Regression Discontinuity and Event Studies in Time Series

Check out the new paper, "Regression Discontinuity in Time [RDiT]: Considerations for Empirical Applications", by Catherine Hausman and David S. Rapson.  (NBER Working Paper No. 23602, July 2017.  Ungated copy here.)

It's interesting in part because it documents and contributes to the largely cross-section regression discontinuity design literature's awakening to time series. But the elephant in the room is the large time-series "event study" (ES) literature, mentioned but not emphasized by Hausman and Rapson.  [In a one-sentence nutshell, here's how an ES works: model the pre-event period, use the fitted pre-event model to predict the post-event period, and ascribe any systematic forecast error to the causal impact of the event.]  ES's trace to the classic Fama et al. (1969).  Among many others, MacKinlay's 1997 overview is still fresh, and Gürkaynak and Wright (2013) provide additional perspective.

One question is what the RDiT approach adds to the ES approach, and related, what it adds to well-developed time-series toolkit of other methods for assessing structural change. At present, and notwithstanding the Hausman-Rapson paper, my view is "little or nothing".  Indeed in most respects it would seem that a RDiT study *is* an ES, and conversely.  So call it what you will, "ES" or "RDiT". 

But there are important open issues in ES / RDiT, and Hausman-Rapson correctly emphasize one of them, namely issues and difficulties associated with "wide" pre- and post-event windows, which is often the relevant case in time series.

Things are generally "easy" in cross sections, where we can usually take narrow windows (e.g., in the classic scholarship exam example, we use only test scores very close to the scholarship threshold).  Things are similarly "easy" in time series *IF* we can take similarly narrow windows (e.g., high-frequency asset return data facilitate taking narrow pre- and post-event windows in financial applications).  In such cases it's comparatively easy to credibly ascribe a post-event break to the causal impact of the event.

But in other time-series areas like macro and environmental, we might want (or need) to use wide pre- and post-event windows.  Then the trick becomes modeling the pre- and post-event periods successfully enough so that we can credibly assert that any structural change is due exclusively to the event -- very challenging, but not hopeless.

Hats off to Hausman and Rapson for beginning to bridge the ES and regression discontinuity literatures, and for implicitly helping to push the ES literature forward.

Time-Series Regression Discontinuity

I'll have something to say in next week's post.  Meanwhile check out the interesting new paper, "Regression Discontinuity in Time: Considerations for Empirical Applications", by Catherine Hausman and David S. Rapson, NBER Working Paper No. 23602, July 2017.  (Ungated version here.)

A Fascinating Event Study

I just read an absolutely fascinating event study, "The Power of the Street: Evidence from Egypt’s Arab Spring," by Daron Acemoglu, Tarek Hassan and Ahmed Tahoun (DHT). The paper is here. I'm looking forward to seeing the seminar later today.

[Abstract: During Egypt’s Arab Spring, unprecedented popular mobilization and protests brought down Hosni Mubarak’s government and ushered in an era of competition between three groups: elites associated with Mubarak’s National Democratic Party, the military, and the Islamist Muslim Brotherhood. Street protests continued to play an important role during this power struggle. We show that these protests are associated with differential stock market returns for firms connected to the three groups. Using daily variation in the number of protesters, we document that more intense protests in Tahrir Square are associated with lower stock market valuations for firms connected to the group currently in power relative to non-connected firms, but have no impact on the relative valuations of firms connected to other powerful groups. We further show that activity on social media may have played an important role in mobilizing protesters, but had no direct effect on relative valuations. According to our preferred interpretation, these events provide evidence that, under weak institutions, popular mobilization and protests have a role in restricting the ability of connected firms to capture excess rents.]

When first reading DHT, I thought the authors might be unaware of the large finance literature on event studies, since they don't cite any of it. Upon closer reading, however, I see that they repeatedly use the term "standard event study," indicating awareness coupled with a view that the methodology is now so well known as to render a citation unnecessary, along the lines of "no need to cite Student every time you report a t-statistic."

Well, perhaps, although I'm certain that most economists, even thoroughly empirical economists -- indeed even econometricians! -- have no idea what an event study is.

Anyway, here's a bit of background for those who want some.

DHT-style event studies originated in finance. The idea is to fit a benchmark return model to pre-event data, and then to examine cumulative "abnormal" returns (assessed using the model fitted pre-event) in a suitable post-event window. Large abnormal returns indicate a large causal impact of the event under study. The idea is brilliant in its simplicity and power.

Like so many things in empirical finance, event studies trace to Gene Fama (in this case, with several other luminaries):

The adjustment of stock prices to new information

EF Fama, L Fisher, MC Jensen, R Roll - International economic review, 1969 - JSTOR

THERE IS an impressive body of empirical evidence which indicates that successive price

changes in individual common stocks are very nearly independent. 2 Recent papers by

Mandelbrot [11] and Samuelson [16] show rigorously that independence of successive ...

Cited by 4223 Related articles All 20 versions Cite Save More

For surveys, see:

The econometrics of event studies

SP Kothari, JB Warner - Available at SSRN 608601, 2004 - papers.ssrn.com,

Abstract: The number of published event studies exceeds 500, and the literature continues 

to grow. We provide an overview of event study methods. Short-horizon methods are quite
reliable. While long-horizon methods have improved, serious limitations remain. A ...

Cited by 824 Related articles All 14 versions Cite Save More


Event studies in economics and finance
AC MacKinlay - Journal of economic literature, 1997
ECONOMISTS are frequently asked to measure the effects of an economic event on the value of firms. On the surface this seems like a difficult task, but a measure can be constructed easily using an event study. Using financial market data, an event study ...Cited by 3290 Related articles All 27 versions Web of Science: 499 Cite Save More

(Not much has changed since 2004, or for that matter, since 1997.)