Principal component factor variable for the forecasts of GDP loss from climate change

This file presents a dataset for the principal component factor variable (see Rencher and Christensen 2012 for a full explanation of the methodology) for the GDP loss forecasts across several countries for the horizons 2030, 2050 and 2100. The first variable (fGDP_) is the first principal component factor obtained from 6 measures of GDP loss from previous studies in the literature: Burke et al. 2015, Kalkuhl and Wenz 2020: panel, Kalkuhl and Wenz 2020: cross-section, Kahn et al. 2021: RCP 2.6, Kahn et al. 2021: RCP 8.5, Roson and Sartori 2016. The second variable is a similar principal component factor variable obtained from the first 5 measures of GDP loss, therefore it excludes the Roson and Sartori 2016 measure. I also include the Stata codes necessary to replicate this empirical dataset from the original source datasets, with M_CC_analysis.do being the master file that call all the other codes in sequence to implement the analysis from beginning to end.

References:
Burke, M., S. Hsiang and E. Miguel (2015), "Global non-linear effect of temperature on economic production," Nature, 527, 235--239.
Kahn, M., K. Mohaddes, R. Ng, M. Hashem Pesaran, M. Raissi and J. Yang (2021), "Long-Term Macroeconomic Effects of Climate Change: A Cross-Country Analysis". Energy Economics, 104, 105624.
Kalkuhl, M. and L. Wenz (2020), "The impact of climate conditions on economic production. Evidence from a global panel of regions," Journal of Environmental Economics and Management, 103, 102360.
Rencher, A. and W. Christensen (2012), "Methods of Multivariate Analysis," 3rd ed., Wiley.
Roson, R. and M. Sartori (2016), "Estimation of Climate Change Damage Functions for 140 Regions in the GTAP 9 Database," Journal of Global Economic Analysis, 1(2), 78-115.