A usual practice while we’re handling economic data, is the use of logarithms, the main idea behind using them is to reduce the Heteroscedasticity -HT- of the data (Nau, 2019). Thus reducing HT, implies reducing the variance of the data. Several times, different authors implement some kind of double logarithm transformation, which is defined as […]
Taking Logarithms of Growth Rates and Log-based Data. Read More »
A discussed solution in order to accomplish the normality assumption in regression models relates to the correct specification of a Data Generating Process (Rodríguez Revilla, 2014), the objective here is to demonstrate how functional form might influence the distribution of the residuals in a regression model using ordinary least squares technique. Let’s start with a
The impact of functional form over the normality assumption in the residuals Read More »
The regression analysis which is basic tool of econometrics was invented in 1880s by Francis Galton, a cousin of Charles Darwin who is famous for his theory of evolution. Like Darwin, Galton was a biologist interested in the laws of heredity, and he intended to use the regression for the laws of heredity. He used
How Modern Time Series Analysis Emerged? Read More »
A traditional approach of analyzing the residuals in regression models can be identified over the Classical Assumptions in Linear Models (Rodríguez Revilla, 2014), which primarily involves the residuals in aspects as homoscedasticity, no serial correlation (or auto-correlation), no endogeneity, correct specification (this one includes no omitted variables, no redundant variables, and correct functional form) and
Discussing the Importance of Stationary Residuals in Time Series Read More »
