There are some assumptions relating to CLRM. These assumptions are necessary to show that the estimation technique, Ordinary Least Square Method has a number of desirable properties and also that the hypothesis tests regarding the coefficient estimates could be validly conducted.
The Assumptions are:
- Linear Regression Model, that is, the model is linear in parameters and not necessarily in variables.
- X (explanatory variable) is fixed or known to us. Hence, X is non-stochastic.
- Mean of disturbance term, u is 0.
- u is homoscedastic, that is, u has equal variance for all the terms.
- There is not auto correlation between two disturbance terms, u. That is, covariance between them is 0.
- 0 correlation between u and X. Therefore, X is exogenous.
- Number of sample observations (n) is greater than number of parameters to be estimated.
- Variance of X is finite, that is, greater than 0.
- There is no specification bias or error in model, which means, all the relevant variables must be included in the linear regression model.
- There exists no perfect linear relationship between explanatory variables.
- Random error term u, follows Normal Distribution with mean 0.
10 Comments. Leave new
The assumptions have been clearly stated. And I’m glad that people are interested in a subject like ecometrics. Good efforts.
Nice one …
well explained…
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Very informative!
nice 🙂
Well written
You cleared my doubt.
Informative post 😀
New topic 😀
had been studying it since an year pretty much familiarised… great work keep it up