So, inspired by Brian and the general spirit of end-of-year reflection, some thoughts on what I’ve read this year.
According to my reference manager software, I’ve read 183 papers this year, which is somewhat overstated because many were read last year but are dated incorrectly, and a substantial portion of the list contains slides, lecture notes, or other documents not quite meriting the status of article. I think as I’ve progressed through grad school I find it much easier to get through technical material than I once did, though some of the volume reflects a period in the spring when I was reading quite a bit to find a new research topic.
In, apparently, reverse alphabetical order, a list of papers I enjoyed this year, making no claim of endorsement of conclusions, just ones I enjoyed reading or from which I felt I learned a lot. A star indicates papers read directly for some sort of work.
Sanz-Solé, Marta (2008) Applications of Malliavin Calculus to Stochastic Partial Differential Equations
Rakhlin, Alexander & Karthik Sridharan (2015) Hierarchies of Relaxations for Online Prediction Problems with Evolving Constraints
Paul, Arnab & Suresh Venkatasubramanian (2015) Why Does Deep Learning Work? - A Perspective From Group Theory
Mukherjee, Sayan & John Steenbergen (2013) Random Walks on Simplicial Complexes and Harmonics
Mohammed, Saleh-Eldin, Tusheng Zhang, & Huaizhong Zhao (2008) The Stable Manifold Theorem for Semilinear Stochastic Evolution Equations and Stochastic Partial Differential Equations*
Méléard, Sylvie (1996) Asymptotic Behaviour of some interacting particle systems; McKean-Vlasov and Boltzmann models*
Kadri, Hachem et. al. (2015) Operator-valued Kernels for Learning from Functional Response Data
Jakab, Zoltan & Michael Kumhof (2015) Banks are not intermediaries of loanable funds — and why this matters
Itô, Kiyosi (1983) Distribution-valued processes arising from independent Brownian motions*
Hairer, Martin (2014) Introduction to regularity structures
Hahn, Jinyong, Guido Kuersteiner, Maurizio Mazzocco (2015) Estimation with Aggregate Shocks
Guéant, Olivier, Jean-michel Lasry, & Pierre-Louis Lions (2011) Mean field games and applications*
Gao, Tingran (2015) Hypoelliptic diffusion maps I: tangent bundles
Florens, Jean-Pierre & Sébastien Van Bellegem (2014) Instrumental variable estimation in functional linear models
Faust, Jon, & Eric M. Leeper (2015) The Myth of Normal: The Bumpy Story of Inflation and Monetary Policy
Desmet, Klaus, Dávid Krisztián Nagy, & Esteban Rossi-Hansberg (2015) The Geography of Development: Evaluating Migration Restrictions and Coastal Flooding
Creal, Drew (2009) A survey of sequential Monte Carlo methods for economics and finance
Cohen, Albert, Marc Hoffmann, & Markus Reiss (2004) Adaptive Wavelet Galerkin Methods for Linear Inverse Problems
Chen, Xiaohong & Timothy Christensen (2015) Optimal uniform convergence rates and asymptotic normality for series estimators under weak dependence and weak conditions I*
Beylkin, G., Coifman, Ronald & Vladimir Rokhlin (1991) Fast Wavelet Transforms and Numerical Algorithms I*
Belloni, Alexandre, Victor Chernozhukov, & Kengo Kato (2014) Uniform post-selection inference for least absolute deviation regression and other Z-estimation problems
This list is by no means representative of my reading for the year, covering substantially less on topics where I read a lot due to research, especially on functional data analysis, and probably more on the mathematics than the economics side, I suppose because those results are more novel to me. The selection of papers is largely by whim and happenstance: I would do well to read more systematically, especially classic journal articles, rather than whatever recent work catches my eye. I could say a bit about why I liked each of these papers, but maybe better to keep this short, so just ask if you’re interested.
I will say that the standout, read in March and a clear winner for best paper I read this year, is Rakhlin and Sridharan on nonstationary prediction on graphs. It extends their essential earlier work on regret-based statistical learning theory for nonstationary forecasting to learning about processes on graphs, bringing together results from a host of areas to elucidate the relationship between learning, structure, and computation. The work that Rakhlin and Sridharan are doing isn’t directly related to what I do day to day, but I think in the long run it stands a chance of completely changing the way we go about learning from and using data, so it’s very much worth keeping track of.