GeoRisk Data Lab 1
This interactive lab teaches you Local Projections (Jordà 2005) using real Geopolitical Risk data (Caldara & Iacoviello 2022). You will explore the GPR Index, understand the LP estimator, interpret impulse response functions, and produce an analysis report—all in the browser.
Caldara & Iacoviello (2022): Measuring Geopolitical Risk
Published in the American Economic Review 112(4), pp. 1194–1225, this paper constructs a monthly index of geopolitical risk (GPR) by counting the share of newspaper articles in major outlets that discuss geopolitical threats and events. The index covers 1900 to the present and decomposes into two sub-indices:
Articles mentioning risks of war, terrorism, geopolitical tensions—capturing ex ante uncertainty about future adverse events.
Articles reporting actual geopolitical events: military actions, terrorist attacks, escalations—measuring realized geopolitical shocks.
The key finding: an increase in the GPR index causes a decline in investment, employment, and stock market returns, while raising oil prices and capital flows toward safe-haven economies. The effect is economically significant and robust across identification strategies.
Local Projections (Jordà 2005)
Introduced in Econometrica 73(1), pp. 161–182, Local Projections (LPs) offer a flexible, robust alternative to Vector Autoregressions (VARs) for estimating impulse response functions (IRFs). The core idea is remarkably simple:
The sequence of coefficients {β0, β1, …, βH} traces out the impulse response function. At each horizon h, you run a different OLS regression where the dependent variable is y shifted forward by h periods. The shock variable (GPRt) stays at date t.
VARs impose the same dynamic structure at every horizon. LPs are robust to misspecification of the lag structure, naturally handle nonlinearities, and provide direct standard errors.
Typically include lags of the dependent variable, lags of GPR, and other macro controls (interest rates, oil prices, VIX) to isolate the causal effect of the geopolitical shock.
Standard errors must be corrected for serial correlation in the residuals (Newey-West HAC), since the projection residuals at horizon h > 0 are mechanically autocorrelated.
The GPR index captures exogenous variation in geopolitical risk because it is constructed from newspaper text, not from economic data. This provides a narrative identification strategy.
- Jordà, Ò. (2005). “Estimation and Inference of Impulse Responses by Local Projections.” American Economic Review, 95(1), 161–182.
- Caldara, D. & Iacoviello, M. (2022). “Measuring Geopolitical Risk.” American Economic Review, 112(4), 1194–1225.
- Bloom, N. (2009). “The Impact of Uncertainty Shocks.” Econometrica, 77(3), 623–685.
- Ramey, V.A. (2016). “Macroeconomic Shocks and Their Propagation.” Handbook of Macroeconomics, Vol. 2.
GPR Index: January 2000 – December 2024
The chart below plots the monthly Geopolitical Risk Index (global) alongside its two components: Threats and Acts. Toggle the series to compare them. Click on any annotated event marker to see details about the geopolitical spike.
Observations. The GPR index captures distinct geopolitical regimes. The post-9/11 period (2001–2003) shows persistently elevated threat levels. The 2022 spike following Russia’s invasion of Ukraine is the largest since the Iraq War. Notice how Threats and Acts often diverge: a buildup of threats does not always culminate in acts (e.g., North Korea 2017), while some events arrive with little warning in the threat index.
Summary Statistics & Distribution
Impulse Response Functions: GPR → Financial Variables
Below are pre-computed LP estimates of the effect of a one-standard-deviation GPR shock on four financial variables, at horizons h = 0 to 12 months. Select a dependent variable to display its IRF with 68% and 90% confidence bands (Newey-West HAC standard errors). Controls include 4 lags of the dependent variable, 4 lags of GPR, the federal funds rate, and the log of oil prices.
Interpret the Impulse Responses
Answer the following questions based on the IRFs displayed above. Your score is tracked in the status panel.
What We Learned
Using Local Projections on monthly data (2000–2024), we estimated the dynamic causal effect of geopolitical risk shocks on four financial variables. The results are broadly consistent with Caldara & Iacoviello (2022):
Lab Score
Complete all phases and quiz questions to receive your assessment.
Markdown Report
Click the button below to generate a Markdown report summarizing your lab session. You can copy it to your clipboard or download it as a file.
R Script
The following R script replicates the Local Projection exercise using the lpirfs package and actual GPR data. Download it and run it in RStudio.