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description= 15 posts published by Lars Syll during November 2023;
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t die vielfalt der meinungen zu fördern begünstigt der wokismus einen engen ideologischen konformismus bei dem nur als legitim betrachtetes denken akzeptiert wird wokismus neigt dazu unterschiede zwischen gruppen zu essentialisieren und so eine vereinfachte und stereotypisierte sicht der gesellschaft zu schaffen er leugnet die individuelle vielfalt innerhalb jeder gruppe indem er annimmt dass alle mitglieder die gleichen erfahrungen und perspektiven teilen diese übermäßige verallgemeinerung kann zu vorurteilen und umgekehrter diskriminierung führen bei der bestimmte gruppen einfach aufgrund ihrer identität bevorzugt werden obwohl der wokismus von einem wunsch nach sozialer gerechtigkeit motiviert sein kann ist es entscheidend seine problematischen implikationen zu untersuchen kollektive viktimisierung und intoleranz gegenüber abweichenden meinungen behindern den fortschritt zu einer inklusiveren und gerechteren gesellschaft es ist wichtig einen offenen dialog zu führen und einen nuancierteren und ausgewogeneren ansatz für soziale fragen zu fördern der sowohl individuelle autonomie als auch eine echte suche nach gerechtigkeit schätzt share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky ignorability a questionable assumption 25 nov 2023 at 18 59 posted in statistics econometrics 1 comment researchers adhering to missing data analysis invariably invoke an ad hoc assumption called conditional ignorability often decorated as ignorable treatment assignment mechanism which is far from being well understood by those who make it let alone those who need to judge its plausibility for readers versed in graphical modeling conditional ignorability is none other than the back door criterion that students learn in the second class on causal inference and which missing data advocates have vowed to avoid at all cost as we know this criterion can easily be interpreted and verified when background knowledge is presented in graphical form but as you can imagine it turns into a frightening enigma for those who shun the light of graphs still the simplicity of reading this criterion off a graph makes it easy to test whether those who rely heavily on ignorability assumptions know what they are assuming the results of this test are discomforting unfortunately the mantra missing data analysis in causal inference is well understood continues to be chanted at an ever increasing intensity building faith among the faithful and luring chanters to assume ignorability as self evident worse yet the mantra blinds researchers from seeing how an improved level of understanding can emerge by abandoning the missing data prism altogether and conducting causal analysis in its natural habitat using scientific models of reality rather than unruly patterns of missingness in the data we come now to claim 2 concerning the possibility of causality free interpretation of missing data problems it is possible indeed to pose a missing data problem in purely statistical terms totally void of missingness mechanism vocabulary void even of conditional independence assumptions but this is rarely done because the answer is trivial none of the parameters of interest would be estimable without such assumptions i e the likelihood function is flat in theory one can argue that there is really nothing causal about missingness mechanism as conceptualized by rubin 1976 since it is defined in terms of conditional independence relations a purely statistical notion that requires no reference to causation not quite the conditional independence relations that define missingness mechanisms are fundamentally different from those invoked in standard statistical analysis in standard statistics independence assumptions are presumed to hold in the distribution that governs the observed data whereas in missing data problems the needed independencies are assumed to hold in the distribution of variables which are only partially observed in other words the independence assumptions invoked in missing data analysis are necessarily judgmental and only rarely do they have testable implications in the available data we hope these arguments convince even the staunchest missing data enthusiast to switch mantras and treat missing data problems for what they are causal inference problems judea pearl karthika mohan an interesting article on a highly questionable assumption used in potential outcome causal models it also exemplifies how tractability often has come to override reality and truth in science not least in modern mainstream economics a tractable model is of course great since it usually means you can solve it but using simplifying tractability assumptions rational expectations common knowledge representative agents linearity additivity ergodicity exchangeability ignorability etc because otherwise they cannot manipulate their models or come up with rigorous and precise predictions and explanations does not exempt scientists from having to justify their modelling choices being able to manipulate things in models cannot per se be enough to warrant a methodological choice suppose economists do not really think their tractability assumptions make for good and realist models in that case it is certainly a just question to ask for clarification of the ultimate goal of the whole modelling endeavour take for example the ongoing discussion on rational expectations as a modelling assumption in economics those who want to build macroeconomics on microfoundations usually maintain that the only robust policies are those based on rational expectations and representative actors models as yours truly has tried to show in on the use and misuse of theories and models in mainstream economics there is really no support for this conviction at all if microfounded macroeconomics has nothing to say about the real world and the economic problems out there why should we care about it the final court of appeal for macroeconomic models is not if we once we have made our tractability assumptions can manipulate them but the real world and as long as no convincing justification is put forward for how the inferential bridging de facto is made macroeconomic modelbuilding is little more than hand waving that gives us rather a little warrant for making inductive inferences from models to real world target systems if substantive questions about the real world are being posed it is the formalistic mathematical representations utilized to analyze them that have to match reality not the other way around share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky the dangers of too much control 23 nov 2023 at 15 41 posted in statistics econometrics comments off on the dangers of too much control you see it all the time in studies we controlled for and then the list starts the more things you can control for the stronger your study is or at least the stronger your study seems controls give the feeling of specificity of precision but sometimes you can control for too much sometimes you end up controlling for the thing you re trying to measure an example is research around the gender wage gap which tries to control for so many things that it ends up controlling for the thing it s trying to measure take hours worked which is a standard control in some of the