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The Null Hypothesis - H0: There is no autocorrelation
The Alternative Hypothesis - H1: There is at least first order autocorrelation
Rejection Criteria: Reject H0 if LBQ1 >
Autocorrelation Function: RESI1
Lag ACF T LBQ
1 0.0081553 0.32 0.10
2 0.0065510 0.26 0.17
3 -0.0279832 -1.09 1.36
4 -0.0079441 -0.31 1.46
5 0.0254074 0.99 2.44
There are 83 lags but the first 5 have been used identify whether there is auto correlation present:
Lag
LBQ
Chi-Squared
Interpretation
1
0.10
= 3.84
Since LBQ = 0.10 < 3.84 so accept H0 as there is sufficient evidence to suggest there is no autocorrelation
2
0.17
= 5.99
Since LBQ = 0.17 < 5.99 so accept H0 as there is sufficient evidence to suggest there is no autocorrelation
3
1.36
= 7.81
Since LBQ = 1.36 < 7.81 so accept H0 as there is sufficient evidence to suggest there is no autocorrelation
4
1.46
= 9.48
Since LBQ = 1.46 < 9.48 so accept H0 as there is sufficient evidence to suggest there is no autocorrelation
5
2.44
= 11.07
Since LBQ = 2.44 < 11.07 so accept H0 as there is sufficient evidence to suggest there is no autocorrelation
#how to analyse data
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The Null Hypothesis - H0: There is autocorrelation The Alternative Hypothesis - H1: There is no autocorrelation Rejection Criteria: Reject H0 (n-s)R 2 > = (1515 - 4) x (0.
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