# Econometrics

## Performing a parallel pre-trend test in universal absorbing treatments in R

Introduction In a recent paper from the Journal of Research, Innovation and Technologies published last year, there was a significant discussion (Riveros Gavilanes, 2023) about how control variables might be related to the fulfillment of parallel trends in event study (or dynamic differences-in-differences) of panel data. In this article, we will replicate such paper and […]

## How to learn Macroeconomic CGE Modeling?

What is CGE Modeling? Computable General Equilibrium (CGE) modeling is a type of economic modeling used to study the impacts of economic policies and shocks on the economy as a whole. The goal of CGE modeling is to provide a comprehensive and consistent representation of the interactions among different sectors and agents within an economy.

## R for World Bank Data and Descriptive Plots

Summary: In this article, we will review very useful packages in the R library which allow downloading the data from the World Development Indicators (WDI) of the World Bank. After that, we will execute some simple tools to create a nice graph of correlations and some basic descriptive statistics. Let’s say we want to use

## Endogenous co-regressor… Not what you think!

In classic econometrics textbooks and classes, we often associate endogeneity to the correlation or relationship with the error term from a regressor. This is correct and fully agreed upon across authors and professors. But there’s some kind of new endogenous behavior that may not be correlated with the error/residual only, and it may be a

## Modeling Asset Prices with Geometric Brownian Motion in Python

One landmark theorem in Financial Economics is the Efficient Market Hypothesis (EMH). This theorem posits that in an arbitrage-free market, we can model an asset’s present price as the discounted expected future price: We can take the natural logarithm to show that the natural logarithm of asset prices follows a random walk – the best

## Threat to validity of regression analysis – Omitted Variables Bias

Most of the readers of this blog would be familiar with ordinary least squares estimator and regression models. Let us talk about one source that can cause these estimates and models to be biased and inconsistent. This is especially important when we think about the causal relationship of interest or the relationship which being studied.

## The black box and econometrics.

Some of the most popular models used in Data Analysis imply the use of the so-called “Black Box” approach. Regarding the simplest interpretation one can give in this context, it depends on the inputs and outputs that a certain model can deliver in terms of prediction power. If econometrics is thought to estimate population parameters,

## Estimating long-run coefficients from an ARDL model

Whether if we’re working with Time Series Data or Panel Data, most of the times we want to follow the analysis of the long-run behavior and the short-run dynamics. An interesting but well-known model that enable us for such approach is the Auto-Regressive Distributed Lag model which stands as ARDL. There are a lot of