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Yes I have. The underlying dataset is also freely available [1]. And all the points you raise have of course been considered and discussed, in the paper or in t

• mike_hearn 2y ago
  Good that you've read it, but I'm afraid the entire idea of this analysis just seems absurd to me. It's exactly the kind of thing that creates the replication crisis, which outside of the experimental sciences is driven by a morass of P=0.049 modeling claims that disappear when re-examined. Many such papers are well cited, are claimed to use "robust" methods and so on. Yet when the claims fail to generalize, nothing happens. There's no postmortem or other institutional attempt to learn anything, no period of self reflection or adjustment of methods. Academia unfortunately no longer has credibility when it comes to this type of statistical analysis. Maybe they address all of these obvious problems in their paper, but I doubt it: 1. The low quality and wide CIs of the temperature datasets themselves. Look at the recent revelations about the CIs on the UK's weather data for an example (and the total lack of care from the Met Office about fixing the situation). It's one country but similar problems are found everywhere. One of the most depressing conclusions I reached when studying this topic a few years ago was that we don't really know if the world is getting warmer or not. It's not even a well posed question to begin with, and the datasets are subject to massive historical revisions, but even ignoring those fatal problems the error bars on the underlying data are wider than the claimed increases. 2. Dubious nature of the gridded GDP dataset they're using. Much of the data comes from sources merely cited as "literature". Of those, many are themselves estimated and modeled data dubious on their face, e.g. the Australia data comes from a paper with only two citations (one of which is this paper) and which claims to accurately reconstruct GDP back to the 1850s! This is a jenga tower of estimates and there doesn't seem to be any attempt to measure or think about error in a systematic way: they explain that they looked for outliers in the data to do manual validation (unmentioned, did they remove outliers?), and they admit that the dataset has serious problems, but just blithely hand wave it all away. There is no attempt to estimate the accuracy of their data beyond observing it isn't 100% accurate. 3. Inability to validate the resulting model ("robustness tests" aren't the same thing as validation, at all). Unvalidated models aren't useful for anything. As for Nordhaus, I wouldn't try to outsource decisions about the reliability of work to old names. They've been pointing out serious problems in the literature for years, some of them decades, and there have never been reforms. If someone warns of problems 100 times and is ignored every time, the lack of a warning the 101st time doesn't mean anything. They could just be tired. I would be, in their shoes!
  • Certhas 2y ago
    With all due respect, it does not look like you have looked at any of this earnestly. First of all, the results are not marginally significant. In every variant of the analysis, the results are significant at the p < 0.001 level. This is not modelling, this is data analysis. "Validated" doesn't really make sense in this context. Methodologically this is bog standard econometrics. The quality of the dataset is as good as any used in economics, all you have done is cited their own transparent disclosure of the limitations. You can now say that we should never look at any economic data to make any analysis, but that would be a rather extreme position to take. Much much worse papers have had disproportionate policy impacts [1]. These papers are taken very seriously by every major relevant institution. If the data is low quality, and you still obtain a strong signal, that shows the result is robust, unless the data errors can be expected to be correlated with the drivers. And this is where this analysis is a lot simpler and more robust than standard econometrics: The economy doesn't influence the weather. Common drivers are conceivable, but the panel regression controls for both regional and temporal background rates. At this point it is very difficult to conceive of common drivers that would not be controlled for here. Finally: "One of the most depressing conclusions I reached when studying this topic a few years ago was that we don't really know if the world is getting warmer or not." If your conclusion is contrary to that of _every_ scientist who studies this for a living, you should consider the hypothesis that the problem is with your reasoning/analysis and not with everyone else. [1] https://en.wikipedia.org/wiki/Growth_in_a_Time_of_Debt https://en.wikipedia.org/wiki/Growth_in_a_Time_of_Debt