Author: Econometrics and Free Software
Synthetic micro-datasets: a promising middle ground between data privacy and data analysis
Survey and administrative data are essential for scientific research, however accessing such datasets can be very tricky, or even impossible. In my previous job I was responsible for getting access...continue reading.
Synthetic micro-datasets: a promising middle ground between data privacy and data analysis
Survey and administrative data are essential for scientific research, however accessing such datasets can be very tricky, or even impossible. In my previous job I was responsible for getting access...continue reading.
Dynamic discrete choice models, reinforcement learning and Harold, part 2
In this blog post, I present a paper that has really interested me for a long time. This is part2, where I will briefly present the model of the paper,...continue reading.
Dynamic discrete choice models, reinforcement learning and Harold, part 2
In this blog post, I present a paper that has really interested me for a long time. This is part2, where I will briefly present the model of the paper,...continue reading.
Dynamic discrete choice models, reinforcement learning and Harold, part 1
Introduction I want to write about an Econometrica paper written in 1987 (jstor link) by John Rust, currently Professor of Economics at Georgetown University, paper which has been on my...continue reading.
Dynamic discrete choice models, reinforcement learning and Harold, part 1
Introduction I want to write about an Econometrica paper written in 1987 (jstor link) by John Rust, currently Professor of Economics at Georgetown University, paper which has been on my...continue reading.
Intrumental variable regression and machine learning
Just like the question “what’s the difference between machine learning and statistics” has shed a lot of ink (since at least Breiman (2001)), the same question but where statistics is...continue reading.
Intrumental variable regression and machine learning
Just like the question “what’s the difference between machine learning and statistics” has shed a lot of ink (since at least Breiman (2001)), the same question but where statistics is...continue reading.
Multiple data imputation and explainability
Introduction Imputing missing values is quite an important task, but in my experience, very often, it is performed using very simplistic approaches. The basic approach is to impute missing values...continue reading.
Multiple data imputation and explainability
Introduction Imputing missing values is quite an important task, but in my experience, very often, it is performed using very simplistic approaches. The basic approach is to impute missing values...continue reading.
Cluster multiple time series using K-means
I have been recently confronted to the issue of finding similarities among time-series and though about using k-means to cluster them. To illustrate the method, I’ll be using data from...continue reading.
Split-apply-combine for Maximum Likelihood Estimation of a linear model
Intro Maximum likelihood estimation is a very useful technique to fit a model to data used a lot in econometrics and other sciences, but seems, at least to my knowledge,...continue reading.
Split-apply-combine for Maximum Likelihood Estimation of a linear model
Intro Maximum likelihood estimation is a very useful technique to fit a model to data used a lot in econometrics and other sciences, but seems, at least to my knowledge,...continue reading.
{disk.frame} is epic
Note: When I started writing this blog post, I encountered a bug and filed a bug report that I encourage you to read. The responsiveness of the developer was exemplary....continue reading.
{disk.frame} is epic
Note: When I started writing this blog post, I encountered a bug and filed a bug report that I encourage you to read. The responsiveness of the developer was exemplary....continue reading.