Lecturer:
Emanuele Massetti, Georgia Institute of Technology, CESIfo, CMCC
“Learning from Climate Big Data: the Case of Climate Impacts on US Agriculture” (pdf file)
Abstract:
This seminar is based on a paper that applies “statistical learning” methods to estimate how climate affects US agriculture using the largest dataset of climate variables ever assembled in the literature. Preliminary results using OLS regression suggest that temperature and precipitation coefficients may be biased by other omitted climate variables. Lasso regression is used for model selection and lead to insignificant temperature variables but precipitation coefficients appear to be robust. Lasso regression reduces out-of-sample average and standard deviation of the root mean squared error.
Working Language: English
Venice, h.12.30 pm, Aula Radice, ground floor, Edificio Porta dell’Innovazione – VEGA, Via della Libertà 12, Venezia Marghera - Aula Radice, ground floor, Edificio Porta dell’Innovazione – VEGA, Via della Libertà 12, Venezia Marghera, Venice, h.12.30 pm -
12 Jun 2018
Contacts Organized by
- CMCC - Centro Euro-Mediterraneo sui Cambiamenti Climatici