Linear Probability Model (LPM): Meaning and Problems

The Linear Probability Model is an application of Ordinary Least Squares to qualitative response or dependent variables. We often encounter dependent variables that are continuous, such as income or consumption,…

Durbin Watson Test for Autocorrelation

Economists James Durbin and Geoffrey Watson developed the Durbin Watson test in the early 1950s. They introduced the test in their paper "Testing for Serial Correlation in Least Squares Regression:…

Autocorrelation: Causes and Consequences

Autocorrelation occurs when the error terms in the model exhibit correlation or dependency on each other. That is, errors in previous periods influence the errors in the current period. Economists…

Multicollinearity: Detection and Solutions

Multicollinearity refers to a situation where the independent or explanatory variables in the model have a strong relationship with each other. Perfect multicollinearity exists if the correlation coefficient for these…

Weighted Least Squares Estimation

The Weighted Least Squares (WLS) method is a special form of Generalized Least Squares estimation. In this method, the original model is transformed so that the variance of residuals becomes…

Goldfeld Quandt Test for Heteroscedasticity

The Goldfeld Quandt Test is used to detect the presence of heteroscedasticity in a regression model. This test was introduced by economists Arthur Goldfeld and Richard Quandt in the 1960s.…

Breusch Pagan test for Heteroscedasticity

The Breusch Pagan test for heteroscedasticity is sometimes also referred to as the BPG or Breusch Pagan Godfrey test. It is one of the most widely known tests for detecting…

White Test for Heteroscedasticity

The White test is one of the most commonly used statistical methods of detecting heteroscedasticity. It focuses on analysing the residuals from regression models to check for heteroscedasticity. Furthermore, the…

Heteroscedasticity: Causes and Consequences

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…

Indirect Least Squares Estimation

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…

Simultaneous Equation Bias

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…

Adjusted R Square and its application

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…

R square and its drawback

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…

Ordinary Least Squares Estimation

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)…

Goodness of fit in Rstudio

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…

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