Macro with R

A Practical Guide to Macroeconomic Data and Policy Analysis

Author

Charles Coverdale

Published

March 10, 2026

Preface

This book teaches you how to do macroeconomics with R.

It covers the full workflow of a macro practitioner: pulling data from central banks and statistical agencies, building publication-quality charts, and estimating the models that underpin economic policy — Phillips curves, Taylor rules, yield curve models, VARs, and nowcasting systems.

Who is this book for?

This book is for anyone who works with macroeconomic data and wants to do it in R. That includes:

Economics students (Masters and advanced undergraduate) who want to go beyond textbook theory and work with real data
Central bank and treasury analysts who use R for data pipelines, visualisation, and ad-hoc modelling
Think tank researchers and economic consultants who need reproducible, transparent analysis
Data journalists covering economics who want to automate their workflow
Finance professionals who need to understand the macro environment

What you need to know

This book assumes you are comfortable with R basics: installing packages, writing scripts, using dplyr and ggplot2. If you are new to R, start with R for Data Science by Hadley Wickham, Mine Cetinkaya-Rundel, and Garrett Grolemund.

You do not need a background in econometrics, though some familiarity with regression helps. Each chapter introduces the relevant theory before diving into code.

How this book is organised

Part I: Getting Data into R shows you how to pull macroeconomic data from the ONS, Bank of England, HMRC, OBR, ECB, and OECD — using purpose-built R packages that abstract away the complexity of each API.

Part II: Visualising the Economy covers the chart types that macro practitioners actually use: time series with recession shading, fan charts for uncertainty, yield curve plots, and interactive Shiny dashboards.

Part III: Macro Models in Practice walks through the workhorses of applied macroeconomics — estimating Phillips curves, Taylor rules, yield curve decompositions, vector autoregressions, and GDP nowcasting models — all with real data.

Part IV: Special Topics covers exchange rates and trade, climate and the macroeconomy, and cross-country growth analysis.

Software

All code in this book is written in R. The following packages are used throughout:

# Data access
install.packages(c("ons", "boe", "hmrc", "obr", "readecb", "readoecd", "inflateR"))

# Core tidyverse
install.packages(c("dplyr", "ggplot2", "tidyr", "readr", "lubridate"))

# Econometrics
install.packages(c("vars", "BVAR", "seasonal", "dfms"))

Acknowledgements

This book builds on the work of many people. Rob Hyndman’s Forecasting: Principles and Practice set the standard for free online R textbooks. Franz Mohr’s r-econometrics.com provides excellent VAR tutorials. Kevin Kotze’s time series course at the University of Cape Town inspired several chapters.

License

This book is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0.

# Preface {.unnumbered} This book teaches you how to do macroeconomics with R. It covers the full workflow of a macro practitioner: pulling data from central banks and statistical agencies, building publication-quality charts, and estimating the models that underpin economic policy — Phillips curves, Taylor rules, yield curve models, VARs, and nowcasting systems. ## Who is this book for? This book is for anyone who works with macroeconomic data and wants to do it in R. That includes: - **Economics students** (Masters and advanced undergraduate) who want to go beyond textbook theory and work with real data - **Central bank and treasury analysts** who use R for data pipelines, visualisation, and ad-hoc modelling - **Think tank researchers and economic consultants** who need reproducible, transparent analysis - **Data journalists** covering economics who want to automate their workflow - **Finance professionals** who need to understand the macro environment ## What you need to know This book assumes you are comfortable with R basics: installing packages, writing scripts, using `dplyr` and `ggplot2`. If you are new to R, start with [R for Data Science](https://r4ds.hadley.nz/) by Hadley Wickham, Mine Cetinkaya-Rundel, and Garrett Grolemund. You do not need a background in econometrics, though some familiarity with regression helps. Each chapter introduces the relevant theory before diving into code. ## How this book is organised **Part I: Getting Data into R** shows you how to pull macroeconomic data from the ONS, Bank of England, HMRC, OBR, ECB, and OECD — using purpose-built R packages that abstract away the complexity of each API. **Part II: Visualising the Economy** covers the chart types that macro practitioners actually use: time series with recession shading, fan charts for uncertainty, yield curve plots, and interactive Shiny dashboards. **Part III: Macro Models in Practice** walks through the workhorses of applied macroeconomics — estimating Phillips curves, Taylor rules, yield curve decompositions, vector autoregressions, and GDP nowcasting models — all with real data. **Part IV: Special Topics** covers exchange rates and trade, climate and the macroeconomy, and cross-country growth analysis. ## Software All code in this book is written in R. The following packages are used throughout: ```r # Data access install.packages(c("ons", "boe", "hmrc", "obr", "readecb", "readoecd", "inflateR")) # Core tidyverse install.packages(c("dplyr", "ggplot2", "tidyr", "readr", "lubridate")) # Econometrics install.packages(c("vars", "BVAR", "seasonal", "dfms")) ``` ## Acknowledgements This book builds on the work of many people. Rob Hyndman's [Forecasting: Principles and Practice](https://otexts.com/fpp3/) set the standard for free online R textbooks. Franz Mohr's [r-econometrics.com](https://www.r-econometrics.com/) provides excellent VAR tutorials. Kevin Kotze's [time series course](https://kevinkotze.github.io/ts-outline/) at the University of Cape Town inspired several chapters. ## License This book is licensed under [Creative Commons Attribution-NonCommercial-ShareAlike 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).