Replicação em economia

John Cochrane soltou um post bacana sobre replicação em economia. Vale a pena conferir.

On replication in economics. Just in time for bar-room discussions at the annual meetings.

“I have a truly marvelous demonstration of this proposition which this margin is too narrow to contain.” -Fermat

“I have a truly marvelous regression result, but I can’t show you the data and won’t even show you the computer program that produced the result” – Typical paper in economics and finance.

The problem 

Science demands transparency. Yet much research in economics and finance uses secret data. The journals publish results and conclusions, but the data and sometimes even the programs are not available for review or inspection.  Replication, even just checking what the author(s) did given their data, is getting harder.

Quite often, when one digs in, empirical results are nowhere near as strong as the papers make them out to be.

I have seen many examples of these problems, in papers published in top journals. Many facts that you think are facts are not facts. Yet as more and more papers use secret data, it’s getting harder and harder to know.

The solution is pretty obvious: to be considered peer-reviewed “scientific” research, authors should post their programs and data. If the world cannot see your lab methods, you have an anecdote, an undocumented claim, you don’t have research. An empirical paper without data and programs is like a theoretical paper without proofs.

(continue lendo no blog do Cochrane)

Dados de pesquisas eleitorais de 1989 a 2015

Neale Ahmed El-Dash, do Polling Data (que já mencionamos aqui algumas vezes, como no modelo de impeachment), acabou de divulgar dados de pesquisas eleitorais brasileiras publicadas entre 1989 a 2015. Você pode acessar os dados clicando em  “Acervo/Past Elections”.


A desigualdade de renda se manteve estável no Brasil? Ou sobre a acurácia das variáveis econômicas IV

Paper do Pedro Souza e Marcelo Medeiros e apresentação do Marcelo Medeiros na UERJ:

Dica do Leo Monastério.

Inferência causal e Big Data: Sackler Big Data Colloquium

Uma série de palestras interessantes do Sackler Big Data Colloquium:


Hal Varian: Causal Inference, Econometrics, and Big Data


Leo Bottou: Causal Reasoning and Learning Systems


David Madigan: Honest Inference From Observational Database Studies


Susan Athey: Estimating Heterogeneous Treatment Effects Using Machine Learning in Observational Studies