This year, I’ll start with the forecasts, then describe the process. First, though, a couple of things to bear in mind as you look at the map and dot plot:
- Coup attempts rarely occur, so the predicted probabilities are all on the low side, and most are approximately zero. The fact that a country shows up in dark red on the map or ranks high on an ordered list does not mean that we should anticipate a coup occurring there. It just means that country is at relatively high risk compared to the rest of the world. Statistically speaking, the safest bet for any country almost any year is that a coup attempt won’t occur. The point of this exercise is to try to get a better handle on where the few coup attempts we can expect to see this year are most likely to happen.
- These forecasts are estimates based on noisy data, so they are highly imprecise, and small differences are not terribly meaningful. The fact that one country lands a few notches higher or lower than other on an ordered list does not imply a significant difference in risk.
Okay, now the the forecasts. First, the heat-map version, which sorts the world into fifths. From cross-validation in the historical data, we can expect nearly 80 percent of the countries with coup attempts this year to be somewhere in that top fifth. So, if there are four countries with coup attempts in 2014, three of them are probably in dark red on that map, and the other one is probably dark orange.
Now, a dot plot of the Top 40, which is a slightly larger set than the top fifth in the heat map. Here, the gray dots show the forecasts from the two component models (see below), while the red dots are the unweighted average of those two—what I consider the single-best forecast.
A lot of food for thought in there, but I’m going to leave interpretation of these results to future posts and to you.
Now, on the process: As statistical forecasters are wont to do, I have tinkered with the models again this year. As I said in a blogged research note a couple of weeks ago, this year’s tinkering was driven by a combination of data practicalities and the usual sense of, “Hey, let’s see if we can do a little better this time.”Predictably, though, I also ended up doing things a little different than I’d expected in December. Specifically:
- I trained and validated the models on an amalgamation of two coup data sets—as described in a November post that showed an animated map of coup attempts worldwide since 1946—instead of just using the Powell and Thyne list. So that map and the bar plots with it should give you a clearer sense of what these forecasts are (and aren’t) trying to anticipate.
- After waiting for Freedom House to update its Freedom in the World data, which it did a few days ago, I decided to go back to using Polity after all because the forecasts based on it were noticeably more accurate in cross-validation. The models include a categorical measure of regime type based on the Polity scale and a “clock” counting years since the last significant change in that score. I hard-coded updates to those measures, which are much coarser (and therefore easier to update) than the Polity scale or its component variables.
- As with coup events, I used an amalgamation of GDP growth data from the World Bank and IMF instead of picking one. I also went back to summarizing this feature in the models with a binary indicator for slow growth of less than 2 percent (annual, per capita).
- Finally, I did not include GDELT summaries in the models because they only slightly improved forecast accuracy, and they did not cover a country of great interest to me (South Sudan). The latter is surely a fixable glitch, but it’s not fixed now, and I really wanted to have a forecast for that particular country in this year’s list for reasons that should now be evident from the results. On the accuracy part, I should note that I’ve only done a little bit of checking, and there are still plenty of ways to try to squeeze more forecasting power out of those data, not the least of them being to build more dynamic models that use monthly instead of annual summaries.
The forecasts are an unweighted average of predicted probabilities from a logistic regression model and a Random Forest that use more or less the same inputs. Both models were trained on data covering the period 1960-2010; applied to data from 2011 to 2013 to assess their predictive performance; and then applied to the newest data to generate forecasts for 2014. Variable selection was based mostly on my prior experience working this problem. As noted above, I did a little bit of model checking—using stratified 10-fold cross-validation—to make sure the process worked reasonably well, and to help choose between some different measures for the same concept. In that cross-validation, the unweighted average got good but not great accuracy scores, with an area under the ROC curve in the low 0.80s. Here are the variables used in the models:
- Geographic Region. Per the U.S. Department of State (and only in the Random Forest).
- Last Colonizer. Indicators for former French, British, and Spanish colonies.
- Country Age. Years since independence, logged.
- Post-Cold War Period. Indicator marking country-years since 1991, when coup activity has generally slowed.
- Infant Mortality Rate. Relative to the annual global median, logged, and courtesy of the U.S. Census Bureau. The latest version ends in 2012, so I’ve simply pulled those values forward a year here.
- Political Regime Type. Four-way categorization based on the Polity scale into autocracies, “anocracies,” democracies, and transitional, collapsed, or occupied cases.
- Political Stability. Count of years since a significant change in the Polity scale, logged.
- Political Salience of Elite Ethnicity. Yes or no, per a data set on elite characteristics produced by the Center for Systemic Peace (CSP) for the Political Instability Task Force (PITF), with hard-coded updates for 2013 (no changes). This one is not posted on CSP’s data page and was obtained from PITF and shared with their permission.
- Violent Civil Conflict. Yes or no, per CSP’s Major Episodes of Political Violence data set (here), with hard-coded updates for 2013 (a few changes).
- Election Year. Yes-or-no indicator for any national elections—executive, legislative, or constituent assembly—courtesy of the NELDA project, with hard-coded updates for 2012 through 2014 (scheduled).
- Slow Economic Growth. Yes-or-no indicator for less than 2 percent, as described above.
- Domestic Coup Activity. Yes-or-no indicator for countries with any attempts in the past 5 years, successful or failed.
- Regional Coup Activity. A count of other countries in the same region with any coup attempts the previous year, logged.
- Global Coup Activity. Same as the previous tic, but for the whole world.
All of the predictors are lagged one year except for region, last colonizer, country age, post-Cold War period, and the election-year indicator. The fact that a variable appears on this list does not necessarily mean that it has a significant effect on the risk of any coup attempts. As I said earlier, I drew up a roster of variables to include based on a sense of what might matter (a.k.a., theory) and past experience and did not try to do much winnowing.
If you are interested in exploring the results in more detail or just trying to do this better, you can replicate my analysis using code I’ve put on GitHub (here). The posted script includes a Google Drive link with the requisite data. If you tinker and find something useful, I only ask that you return the favor and let me know. [N.B. As its name implies, the generation of a Random Forest is partially stochastic, so the results will vary slightly each time the process is repeated. If you run the posted script on the posted data, you can expect to see some small differences in the final estimates. I think these small differences are actually a nice representation of the forecasts’ inherent uncertainty, so I have not attempted to eliminate it by, for example, setting the random number seed within the R script.]
UPDATE: In response to a comment, I tried to produce another version of the heat map that more clearly differentiates the quantiles and better reflects the fact that the predicted probabilities for cases outside the top two fifths are all pretty close to zero. The result is shown below. Here, the differences in the shades of gray represent differences in the average predicted probabilities across the five tiers. You can decide if it’s clearer or not.