CLRM – Assumption 2: Full Rank of Matrix X

Assumption 2 requires the matrix of explanatory variables X to have full rank. This means that in case matrix X is a N \times K matrix the rank of matrix X is K. Namely, Rank(X) = K

Two conditions are necessary to ensure assumption 2. First, the number of observations (N) cannot be smaller than the number of explanatory variables (K) in the model. Formally, N \geqslant K. Second, there cannot be an exact linear relationship between two explanatory variables. This means that it is impossible to include one variable twice or include a variable which is a linear combination of another variable as this would lead to exact multicollinearity. We have exact multicollinearity when our model includes two variables with an exact linear dependence. We cannot calculate the OLS estimator in the case perfect multicollinearity in our matrix X, because X'X would be singular.

Assumption 2 ensures that the vector of coefficients \beta is unambiguous. Alternatively, we can express assumption 2 the following way:

X \beta^{1} = X \beta^{2} \iff \beta^{1} = \beta^{2}


Assumptions of Classical Linear Regressionmodels (CLRM)

Overview of all CLRM Assumptions
Assumption 1
Assumption 2
Assumption 3
Assumption 4
Assumption 5

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6 Responses to CLRM – Assumption 2: Full Rank of Matrix X

  1. Pingback: CLRM – Assumption 1: Linear Parameter and correct model specification | Economic Theory Blog

  2. Pingback: Assumptions of Classical Linerar Regressionmodels (CLRM) | Economic Theory Blog

  3. Pingback: CLRM – Assumption 3: Explanatory Variables must be exogenous | Economic Theory Blog

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  5. Pingback: Violation of CLRM – Assumption 4.1: Consequences when the expected value of the error term is non-zero | Economic Theory Blog

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