Among other things, Leamer shows that regressions using different sets of control variables, both of which might be deemed reasonable, can lead to different substantive interpretations (see Section V.). Fortunately, in many economic applications, pa rticularly using linear models, the analysis is more robust than the assumptions, and sensibly interpreted will provide useful results even if some assumptions fail. Robustness tests have become an integral part of research methodology in the social sciences. Yet many people with papers that have very weak inferences that struggle with alternative arguments (i.e., have huge endogeneity problems, might have causation backwards, etc) often try to just push the discussions of those weaknesses into an appendix, or a footnote, so that they can be quickly waved away as a robustness test. j. Find more ways to say robustness, along with related words, antonyms and example phrases at Thesaurus.com, the world's most trusted free thesaurus. True, positive results are probably overreported and some really bad results are probably hidden, but at the same time it’s not unusual to read that results are sensitive to specification, or that the sign and magnitude of an effect are robust, while significance is not or something like that. As discussed frequently on this blog, this “accounting” is usually vague and loosely used. > Shouldn’t a Bayesian be doing this too? Perhaps “nefarious” is too strong. is predicted, holding all other variables constant. is in the model. single, and a postestimation graph appear below. Given that these conditions of a study are met, the models can be verified to be true through the use of mathematical proofs. obvious typo at the end: “some of these checks” not “some these these checks”. is not equal to zero. Err. We will drop Some examples of checking for heteroscedasticity can be found in Goldstein [18, Chapter 3] and Snijders and Bosker [51, Chapter 8]. So, at best, robustness checks “some” assumptions for how they impact the conclusions, and at worst, robustness becomes just another form of the garden of forked paths. I wanted to check that I have done the correct robustness checks for my model. But then robustness applies to all other dimensions of empirical work. But the usual reason for a robustness check, I think, is to demonstrate that your main analysis is OK. But, there are other, less formal, social mechanisms that might be useful in addressing the problem. It’s interesting this topic has come up; I’ve begun to think a lot in terms of robustness. (To put an example: much of physics focuss on near equilibrium problems, and stability can be described very airily as tending to return towards equilibrium, or not escaping from it – in statistics there is no obvious corresponding notion of equilibrium and to the extent that there is (maybe long term asymptotic behavior is somehow grossly analogous) a lot of the interesting problems are far from equilibrium (e.g. Maybe a different way to put it is that the authors we’re talking about have two motives, to sell their hypotheses and display their methodological peacock feathers. Title stata.com robust ... the context of robustness against heteroskedasticity. Robustness checks involve reporting alternative specifications that test the same hypothesis. regress, vce(robust) uses, by default, this multiplier with kequal to the number of explanatory variables in the model, including the constant. 35 years in the business, Keith. In any case, if you change your data, then you need to check normality (presumably using Shapiro-Wilk) and homogeneity of variances (e.g. Machine learning is a sort of subsample robustness, yes? The t-test and robustness to non-normality September 28, 2013 by Jonathan Bartlett The t-test is one of the most commonly used tests in statistics. Similarly, Oster (2013) found that 75% of 2012 papers published in The American Economic Review, Journal of Political Economy, and Quarterly Journal of Economics explored the sensitivity of results to varying control sets3. There are other routes to getting less wrong Bayesian models by plotting marginal priors or analytically determining the impact of the prior on the primary credible intervals. In situations where missingness is plausibly strongly related to the unobserved values, and nothing that has been observed will straighten this out through conditioning, a reasonable approach is to develop several different models of the missing data and apply them. weight. If we set our alpha level to 0.05, we would fail to reject the Using Stata 11 & higher for Logistic Regression Page 3 Robust statistics are statistics with good performance for data drawn from a wide range of probability distributions, especially for distributions that are not normal.Robust statistical methods have been developed for many common problems, such as estimating location, scale, and regression parameters.One motivation is to produce statistical methods that are not unduly affected by outliers.