Following the last post which gave an example to model the Cobb-Douglas utility function regarding microeconometrics, we need to provide an important aspect related to the behavior of the consumer. That is the budget constraint (referred to as a monetary linear constraint) which limits the number of goods that the consumer can buy and use […]
The budget constraints in the microeconomic approach Read More »
Regarding microeconometrics, we can find applications that go from latent variables to model market decisions (like logit and probit models) and techniques to estimate the basic approaches for consumers and producers. In this article, I want to start with an introduction of a basic concept in microeconomics, which is the Cobb-Douglas utility function and its
A brief example to model the Cobb-Douglas utility function using Stata. Read More »
As we saw in other econometric blogs of M&S Research Hub, the use of logarithms constitutes a usual practice in econometrics, not only for the problems that can be derived from overusing them, but also it was mentioned the advantage to reduce the Heteroscedasticity -HT- (Nau, 2019) present in the series of a dataset, and
Handling structural breaks with logarithms Read More »
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 »
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 »
