## ADF Test: Augmented Dickey Fuller Equation

The Augmented Dickey Fuller or ADF Test of stationarity is a unit root based test. It attempts to overcome the shortcomings of the original Dickey Fuller test. Researchers often observed…

## Probit Model: Theory and Estimation

The Probit Model is used when we have a binary or qualitative dependent variable in the model. Similar to the Logit Model, the Probit Model overcomes the difficulties or problems…

## Logit Model: Theory and Estimation

The Logit Model is used to estimate models with a qualitative binary dependent variable. It overcomes the problems associated with the Linear Probability Model (LPM). In the post on LPM,…

## 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,…

## Random Effects Model: Assumptions and GLS Estimation

The Random Effects Model is also sometimes called the Error Components Model or the Two-error structure approach. The Fixed Effects Model has some problems such as the use of dummy…

## Pooled OLS vs Fixed Effects Model: F-test

The Fixed Effects Model for Panel data should only be applied if the cross-sectional or time-specific effects are significant. In the case where these effects are insignificant, a simple Pooled…

## Fixed Effects Model: LSDV Approach

The Least Squares Dummy Variables or LSDV approach is one of the ways to estimate the Fixed Effects Model. Hence, the Fixed Effects Model is sometimes called the Least Squares…

## 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…

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