Category Archives: Blogs

Tomorrow is the 11th ECB Forecasting Conference. I am excited to see so many top authors: Sims, Engle, Koop, Marcellino, Schorfeide, and many more. It is fitting that so many progenitors of the innovative models of yesteryear and workhorse methods of today — VAR, ARCH, and Bayesian macroeconometrics — are here to oversee the nextContinue reading “Macro Random Forest Leads To Macro Gains In Macro Forecasting”

There is a popular Venn diagram that purports that data science exists at the intersection of applied statistics, programming, and domain knowledge. Companies would love nothing more than to replace their statistician, software developer, and consultant with one person. Unfortunately, life experience says that very few people can be experts in all three distinct andContinue reading “Data Scientists: Jack of All Trades, Master of One?”

I took two courses that explicitly touched on causal inference in college. Both began with the idea that the Rubin average causal effect of treatment D on outcome Y is given by: The idea here is that many things may determine Y. D determines Y, but so do other things (X). If we can estimateContinue reading “The Limited Usefulness of Rubin Causality For Decision Makers”

In many intro time series classes, you come across the autoregressive integrated moving average (ARIMA) forecasting technique. But you only took economics or finance so you can infiltrate the capitalist beast and make out like a tapeworm. How does ARIMA make my bank account go arriba? A lot of people fail when trying to useContinue reading “ARIMA For Options Investing”

In industry, reduced-form economic models need to be simple and understandable. Assume your audience is passively familiar with regression from that one stats course in college around the time they realized beer bongs do not go well with their lexapro. Take your target and regress on predictors via OLS. is a few select predictors –Continue reading “Academia vs Industry: In Pursuit of Truth and Story”

I am somewhat irked by the lack of a comprehensive R package for multivariate time series. Rob Hyndman’s forecast/ fable package is an excellent, if not exhaustive (how could it really be?), resource for univariate time series. Here, I am collecting a list of packages that work multivariate time series models, particularly vector autoregressions (VARs).Continue reading “VARs in R”