presence of autocorrelation and applied the former three methods, Yule-Walker, nonlinear least squares and maximum likelihood method, to a 20-year real data set. Monte carlo simulation was used to compare the three parameter estimation methods. However, the simulation results showed that the mean squared error distributions from the three methods simulated do not differ significantly.
Keywords
Autocorrelation; Yule-Walker method; Prewhitening; Monte carlo simulation; Fisheries time series data;
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