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We identify two issues with the reverse regression approach as implemented in several classic reconstructions of past climate fluctuations from dendroclimatologcical data series. First, instead of estimating the causal relationship between the proxy, which is measured with significant error, as function of climate and formally inverting the relationship, most papers estimate the inverted relationship directly. This leads to biased coefficients and reconstructions with artificially low variance. Second, we show that inversion of the relationship is often done incorrectly when the underlying causal relationship is dynamic in nature. We show analytically as well as using Monte Carlo experiments and actual tree ring data, that the reverse regression method results in biased coefficients, reconstructions with artificially low variance and overly smooth reconstructions. We further demonstrate that correct application of the inverse regression method is preferred. However, if the measurement error in the tree ring index is significant, neither method provides reliable reconstructions.

The purpose of this study is to define destination social responsibility as a multidimensional construct and examine the relationships between destination social responsibility, tourists' emotions and their satisfaction, through the lens of corporate social responsibility. A model is empirically tested with a sample of 359 random foreign tourists visiting Hoi An, Vietnam. The results indicate that all destination social responsibility dimensions, including economic, environmental, legal–ethical, and philanthropic responsibilities significantly enhance tourists' emotions, while only legal–ethical and philanthropic responsibilities directly affect tourists' satisfaction. The findings also confirm the mediating effect of emotions between destination social responsibility and tourists' overall satisfaction. [ABSTRACT FROM AUTHOR]