Economic evaluation is data intensive. What are the data requirements and when should you think about them? I've written a guide to help set up the data and information that you'll need to apply economic evaluation to social interventions.
Some quick notes of a few basic things I've learned about time-series forecasting with AutoGluon
Taking a look at the accuracy of forecasts produced by the AutoGluon package from Amazon.
Building some simple infrastructure to store and visualise public time-series data
Testing if large language models can be used to verbalise small datasets.
Traps for the unwary when using dplyr to filter out rows based on logical conditions.
Various silly measures of how much rain fell on Auckland in 2023.
A look at recent trends in retail sales volumes.
Some things I learned while trying out R in VS Code.
Taking a look at the impacts of internal and international migration on NZ population.
A statistical reason to celebrate getting older.
A practical guide to using automated sentiment analysis, topic detection, and summarisation in research and evaluation projects.
Mapping changes in Auckland's population aged 20 to 34.
Improving data analysis by using natural language processing.
Some data visualisations to explore producer input and output price index data.
A few more tweaks for using R in VS Code.
R code with RSelenium to help automate downloads of time-series data from StatsNZ's Infoshare website.
Experimenting with how to show the uncertainty associated with an economic forecast.
