Hi Datta

1. Autocorrelation is a characteristic of data in which the correlation between the values of the same variables is based on related objects. It violates the assumption of instance independence, which underlies most of the conventional models. It generally exists in those types of data-sets in which the data, instead of being randomly selected, is from the same source.

2. The most common form of autocorrelation is first-order serial correlation, which can either be positive or negative.

i. Positive serial correlation is where a positive error in one period carries over into a positive error for the following period.

ii.Negative serial correlation is where a negative error in one period carries over into a negative error for the following period.

3. There is a very popular test called the Durbin Watson test that detects the presence of autocorrelation. If the researcher detects autocorrelation in the data, then the first thing the researcher should do is to try to find whether or not it is pure. If it is pure, then one can transform it into the original model that is free from pure autocorrelation.

4. You can test for autocorrelation with:

i. A plot of residuals. Plot et against t and look for clusters of successive residuals on one side of the zero line. You can also try adding a Lowess line, as in the image below.

ii. A Lagrange Multiplier Test.

iii. A correlogram. A pattern in the results is an indication for autocorrelation. Any values above zero should be looked at with suspicion.

iv. The Moran’s I statistic, which is similar to a correlation coefficient.

5. The test can be developed using different Statistical packages. Examples are:

i. In Minitab:

Click Stat > Regression > Regression > Fit Regression Model. Click “Results,” and check the Durbin-Watson statistic.

ii. SAS: Find directions here on the UCLA website.

iii. MATLAB: The procedure can be found here on the Mathworks site.

iv. SPSS: From the main regression dialog box, click Statistics. Check the box for Durbin-Watson (in the Residuals section of Linear Regression Statistics).

Regards,

Zuhair

Hi,

Your regression model is for cross sectional data, so you need to check for the independence of your explanatory variables (not autocorrelation, since it is only for time series data). Very simply you can use Durbin-Watson test in SPSS.

Tanks everyone for your valuable suggestions. I already adjusted my work with the help of your guidelines.