classical asumptions

Econometrics: What is the Gauss Markov Theorem?

According to the Gauss Markov Theorem, if the first six classical assumptions are met within your model, then the estimates produced by Ordinary Least Squares will be BLUE. This means that Ordinary Least Squares will produce the Best Linear Unbiased Estimates available. Hence, BLUE. Check out our blogs on the classical assumptions here. So what exactly …

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Econometrics: Classical Assumption 7 – The error term is normally distributed.

According to the Gauss Markov Theorem, an Ordinary Least Squares (OLS) model can be considered as the Best Linear Unbiased Estimator (BLUE) as long as the first six classical assumptions are met. However, if you are conducting an econometric analysis and wish to make statistical inferences about the data, such as performing statistical hypothesis testing …

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Econometrics: Classical Assumption 6 – No explanatory variable is a perfectly linear function of any or all of the other explanatory variables.

Two variables are a perfectly linear function of each other when one variable can be entirely explained by the movement in the other variable and vice versa even though the absolute change in each variable may differ. Examples of positively perfectly collinear variables would include age and experience or sales and taxes paid. Whereas examples …

Econometrics: Classical Assumption 6 – No explanatory variable is a perfectly linear function of any or all of the other explanatory variables. Read More »

Econometrics: Classical Assumption 5 – The error term has a constant variance.

In econometrics, variance can be described as the spread of the data from the average value of the data set in question. When running Ordinary Least Squares (OLS), it is vital that this level of variation in the data stays constant. If the variance of the errors in the data set is not consistent but …

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Econometrics: Classical Assumption 4 – All observations of the error term are entirely uncorrelated with each other.

  In econometrics, a fantastic way of observing the relationship between the respective error observations within a given data set  is through a visual relationship. It makes any relationship immediately obvious when you draw a representation of the residual level present within the data set. What we see here is what can be described as …

Econometrics: Classical Assumption 4 – All observations of the error term are entirely uncorrelated with each other. Read More »

Econometrics: Classical Assumption 3 – There is no correlation between any of the explanatory variables and the error term.

There cannot be any correlation between the explanatory variables present within a given equation and the error term. If it were the case that the error term and the explanatory variables were in fact correlated, what you would find happening is that some of the variation that occurs in the dependent or Y variable will …

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Econometrics: Classical Assumption 2 – The error term has a zero population mean.

When the intercept term is present in any given regression equation, it forces the average of the error term to be equal to zero. This occurs because the intercept, when included, will account for the fixed portion change of the dependent variable and the explanatory variables will account for the non-fixed portion. Therefore, the error term …

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Econometrics: Classical Assumption 1 – The model is linear in the slope coefficients and error term.

In this assumption, it is assumed that the model is linear in the slope coefficients and the associated error term in the equation. What this means is that the slope in a given linear equation is a number that it is neither squared or it is not a reciprocal for example. It is a number that …

Econometrics: Classical Assumption 1 – The model is linear in the slope coefficients and error term. Read More »

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