Heteroscedasticity is a situation where the variance of residuals is non-constant. Hence, it violates one of the assumptions of Ordinary Least Squares (OLS) which states that the residuals are homoscedastic…
Cointegration refers to the situation where the variables have a long-run or equilibrium relationship. Such variables move together over time and can be said to have a similar wavelength or…
If a non-stationary time series has to be differenced “d” times to make it stationary, that time series is integrated of order "d". In other words, the order of integration…
The Dickey Fuller Test is a unit root based test of stationarity. The unit root based tests focus on the coefficient associated with the first lag of the time series…
The ACF or Autocorrelation Function is one of the most widely used methods to check for stationarity of a time series. Moreover, it also gives valuable insights into the autoregressive…
The Random Walk Model is an example of a non-stationary time series. This model is often used to discuss as well as illustrate the concepts of stationarity, unit root process…
A time series is said to be stationary if its characteristics do not change with time. Moreover, a stationary time series can be identified as strictly stationary or weak stationary.…
The Three-Stage Least Squares or 3SLS is applied to simultaneous equation models. It is different from single equation methods like Indirect Least Squares (ILS) and 2SLS because it is applied…
The Indirect Least Squares (ILS) is a method used to estimate simultaneous equation models that are exactly identified. Moreover, it is a single equation method because it is applied to…
A simultaneous equation model is said to be identified if we can obtain unique estimates of the coefficients in the model. The equations in identified models have a unique structure…
The OLS and other single equation models assume that variables treated as independent variables are exogenous. This implies a one-way relationship between independent and dependent variables. It is assumed that…
The Adjusted R square addresses the drawback of the R square by penalising the inclusion of additional independent variables. As a result, this ensures that additional unnecessary variables are not…
The R square and Adjusted R square are often used to assess the fit of the Ordinary Least Squares model. These measures, therefore, help ascertain how well the estimated model…
The OLS or Ordinary Least Squares is one of the most widely used models in Econometrics and many other fields. Therefore, the interpretation of coefficients is one of the most…
OLS or Ordinary Least Squares is one of the most common methods used in Econometrics. It is a linear regression technique that minimizes the sum of squared residuals (error term)…
