Structuring data for API access and WiNDC.jl
August 01, 2025
How should we be storing and using WiNDC and economic data? We currently use sparse arrays to store sets and parameters. However, this approach means every model is tightly coupled to the specific application, for example the National module has fewer parameters than the Household module. This makes it difficult write generic code that can be used across modules. In this post I discuss a new data model that is more flexible and can be used across all modules. Read more
Getting Started with Julia
July 25, 2025
This week the USDA announced the closure of the south building with the (potential) intention of distributing employees to other states. This action will inevitably cause a severe reduction in staffing meaning a lot of experienced economists will soon be unemployed. Out of respect for my colleagues, I am going to use this blog post to share resources for those who want to learn Julia and want to use it for economic modeling. I hope this will help those who are looking for new opportunities. Read more
Closed Economy Model with Monopoly in the X Sector
July 23, 2025
This example is a conversion of the Markusen M51 model into Julia. This is a standard two-good, two-factor, closed-economy general-equilibrium model. Activities are X, Y and W (welfare or utility). Factors of production are called unskilled and skilled labor. Unskilled labor is typically called L with a price of PW or just W, and skilled labor is called S with a price of PZ or just Z. PU is the price of a unit of utility (the value of the unit expenditure function). Read more
Understanding MPSGE: Interpreting solutions
July 18, 2025
The discussion during the MPSGE monthly this week got me thinking about an interesting example to demonstrate what the solutions from MPSGE mean. I thought it would be useful to share this example here, as it is a common question that comes up when working with MPSGE. Read more
The current WiNDC data processing pipeline
July 11, 2025
This week I would like to discuss how WiNDC takes our raw data and transforms it into the calibrated datasets used by our models. This is a multi-step process where the data passes through both Python and GAMS. Read more