The common intercept value in a linear regression equation is always taken as one. The basis for this is that including a constant in a given model is the same as including an explanatory variable whose value is always equal to one. When using dummy variables in econometrics, dummy variable will always takes the value …
The correlation coefficient is used in economics and throughout econometrics to measure the strength of a relationship between two given variables. This econometric measure also gives an indication of whether the two variables are positively related or negatively related to each other. The correlation coefficient is measured between -1 to +1. For example if we …
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 …
The p-value tells us the lowest level of significance at which we can reject the null hypothesis. What we mean by this is that if let’s say your testing level of significance is 5% or 0.05. If you are presented with a p-value lower than this, you can reject the null hypothesis. Let me explain …
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 …
Econometrics: Classical Assumption 7 – The error term is normally distributed. Read More »
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 …
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 …
Econometrics: Classical Assumption 5 – The error term has a constant variance. Read More »
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 …
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 …
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 …
Econometrics: Classical Assumption 2 – The error term has a zero population mean. Read More »
