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…
After estimating Ordinary Least Squares or OLS in Rstudio, we must ensure that the model is a good fit. The model must satisfy the assumptions of OLS including heteroscedasticity, no…
The VAR-VECM Goodness of fit can be analyzed using similar methods. After estimating Vector Autoregressive (VAR) or Vector Error Correction Mechanism (VECM), it is essential to assess how well the…
Information Criteria are used to compare and choose among different models with the same dependent variable. Akaike Information Criterion (AIC) and Schwarz or Bayesian Information Criterion (SIC or BIC) are…
The usual Goodness-of-fit statistics such as R-square and Adjusted R-square are not applicable in the case of Qualitative Response models. This is because the Ordinary Least Squares method of estimation…
The Standard Error of an estimate is the measure of the standard deviation of that coefficient. It helps to determine the reliability or precision of a coefficient estimated by the…
