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description=15 posts published by Lars Syll during July 2022;

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ream economics has lost immensely in terms of status and prestige during the last years not the least because of its manifest inability to foresee the latest financial and economic crises and its lack of constructive and sustainable policies to take us out of the crises we all know that many activities relations processes and events are uncertain and that the data do not unequivocally single out one decision as the only rational one neither the economist nor the deciding individual can fully pre specify how people will decide when facing uncertainties and ambiguities that are ontological facts of the way the world works mainstream economists however have wanted to use their hammer and so decided to pretend that the world looks like a nail pretending that uncertainty can be reduced to risk and constructing models on that assumption have only contributed to financial crises and economic havoc how do we put an end to this intellectual cataclysm how do we re establish credence and trust in economics as a science five changes are absolutely decisive 1 stop pretending that we have exact and rigorous answers on everything because we don t we build models and theories and tell people that we can calculate and foresee the future but we do this based on mathematical and statistical assumptions that often have little or nothing to do with reality by pretending that there is no really important difference between model and reality we lull people into thinking that we have things under control we haven t this false feeling of security was one of the factors that contributed to the financial crisis of 2008 2 stop the childish and exaggerated belief in mathematics giving answers to important economic questions mathematics gives exact answers to exact questions but the relevant and interesting questions we face in the economic realm are rarely of that kind questions like is 2 2 4 are never posed in real economies instead of a fundamentally misplaced reliance on abstract mathematical deductive axiomatic models having anything of substance to contribute to our knowledge of real economies it would be far better if we pursued thicker models and relevant empirical studies and observations mathematics cannot establish the truth value of a fact never has never will 3 stop pretending that there are laws in economics there are no universal laws in economics economies are not like planetary systems or physics labs the most we can aspire to in real economies is establishing possible tendencies with varying degrees of generalizability 4 stop treating other social sciences as poor relations economics has long suffered from hubris a more broad minded and multifarious science would enrich today s economics and make it more relevant and realistic 5 stop building models and making forecasts of the future based on totally unreal micro founded macro models with intertemporally optimizing robot like representative actors equipped with rational expectations this is pure nonsense we have to build our models on assumptions that are not so blatantly in contradiction to reality assuming that people are green and come from mars is not a good not even as a successive approximation modeling strategy share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky nights in white satin 30 jul 2022 at 16 35 posted in varia comments off on nights in white satin on revient toujours à ses premières amours share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky perfect day personal 28 jul 2022 at 21 41 posted in varia comments off on perfect day personal spending a beautiful summer afternoon with my lovely daughters linnea and tora share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky on statistics and causality 28 jul 2022 at 15 56 posted in statistics econometrics 9 comments ironically the need for a theory of causation began to surface at the same time that statistics came into being this was a critical moment in the history of science the opportunity to equip causal questions with a language of their own came very close to being realized but was squandered in the following years these questions were declared unscientific and went underground despite heroic efforts by the geneticist sewall wright 1889 1988 causal vocabulary was virtually prohibited for more than half a century and when you prohibit speech you prohibit thought and stifle principles methods and tools readers do not have to be scientists to witness this prohibition in statistics 101 every student learns to chant correlation is not causation with good reason the rooster s crow is highly correlated with the sunrise yet it does not cause the sunrise unfortunately statistics has fetishized this commonsense observation it tells us that correlation is not causation but it does not tell us what causation is in vain will you search the index of a statistics textbook for an entry on cause students are not allowed to say that x is the cause of y only that x and y are related or associated statistical reasoning certainly seems paradoxical to most people take for example the well known simpson s paradox from a theoretical perspective simpson s paradox importantly shows that causality can never be reduced to a question of statistics or probabilities unless you are miraculously able to keep constant all other factors that influence the probability of the outcome studied to understand causality we always have to relate it to a specific causal structure statistical correlations are never enough no structure no causality simpson s paradox is an interesting paradox in itself but it can also highlight a deficiency in the traditional statistical econometric approach toward causality say you have 1000 observations on men and an equal amount of observations on women applying for admission to university studies and that 70 of men are admitted but only 30 of women running a logistic regression to find out the odds ratios and probabilities for men and women on admission females seem to be in a less favorable position discriminated against compared to males male odds are 2 33 female odds are 0 43 giving an odds ratio of 5 44 but once we find out that males and females apply to different departments we may well get a simpson s paradox result where males turn out to be discriminated against say 800 males apply for economics studies 680 admitted and 200 for physics studies 20 admitted and 100 female apply for economics studies 90 admitted and 900 for physics