Here's a selection of suggested reading for this month:
- Athey, S. & G. W. Imbens, 2019. Machine learning methods economists should know about. Mimeo.
- Bhagwat, P. & E. Marchand, 2019. On a proper Bayes but inadmissible estimator. American Statistician, online.
- Canals, C. & A. Canals, 2019. When is n large enough? Looking for the right sample size to estimate proportions. Journal of Statistical Computation and Simulation, 89, 1887-1898.
- Cavaliere, G. & A. Rahbek, 2019. A primer on bootstrap testing of hypotheses in time series models: With an application to double autoregressive models. Discussion Paper 19-03, Department of Economics, University of Copenhagen.
- Chudik, A. & G. Geogiardis, 2019. Estimation of impulse response functions when shocks are observed at a higher frequency than outcome variables. Globalization Institute Working Paper 356, Federal Reserve Bank of Dallas.
- Reschenhofer, E., 2019. Heteroscedasticity-robust estimation of autocorrelation. Communications in Statistics - Simulation and Computation, 48, 1251-1263.
Why not try directly reading a machine learning textbook or survey paper, instead of being told second-hand? Journal of Machine Learning Research is open source and is full of nice papers of the frontier of the field...
ReplyDeleteThe more you read the better! A lot of people (especially students) find some suggestions helpful, given the volume of material out there. Of course, ML and Econometrics aren't the same thing. The journal "Econometrics" is also open access (and no submission fees). And you can guess from its title what it publishes. Honoured to be on its Editorial Board. See: https://www.mdpi.com/journal/econometrics
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