In this article I want to update and improve an analysis I did some time ago, and published on LinkedIn, which can be accessed through this link: Credit Granting can be used as a proxy for the Financial Cycle, which tends to present a “boom” before the beginning of a recession. Intro I will start with the definition of a cycle: a cycle is a low-frequency variation event.

Modeling Principles Almost all, if not all, applied science is based on the idea of a model. There are problems in the real world, and we would like to create a theory to solve such problems. One of the pillars or the main foundation of quantitative modeling is its effectiveness. But how can a model be effective? By demonstrating that it is simple, reproducible, and applicable in other areas of knowledge.

Aqui voce pode encontrar a apresentação: Link da Apresentação Aqui você se cadastra para participar do sorteio dos livros: Link para o Formulário You can also reproduce this by choosing the city you want. Check out the complete code on my Github. Hey! 👋, did you find my work useful? Consider buying me a coffee ☕, by clicking here 👇🏻 Reuse CC BY 4.0 Citation For attribution, please cite this work as: Costa, Henrique. 2024.

Rambling (proxy for an introduction) We are experiencing the chaos of the information age, and its technological advents with voracious evolution and expansion. And in the midst of all this we have the advent of Big Data that has reached the delights of many with Data Science, Machine Learning and Artificial Intelligence. As a result, the idea of mathematical and statistical “models” became the rage, gaining visibility.

I started this website some time ago with the aim of writing down the expansion of my knowledge and experience in the area of financial risk management. And also other things like programming with R, econometric modeling and a little about data science and machine learning . So I’m going to use this publication as a starting point, and start discussing texts and examples applied to financial risks.

Having decided to improve my skills in the short space of time left between my professional life and personal life, I started researching the available data science options.

Na publicação de hoje, quero trazer uma brincadeira que fiz comigo mesmo, um pequeno incentivo para voltar a publicar conteúdos sobre R (eu já estava enferrujado) entre outros assunto sobre economia. Passeando pelo instagram, encontrei um perfil que pública muita informação sobre economia em formato de visualização, o Economista Visual.

When it comes to job titles, data scientist is one of the biggest buzzwords in recent years. It is also one of the fastest growing fields of professional activity. Can an economist really be a data scientist? What skills are needed?

In today’s post, I want to start a discussion about the use of mathematics and statistics in economics, and with this shocking sentence: When a person studies economics for the first time, it is likely that they will not encounter “crazy” equations that go beyond basic mathematics.

I will present here some personal reflections on a concept that is considered very simple and perhaps common in economics, but which is labeled as the height of ignorance for making an assumption that cannot be observed in reality, but which in my view is a mistake. mistake.