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Consequences, Detection And Forecasting With Autocorrelated Errors   Ademola Adetunji and Olusoga Fasoranbaku

Consequences, Detection And Forecasting With Autocorrelated Errors

88 страниц. 2012 год.
LAP Lambert Academic Publishing
Problem of autocorrelation arises if the assumption of the Classical Linear Regression Model that the errors terms are not autocorrelated is violated. As a consequence, the usual t, F, and ?2 tests cannot be legitimately applied. This text uses various econometric approaches to critically observe the associated problems. Graphical method; Durbin-Watson method; Breush-Godfrey method; and The Runs Test were used to detect existence of autocorrelation among residuals of econometric data. In correcting autocorrelation, the method of first-difference, based on Durbin-Watson d-statistic and the dynamic forecasting techniques were used. The result gave a significantly reduced estimated autocorrelation coefficient. This improves the efficiency of the forecast and the use of various statistics in making inference.
 
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