Model robustness as a confirmatory virtue: The case of climate science

I propose a distinct type of robustness, which I suggest can support a confirmatory role in scientific reasoning, contrary to the usual philosophical claims. In model robustness, repeated production of the empirically successful model prediction or retrodiction against a background of independently-supported and varying model constructions, within a group of models containing a shared causal factor, may suggest how confident we can be in the causal factor and predictions/retrodictions, especially once supported by a variety of evidence framework. I present climate models of greenhouse gas global warming of the 20th Century as an example, and emphasize climate scientists' discussions of robust models and causal aspects. The account is intended as applicable to a broad array of sciences that use complex modeling techniques.     

The role of macroeconometric models in forecasting and policy analysis in the united states

This article stresses how little is known about the quality, particularly the relative quality, of macroeconometric models. Most economists make a strict distinction between the quality of a model per se and the accuracy of solutions based on that model. While this distinction is valid, it leaves unanswered how to compare the‘validity’of conditional models. The standard test, the accuracy of ex post simulations, is not definitive when models with differing degrees of exogeneity are compared. In addition, it is extremely difficult to estimate the relative quantitative importance of conceptual problems of models, such as parameter instability across‘policy regimes’ In light of the difficulty in comparisons of conditional macroeconometric models, many model-builders and users assume that the best models are those that have been used to make the most accurate forecasts are those made with the best models. Forecasting experience indicates that forecasters using macroeconometric models have produced more accurate macroeconomic forecasts than either naive or sophisticated unconditional statistical models. It also suggests that judgementally adjusted forecasts have been more accurate than model-based forecasts generated mechanically. The influence of econometrically-based forecasts is now so pervasive that it is difficult to find examples of‘purely judgemental’forecasts.