Explain the steps followed in estimation of parameters through the method of Generalised least squares (GLS).

The Generalized Least Squares (GLS) method is a statistical method used to estimate the parameters of a linear regression model when the errors or residuals are not normally distributed or have heteroscedastic variances. The GLS method extends the ordinary least squares (OLS) method by incorporating information about the covariance structure of the errors or residuals.

The steps involved in estimating parameters through the GLS method are as follows:

1. Define the model: The first step is to define the linear regression model that you want to estimate. The model should have a dependent variable Y and one or more independent variables X.

2. Specify the covariance structure of the errors: The next step is to specify the covariance structure of the errors or residuals in the model. The GLS method requires that you have information about the covariance structure of the errors, which can be obtained from previous studies or estimated from the data.

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