more sophisticated wage gap studies women tend to work fewer hours than men if you control for hours worked then some of the gender wage gap vanishes as yglesias wrote it s silly to act like this is just some crazy coincidence women work shorter hours because as a society we hold women to a higher standard of housekeeping and because they tend to be assigned the bulk of childcare responsibilities controlling for hours worked in other words is at least partly controlling for how gender works in our society it s controlling for the thing that you re trying to isolate ezra klein trying to reduce the risk of having established only spurious relations when dealing with observational data statisticians and econometricians standardly add control variables the hope is that one thereby will be able to make more reliable causal inferences but as keynes showed already back in the 1930s when criticizing statistical econometric applications of regression analysis if you do not manage to get hold of all potential confounding factors the model risks producing estimates of the variable of interest that are even worse than models without any control variables at all conclusion think twice before you simply include control variables in your models when i present this argument one or more scholars say but shouldn t i control for everything i can in my regressions if not aren t my coefficients biased due to excluded variables the excluded variable argument only works if you are sure your specification is precisely correct with all variables included but no one can know that with more than a handful of explanatory variables a preferable approach is to separate the observations into meaningful subsets internally compatible statistical regimes if this can t be done then statistical analysis can t be done a researcher claiming that nothing else but the big messy regression is possible because after all some results have to be produced is like a jury that says well the evidence was weak but somebody had to be convicted christopher h achen kitchen sink econometric models are often the result of researchers trying to control for confounding but what they usually haven t understood is that the confounder problem requires a causal solution and not statistical control controlling for everything opens up the risk that we control for collider variables and thereby create back door paths which gives us confounding that wasn t there to begin with share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky scientific fraud 23 nov 2023 at 14 28 posted in varia comments off on scientific fraud in 2022 the u s national institutes of health nih placed a large bet on an experimental drug developed to limit brain damage after strokes the gamble seemed warranted lab studies most by a longtime grantee prominent university of southern california usc neuroscientist berislav zlokovic had generated promising data a small safety study of the drug sponsored by a company zlokovic co founded called zz biotech was also encouraging because of its potential to address an unmet medical need the u s food and drug administration fda gave the compound fast track status with the prospect of accelerated approval and priority review zz biotech says the new trial should start within a few months but a 113 page dossier obtained by science from a small group of whistleblowers paints a less encouraging picture the dossier highlights evidence that dozens of papers from zlokovic s lab including many supporting the idea that the compound was ready for human testing contain seemingly doctored data that suggest scientific misconduct speaking to science anonymously four former members of zlokovic s lab say the anomalies the whistleblowers found are no accident they describe a culture of intimidation in which he regularly pushed them and others in the lab to adjust data two of them said he sometimes had people change lab notebooks after experiments were completed to ensure they only contained the desired results there were clear examples of him instructing people to manipulate data to fit the hypothesis one of the lab members says science share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky busting the natural rate of unemployment myth 23 nov 2023 at 11 22 posted in economics 1 comment almost sixty years ago milton friedman wrote an in famous article arguing that 1 the natural rate of unemployment was independent of monetary policy and that 2 trying to keep the unemployment rate below the natural rate would only give rise to higher and higher inflation the hypothesis has always been controversial and much theoretical and empirical work has questioned the real world relevance of the idea that unemployment is independent of monetary policy and that there is no long run trade off between inflation and unemployment my own view on the subject is that the natural rate hypothesis does not hold water simply because the relations it describes have never actually existed the only thing that amazes yours truly is that although this is pretty common knowledge so called new keynesian macroeconomists still today use it and its cousin the phillips curve as a fundamental building block in their models why because without it new keynesians have to give up their again and again empirically falsified neoclassical view of the long run neutrality of money and the simplistic idea of inflation as an excess demand phenomenon the natural rate hypothesis approach nrh is not only of theoretical interest far from it the real damage done is that policymakers who take decisions based on nrh models systematically implement austerity measures and kill off economic expansion the unnecessary and costly unemployment that this self inflicted and flawed illusion eventuates is something new classical and new keynesian advocates should always be kept accountable for if the nrh and rational expectations are both true simultaneously a plot of decade averages of inflation against unemployment should reveal a vertical line at the natural rate of unemployment this prediction fails dramatically there is no tendency for the points to lie around a vertical line and if anything the long run phillips is upward sloping and closer to being horizontal than vertical since it is unlikely that expectations are systematically biased over decades i conclude that the nrh is false roger farmer share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky new keynesianism more useless than ever 19 nov 2023 at 16 16 posted in economics 4 comments macroeconomic models may be an informative tool for research but if practitioners of new keynesian macroeconomics do not investigate and make an effort to provide a justification for the credibility of the assumptions on which they erect their building it will not fulfil its tasks there is a gap between its aspirations and its accomplishments and without more supportive evidence to substantiate its claims critics will continue to consider its ultimate argument as a mixture of rather unhelpful metaphors and metaphysics maintaining that economics is a science in the true knowledge business yours truly remains a sceptic of the pretences and aspirations of new keynesian macroeconomics so far i cannot really see that it has yielded very much in terms of realistic and relevant economic knowledge keynes basically argued that it was inadmissible...
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