Tom Pepinsky

Author: tompepinsky

  • Regional Implications of the Russian Currency Crisis

    Unless something changes dramatically in the next couple of days, we will code this week as the onset of the 2014-15 Russian currency crisis. Using Quantmod, I’ve plotted below the evolution of the ruble-dollar exchange rate since 2010 along with 180-day Bollinger bands.

    The Indonesians have a verb for this kind of currency movement: anjlok, which my Echols and Shadily dictionary defines as “to plummet,” or figuratively, to “jump the rails.”

    One interpretation of the unfolding currency crisis is that this is bad news for Putin, and hence good news for US/Western foreign policy. Here are two reasons to be circumspect, at least at this stage.

    Recent History

    First, the historical context. Let’s look back another decade, shall we?

    Russia managed to ride out the 2008-09 Global Financial Crisis pretty well. One might look to this as evidence that Putin has the “room to move” to withstand an external shock of this sort, although I’d add two caveats to this view. For one, the ruble depreciation of 2008-09 looks a lot like a return to trend than a departure from trend (as it does now). But also, and the 2008-09 depreciation is probably a consequence of a flight to liquidity during the Global Financial Crisis, which is not true today.

    Regional Context

    A second reason to be skeptical is the external implications of a Russian ruble crisis. Let’s also look at XR movements in the Ukraine over the same time period.

    Just eyeballing this, it looks like the same pressures affecting Russia will be affecting the Ukraine too, which would mean that any harm that comes to the Russian economy would also spill over to its neighbors too, including those neighbors that are seen as friends of the U.S.

    Fortunately, we know that we should not just eyeball our time series, because non-stationary time series can be misleading. I’ve plotted below the daily changes in hryvnia/USD (blue) and ruble/USD exchange rates (red).

    It’s not so clear that they are related. We can also look at a scatterplot of day-on-day changes.

    There is only the slightest positive relationship here between hryvnia-USD movements and ruble-USD movements.

    So why write about this at all? Because it is also possible to show you this figure, from a time in which Ukrainian-Russian ties were not marred by invasions and things.

    The t-stat on the line of best fit is 5.6, and remains unchanged when controlling for lagged levels of each country’s exchange rate.*** This reflects the fact that during normal times, we expect there to be spillover effects from the Russian economy to the Ukrainian economy. (This figure is all the more striking given that the Ukraine was maintaining a quasi-fixed exchange rate during this period.) The economies are really tightly integrated: Russia is the Ukraine’s largest trading partner, after all.

    The takeaway thought is that currency crises have external consequences. Russia’s will too, and they might lead analysts to be careful of what they wish for, even as many of them happily watch the markets hammer Russia.

    Note

    *** Of course, I’d be curious to see a full ARIMAX/VECM model, and will post the R code to get all the data together in the first comment on this post.

  • DAGs, Horserace Regressions, and Paradigm Wars

    Thanks to the PolMeth listserv, I came across a new paper by Luke Keele and Randy Stevenson that criticizes the causal interpretation of control variables in multiple regression analyses. It’s a really simple argument, really: using directed acyclic graphs (DAG) to interpret the causal structures that underlie multiple regression analyses, they show that there are many situations in which control variables X can help to identify the causal effect of D on Y, but the causal effects of X are not actually identified.

    Here are two implications of their argument that they did not explore, but which are worth considering. One for applied work in general, and another for International Relations in particular.

    1. The fact that conditioning on alternative causal pathways to achieve identification in D usually does not allow for causal interpretations of pathways in X is one more argument against horserace regressions: testing competing theories by putting two or more independent variables D1 and D2 in a regression and then comparing coefficients and t-statistics. (These are different from garbage can regressions, in which a bunch of Xs are dumped into the regression garbage can in order to achieve identification for D.) The complications of attempting to identify D by conditioning on X only expand when attempting to identify both D1 and D2 by conditioning on X or set of Xs. It could be the case, for example, that D2 is identified only by conditioning on D1 and X1, but that D1 remains unidentified without conditioning on X2, and that conditioning on X2 prevents identification of D1—the problem of “conditioning on a collider“. (Someone else can draw that DAG for me.)
    2. An implication of the previous point has particular relevance for the so-called “Paradigm Wars” in International Relations, what David Lake and others often refer to as the third “Great Debate” in International Relations. There was a time in which quantitative research sought to sort out among paradigms by testing realism, liberalism, and constructivism “against one another” in multiple regression-type analyses. Lately such efforts have become rarer, at least in my read of the literature. Keele and Stevenson’s paper is a reminder that this is a really good thing. To the extent that paradigm wars continue to simmer, they do so at the epistemological or theoretical level, and that is appropriate given the limits of regression-style quantitative research for identifying the effects of multiple causal variables at once.