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description= 15 posts published by Lars Syll during February 2020;
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he menace of the years shall find me unafraid a great poem it helped one of the greatest men that has walked this earth keep hope alive during twenty seven years in prison long live his unconquerable soul via con me 26 feb 2020 at 17 08 posted in varia comments off on via con me non perderti per niente al mondo lo spettacolo di arte varia di un uomo innamorato di te amen hayr sourp 25 feb 2020 at 17 27 posted in varia comments off on amen hayr sourp e penso a te 25 feb 2020 at 16 35 posted in varia comments off on e penso a te adorno on the philosophy of alternative facts 25 feb 2020 at 16 03 posted in politics society 1 comment something repellent clings to the lie and though the consciousness of this was indeed beaten into one with the old whip this simultaneously said something about the master of the dungeon the mistake lies in all too much honesty whoever lies is ashamed because in every lie they must experience what is degrading in the existing state of the world such shame saps the energy of the lies of those who are more subtly organized they do it badly and only thereby does the lie come to be genuinely unmoral for others it suggests the former think the latter are stupid and serves to express disrespect among today s cunning practitioners the lie has long since lost its honest function of concealing something real no one believes anyone everyone is in the loop lies are told only when someone wants others to know they aren t important that the former does not need the latter and does not care what they think today the lie once a liberal means of communication has become one of the techniques of brazenness with whose help every single person spreads the iciness in whose shelter they thrive theodor w adorno alternative facts people like trump erdogan orban and putin sure make reading minima moralia a worthwhile effort share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky econometrics a crooked path from cause to effect 24 feb 2020 at 19 48 posted in statistics econometrics 1 comment in their book mastering metrics the path from cause to effect joshua angrist and jörn steffen pischke write our first line of attack on the causality problem is a randomized experiment often called a randomized trial in a randomized trial researchers change the causal variables of interest for a group selected using something like a coin toss by changing circumstances randomly we make it highly likely that the variable of interest is unrelated to the many other factors determining the outcomes we want to study random assignment isn t the same as holding everything else fixed but it has the same effect random manipulation makes other things equal hold on average across the groups that did and did not experience manipulation as we explain on average is usually good enough angrist and pischke may dream of the trials we d like to do and consider the notion of an ideal experiment something that disciplines our approach to econometric research but to maintain that on average is usually good enough is an allegation that in my view is rather unwarranted and for many reasons first of all it amounts to nothing but hand waving to simpliciter assume without argumentation that it is tenable to treat social agents and relations as homogeneous and interchangeable entities randomization is used to basically allow the econometrician to treat the population as consisting of interchangeable and homogeneous groups treatment and control the regression models one arrives at by using randomized trials tell us the average effect that variations in variable x has on the outcome variable y without having to explicitly control for effects of other explanatory variables r s t etc etc everything is assumed to be essentially equal except the values taken by variable x in a usual regression context one would apply an ordinary least squares estimator ols in trying to get an unbiased and consistent estimate y α βx ε where α is a constant intercept β a constant structural causal effect and ε an error term the problem here is that although we may get an estimate of the true average causal effect this may mask important heterogeneous effects of a causal nature although we get the right answer of the average causal effect being 0 those who are treated x 1 may have causal effects equal to 100 and those not treated x 0 may have causal effects equal to 100 contemplating being treated or not most people would probably be interested in knowing about this underlying heterogeneity and would not consider the ols average effect particularly enlightening limiting model assumptions in economic science always have to be closely examined since if we are going to be able to show that the mechanisms or causes that we isolate and handle in our models are stable in the sense that they do not change when we export them to our target systems we have to be able to show that they do not only hold under ceteris paribus conditions and a fortiori only are of limited value to our understanding explanations or predictions of real economic systems real world social systems are not governed by stable causal mechanisms or capacities the kinds of laws and relations that econometrics has established are laws and relations about entities in models that presuppose causal mechanisms being atomistic and additive when causal mechanisms operate in real world social target systems they only do it in ever changing and unstable combinations where the whole is more than a mechanical sum of parts if economic regularities obtain they do it as a rule only because we engineered them for that purpose outside man made nomological machines they are rare or even non existent unfortunately that also makes most of the achievements of econometrics as most of the contemporary endeavours of mainstream economic theoretical modelling rather useless