Yes, I know it has been far too long since a serious post.  But maybe I'm finally getting my head above water.   In any event here is serious Philly coffee advice: ***The Nook***, 15 South 20th. He roasts; she bakes. Glorious shrine to seriously sourced and roasted coffee, and meticulous and loving baking. So fine. Alas, no tables, but so what? Best coffee in Philly, hands down, as of 2/2023. PLEASE, pay them a visit. For more see https://www.sas.upenn.edu/~fdiebold/PhillyCoffee.htm ..read more

 Just eight co-authorship steps to Gauss! Small world indeed. And the route backward is not too shabby…  --> Marc Nerlove --> Kenneth Arrow --> David Blackwell --> Richard Bellman --> Ernst Straus --> Albert Einstein --> Hermann Minkowski --> Carl Friedrich Gauss ..read more

It strikes me that something may be wrong with me.   In a new paper in progress I wanted to cite the famous and beautiful Sims, Stock and Watson (1990). I found the bibtex on Jim Stock's Harvard site. Fine. Then I noticed that it listed the authors as Stock, Sims, and Watson. OK, fine, I changed it to the correct alphabetical order of Sims, Stock and Watson. (Probably just Jim's administrative assistant aggrandizing on his behalf.) Anyway I also noticed that the bibtex omitted middle initials, just giving C. Sims, J. Stock, and M. Watson. The amazing thing, and why something may be w ..read more

Last week I received the full published special issue of Journal of Econometrics, 231(2), 2022 (The Econometrics of Macroeconomic and Financial Data). I am deeply grateful and humbled. What a wonderful gesture. Heartfelt thanks to the J. Econometrics Editorial Board, and to all the students, co-authors, and colleagues who contributed. Special thanks to Atsushi Inoue, Lutz Kilian and Andrew Patton for their thoughtful introduction and meticulous editing, and for so generously attempting (twice) to host the associated 60th birthday conference. Clearly COVID did not defeat us ..read more

Fascinating color on sea level rise in Jakarta, and good insight into the moral hazard associated with certain types of adaptation.   https://allanhsiao.github.io/files/Hsiao_jakarta.pdf https://allanhsiao.github.io/ Abstract:  Sea level rise poses an existential threat to Jakarta, which faces frequent and worsening flooding. The government has responded with a proposed sea wall. In this setting, I study how government intervention complicates long-run adaptation to climate change. I show that government intervention creates coastal moral hazard, and I quantify this fo ..read more

 Of course machine learning (ML) is everywhere now.  The time-series analysis perspective has been the same for decades (parsimonious predictive modeling allowing for misspecification; out-of-sample evaluation; ensemble averaging; etc.), and there are many areas of overlap even if there are also many differences. It's interesting to see ML emerging as particularly useful in central banking contexts.  The Federal Reserve Bank of Philadelphia, for example, now explicitly recruits and hires "Machine Learning Economists".  Presently they have three, and they're looking for a fo ..read more

Check this out.  The implicit stochastic-gradient update seems very appealing relative to the "standard" GAS/DCS explicit update. "Robust Observation-Driven Models Using Proximal-Parameter Updates", by Rutger-Jan Lange, Bram van Os, and Dick van Dijk. https://www.tinbergen.nl/discussion-paper/6188/22-066-iii-robust-observation-driven-models-using-proximal-parameter-updates ..read more

 Check this out, by Barigozzi, Cavaliere, and Moramarco: http://d.repec.org/n?u=RePEc:arx:papers:2208.02925&r= Very cool methods for dynamic "multilayer networks".  In a standard N-dim net there's one NxN adjacency matrix.  But richer nets may have many kinds of connections, each governed by its own adjacency matrix.  (What a great insight -- so natural and obvious once you hear it.  A nice "ah-ha moment"!)  So perhaps there are K operative NxN adjacency matrices.  Then there is actually a grand 3-dim adjacency matrix (NxNxK) operative -- a cubic rather t ..read more

Ted is among the very greatest statisticians/econometricians of the 20th-century.  I feel very close to him, as my former Penn colleague, Larry Klein, worked closely with him at Cowles in the 1940s, and another former colleague, Bobby Mariano, was his student at Stanford before coming to Penn around 1970.  I recall a Penn seminar he gave late in his career, on unit moving-average roots.  He started painfully slowly, defining, for example, things like "time series" and "covariance stationarity".  Some eyes were rolling.  Ten minutes later, he was far beyond the frontier ..read more

This just blows me away.  So full of great insight.  Equal-weight combinations rule, in yet another context!  See also my papers with Minchul Shin that clearly lead to equal weights for point and density forecasts, respectively: Diebold, F.X. and Shin, M. (2019), "Machine Learning for Regularized Survey Forecast Combination: Partially-Egalitarian Lasso and its Derivatives," International Journal of Forecasting, 35, 1679-1691.  Diebold, F.X., Shin, M. and Zhang, B. (2022), “On the Aggregation of Probability Assessments: Regularized Mixtures of Predictive Densities for E ..read more