Python-bloggers

A recession a year from now? As we saw in the last post, when we run a model with a 6-month look forward, it does a fairly reasonable job in predicting a recession, assuming we use a threshold closer to recession base rate. In this post, we look...

Where did we go wrong? Not another model! As we saw in the last post, one iteration of the yield curve – the spread between 10-year and 3-month Treasuries – doesn’t generate a great model of recession probabilities. Part of this is that recessio...

Where did we go wrong? Not another model! As we saw in the last post, one iteration of the yield curve – the spread between 10-year and 3-month Treasuries – doesn’t generate a great model of recession probabilities. Part of this is that recessio...

All models are wrong Build the model In the last post, we discussed the yield curve, why investors focus on it, and looked at one measure of the curve – the spread between 10-year and 3-month Treasury yields. In this post, we build a model that...

All models are wrong Build the model In the last post, we discussed the yield curve, why investors focus on it, and looked at one measure of the curve – the spread between 10-year and 3-month Treasury yields. In this post, we build a model that...

Introduction What is a yield curve? (Skip if you already know!) Show me the data! (Start here if don’t want the background) Investing pundits like to quote the yield curve as a nearly infallible indicator for the next recession. But what do the ...

Introduction What is a yield curve? (Skip if you already know!) Show me the data! (Start here if don’t want the background) Investing pundits like to quote the yield curve as a nearly infallible indicator for the next recession. But what do the ...

In my neverending quest to track The Popularity of Data Science Software, it’s time to update the section on Scholarly Articles. The rapid growth of R could not go on forever and, as you’ll see below, its use actually declined … Continue reading →

Random forest is a type of machine learning algorithm in which the algorithm makes multiple decision trees that may use different features and subsample to making as many trees as you specify. The trees then vote to determine the class of an example. This approach helps to deal with the ...