Improved Forecasts from Imperfect Models

 Better the Devil You Know: Improved Forecasts from Imperfect Models

by Dong Hwan Oh and Andrew J. Patton

Cool paper from Oh and Patton.  https://www.federalreserve.gov/econres/feds/files/2021071pap.pdf

Interesting to me in part because, when I consider how I would approach their problem, I have some ideas that might work well, but my ideas differ from theirs (it seems).  So the paper made me think a lot, which is good.

Not unrelated, the paper introduced me to the stat literature on local likelihood, like:

Fan, J. Y. Wu and Y. Feng, 2009, Local quasi-likelihood with a parametric guide, Annals of Statistics, 37(6B), 4153-4183

         Fan, J., M. Farmen and I. Gijbels, 1998, Local maximum likelihood estimation and inference,                 Journal of the Royal Statistical Society, Series B, 60(3), 591-608

Hu, F. and J. V. Zidek, 2002, The weighted likelihood, Canadian Journal of Statistics, 30(3), 347-371

Tibshirani, R. and T. Hastie, 1987, Local likelihood estimation, Journal of the American Statistical Association, 82(398), 559-567,

which feeds into the paper's key econometrics ancestors like:

Dendramis, Y., G. Kapetanios and M. Marcellino, 2020, A similarity-based approach for macroeconomic forecasting, Journal of the Royal Statistical Society, Series A, 183(3), 801-827

Kristensen, D. and A. Mele, 2011, Adding and subtracting Black-Scholes: A new approach toapproximating derivative prices in continuous-time models, Journal of Financial Economics,102, 390-415.