Introduction to Heteroscedasticity and How to Correct It in Stata Using White’s Robust Standard Errors
Introduction to Heteroscedasticity and How to Correct It in Stata Using White’s Robust Standard Errors In this video, Dr. Ngozi ADEYELE, PhD, Founder Crunch Econometrix, discusses heteroscedasticity and how to correct it in Stata using White’s robust standard errors. Heteroscedasticity is a statistical problem that occurs when the variance of the residuals (the errors between […]
Explain the nature, cause and consequences of heteroscedasticity in your own words.
Econometrics/Fall 17 Homework Set #5 1. Explain the nature, cause and consequences of heteroscedasticity in your own words. Detect possible heteroskedasticity in your equation–with four variables (in the previous assignment). Justify your answer (explain why there is/is not heteroskedasticity). Suggest corrective remedies (even if serial correlation is not detected). 2. In a metropolitan center with more than […]
How to Forecast Volatility of the Conditional Variance in the GARCH Model
To forecast the volatility of the conditional variance using a GARCH model in EViews, follow these steps: Step-by-Step Guide: Load Your Data: Open EViews and load your time series data. Specify the GARCH Model: Go to Quick -> Estimate Equation. In the equation specification box, enter the mean equation (e.g., Y = C(1) + C(2)*X […]
How to Detect Heteroscedasticity using Eviews
Detecting heteroskedasticity in EViews involves running specific tests and analyzing residual plots. Here are the steps: Step-by-Step Guide: Run Initial OLS Regression: Load your dataset in EViews. Go to Quick -> Estimate Equation. Enter your regression equation (e.g., Y = C(1) + C(2)*X1 + C(3)*X2). Click OK to estimate using Ordinary Least Squares (OLS). Plot […]
How to correct Heteroscedasticity with Functional Forms of the Model in Eviews
Correcting heteroskedasticity by transforming the functional form of the model in EViews involves the following steps: Step-by-Step Guide: Run Initial OLS Regression: Load your dataset in EViews. Go to Quick -> Estimate Equation. Enter your regression equation (e.g., Y = C(1) + C(2)*X1 + C(3)*X2). Click OK to estimate using Ordinary Least Squares (OLS). Diagnose […]
How to correct Heteroscedasticity with Weighted (Generalised) Least Squares in Eviews
To correct heteroskedasticity with Weighted (Generalized) Least Squares (WLS/GLS) in EViews, follow these steps: Step-by-Step Guide: Run Initial OLS Regression: Load your dataset in EViews. Go to Quick -> Estimate Equation. Enter your regression equation (e.g., Y = C(1) + C(2)*X1 + C(3)*X2). Click OK to estimate using Ordinary Least Squares (OLS). Diagnose Heteroskedasticity: Check […]
How to correct Heteroscedasticity with Robust Standard Errors using Eviews
To correct heteroskedasticity using robust standard errors in EViews, follow these expanded steps: Step-by-Step Guide: Run the Initial OLS Regression: Open EViews and load your dataset. Navigate to Quick -> Estimate Equation. Enter the regression equation in the form of Dependent Variable = C(1) + C(2)*Independent Variable + …. Click OK to estimate the equation […]
Moderation Modelling in Time Series Analysis using EVIEWS
Here are the steps of moderation modelling in time series analysis using EViews, based on a hypothetical scenario: Define Research Objectives and Hypothesis: Clearly state your research question and the moderating effect you’re investigating. For example, you might be studying how marketing affects sales (independent variable) and how economic conditions (moderating variable) influence that relationship. […]
Linear vs. Logistic Probability Models: Which is Better, and When?
Interpretability Let’s start by comparing the two models explicitly. If the outcome Y is a dichotomy with values 1 and 0, define p = E(Y|X), which is just the probability that Y is 1, given some value of the regressors X. Then the linear and logistic probability models are: p = a0 + a1X1 + a2X2 + … + akXk (linear) ln[p/(1-p)] = b0 + b1X1 + b2X2 + … + bkXk (logistic) […]
The Difference Between the Bernoulli and Binomial Distributions
You might already be familiar with the binomial distribution. It describes the scenario where the result of an observation is binary—it can be one of two outcomes. You might label the outcomes as “success” and “failure” (or not!). Or, if you want to get mathematical about it, you might label them “1” and “0.” You […]
