More Slides: Forecast Evaluation, DSGE Modeling, and Connectedness

The last post (slides from a recent conference discussion) reminded me of some slide decks that go along with some forthcoming papers.  I hope they're useful.

Diebold, F.X. and Shin, M. (in press), "Assessing Point Forecast Accuracy by Stochastic Error Distance," Econometric Reviews.  Slides here.

Diebold, F.X., Schorfheide, F. and Shin, M. (in press), "Real-Time Forecast Evaluation of DSGE Models with Stochastic Volatility," Journal of Econometrics.  Slides here.

Demirer, M., Diebold, F.X., Liu, L. and Yilmaz, K. (in press), "Estimating Global Bank Network Connectedness", Journal of Applied Econometrics.  Slides here.

Slides from SoFiE NYU Discussion

Here are the slides from my pre-conference discussion of Yang Liu's interesting paper, "Government Debt and Risk Premia", at the NYU SoFiE meeting. The key will be to see whether his result (that debt/GDP is a key driver of the equity premium) remains when he controls for expected future real activity. (See Campbell and Diebold, "Stock Returns and Expected Business Conditions: Half a Century of Direct Evidence," Journal of Business and Economic Statistics, 27, 266-278, 2009.)

Structural Change and Big Data

Recall the tall-wide-dense (T, K, m) Big Data taxonomy.  One might naively assert that tall data (big time dimension, T) are not really a part of the Big Data phenomenon, insofar as T has not started growing more quickly in recent years.  But a more sophisticated perspective on the "size" of T is whether it is big enough to make structural change a potentially serious concern.  And structural change is a serious concern, routinely, in time-series econometrics.  Hence structural change, in a sense, produces Big Data through the T channel.