remember that a model is not the truth it is a lie to help you get your point across and in the case of modeling economic risk your model is a lie about others who are probably lying themselves and what s worse than a simple lie a complicated lie sam l savage the flaw of averages when joshua angrist and jörn steffen pischke in an earlier article of theirs the credibility revolution in empirical economics how better research design is taking the con out of econometrics journal of economic perspectives 2010 say that anyone who makes a living out of data analysis probably believes that heterogeneity is limited enough that the well understood past can be informative about the future i really think they underestimate the heterogeneity problem it does not just turn up as an external validity problem when trying to export regression results to different times or different target populations it is also often an internal problem to the millions of regression estimates that economists produce every year but when the randomization is purposeful a whole new set of issues arises experimental contamination which is much more serious with human subjects in a social system than with chemicals mixed in beakers anyone who designs an experiment in economics would do well to anticipate the inevitable barrage of questions regarding the valid transference of things learned in the lab one value of z into the real world a different value of z absent observation of the interactive compounding effects z what is estimated is some kind of average treatment effect which is called by imbens and angrist 1994 a local average treatment effect which is a little like the lawyer who explained that when he was a young man he lost many cases he should have won but as he grew older he won many that he should have lost so that on the average justice was done in other words if you act as if the treatment effect is a random variable by substituting βt for β0 β zt the notation inappropriately relieves you of the heavy burden of considering what are the interactive confounders and finding some way to measure them if little thought has gone into identifying these possible confounders it seems probable that little thought will be given to the limited applicability of the results in other settings ed leamer evidence based theories and policies are highly valued nowadays randomization is supposed to control for bias from unknown confounders the received opinion is that evidence based on randomized experiments therefore is the best more and more economists have also lately come to advocate randomization as the principal method for ensuring being able to make valid causal inferences i would however rather argue that randomization just as econometrics promises more than it can deliver basically because it requires assumptions that in practice are not possible to maintain especially when it comes to questions of causality randomization is nowadays considered some kind of gold standard everything has to be evidence based and the evidence has to come from randomized experiments but just as econometrics randomization is basically a deductive method given the assumptions such as manipulability transitivity separability additivity linearity etc these methods deliver deductive inferences the problem of course is that we will never completely know when the assumptions are right and although randomization may contribute to controlling for confounding it does not guarantee it since genuine randomness presupposes infinite experimentation and we know all real experimentation is finite and even if randomization may help to establish average causal effects it says nothing of individual effects unless homogeneity is added to the list of assumptions real target systems are seldom epistemically isomorphic to our axiomatic deductive models systems and even if they were we still have to argue for the external validity of the conclusions reached from within these epistemically convenient models systems causal evidence generated by randomization procedures may be valid in closed models but what we usually are interested in is causal evidence in the real target system we happen to live in when does a conclusion established in population x hold for target population y only under very restrictive conditions angrist s and pischke s ideally controlled experiments tell us with certainty what causes what effects but only given the right closures making appropriate extrapolations from ideal accidental natural or quasi experiments to different settings populations or target systems is not easy it works there is no evidence for it will work here causes deduced in an experimental setting still have to show that they come with an export warrant to the target population system the causal background assumptions made have to be justified and without licenses to export the value of rigorous and precise methods and on average knowledge is despairingly small share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky to my children 24 feb 2020 at 08 50 posted in varia comments off on to my children this one is for my sons and daughters david tora linnea sebastian amanda next page recent posts arbetslöshetssiffran som styr den ekonomiska politiken 50 år efter studenten personal finding the late student stuff love s secret stein s paradox a lesson on precision and unbiasedness audioholic summer research ett välförtjänt bottenrekord arbetslösheten inte statsskulden är problemet how credible is the credibility revolution in econometrics randomness and representativeness are not the same lönebildning och arbetslöshet i sverige nancy cartwright s contributions to the philosophy of science what does randomisation guarantee nothing model reification in statistics and econometrics why democracy outperforms dictatorship comments policy i like comments follow netiquette comments especially anonymous ones with pseudo argumentations abusive language or irrelevant links 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