studies 210 admitted giving odds ratios of 0 62 and 0 37 statistical and econometric patterns should never be seen as anything else than possible clues to follow behind observable data there are real structures and mechanisms operating things that are if we really want to understand explain and possibly predict things in the real world more important to get hold of than to simply correlate and regress observable variables statistics cannot establish the truth value of a fact never has never will lars p syll share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky should we save capitalism 28 jul 2022 at 14 23 posted in economics comments off on should we save capitalism 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 insignificance of significance 27 jul 2022 at 10 38 posted in statistics econometrics comments off on the insignificance of significance a significance test is a scientific instrument and like any other instrument it has a certain degree of precision if you make the test more sensitive by increasing the size of the studied population for example you enable yourself to see ever smaller effects that s the power of the method but also its danger the truth is the null hypothesis if we take it literally is probably just about always false when you drop a powerful drug into a patient s bloodstream it s hard to believe the intervention has exactly zero effect on the probability that the patient will develop esophageal cancer or thrombosis or bad breath if only we could go back in time to the dawn of statistical nomenclature and declare that a result passing fisher s test with a p value of less than 0 05 was statistically noticeable or statistically detectable instead of statistically significant that would be truer to the meaning of the method which merely counsels us about the existence of an effect but is silent about its size or importance jordan ellenberg share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky werden wir immer dümmer 26 jul 2022 at 16 27 posted in politics society comments off on werden wir immer dümmer 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 trickle down scam 25 jul 2022 at 10 05 posted in economics comments off on the trickle down scam the empirical literature on the impact of corporate taxes on economic growth reaches ambiguous conclusions corporate tax cuts increase reduce or do not significantly affect growth we apply meta regression methods to a novel data set with 441 estimates from 42 primary studies there is evidence for publication selectivity in favour of reporting growth enhancing effects of corporate tax cuts correcting for this bias we cannot reject the hypothesis of a zero effect of corporate taxes on growth several factors influence reported estimates including researcher choices concerning the measurement of growth and corporate taxes and controlling for other budgetary components sebastian gechert philipp heimberger share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky what to do about the present inflation 20 jul 2022 at 14 40 posted in economics 1 comment to be sure some normalization of interest rates would be a good thing interest rates are supposed to reflect the scarcity of capital and the correct price of capital obviously is not zero or negative as near zero interest rates and very negative real inflation adjusted interest rates would seem to imply but there are substantial dangers in pushing rates too high too fast for example it is important to recognize that us wage growth has slowed sharply from an annualized rate of over 6 in the fall of 2021 to just 4 4 in the most recent period so much for the wage price spiral that previously had everyone scared and fueled demands for rapid monetary policy tightening itis also important to recognize that this development runs completely counter to the standard phillips curve models which assume an inverse relationship between inflation and unemployment over the short term the slowing of wage growth has occurred at a time when the unemployment rate is under 4 a level below anyone s estimates of the non accelerating inflation rate of unemployment this phenomenon may owe something to the much lower level of unionization and worker power in today s economy but whatever the reason the sharp slowdown in wage growth indicates that policymakers should think twice before generating further increases in unemployment to tame inflation most importantly we need to help those at the bottom and middle cope with the consequences of inflation because the us is close to being energy independent the country as a whole is relatively unaffected by changing energy prices gains to exporters are simply offset by importers losses but there is a huge distribution problem oil and gas companies are raking in windfall gains while ordinary citizens struggle to make ends meet an inflation rebate financed by a windfall profits tax on fossil fuel corporations would efficiently address these inequities joseph stiglitz share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky heckman on where causality resides 17 jul 2022 at 13 53 posted in statistics econometrics 11 comments i make two main points that are firmly anchored in the econometric tradition the first is that causality is a property of a model of hypotheticals a fully articulated model of the phenomena being studied precisely defines hypothetical or counterfactual states a definition of causality drops out of a fully articulated model as an automatic by product a model is a set of possible counterfactual worlds constructed under some rules the rules may be the laws of physics the consequences of utility maximization or the rules governing social interactions to take only three of many possible examples a model is in the mind as a consequence causality is in the mind james heckman so according to this nobel prize winning econometrician causality is in the mind but is that a tenable view yours truly thinks not if one as an economist or social scientist would subscribe to that view there would be pretty little reason to be interested in questions of causality at all and it sure doesn t suffice just to say that all science is predicated on assumptions to most of us models are seen as vehicles or instruments by which we represent causal processes and structures that exist and operate in the real world as we all know models often do not succeed in representing or explaining these processes and structures but if we didn t consider them as anything but figments of our minds well then maybe we ought to reconsider why we should be in the science business at all the world as we know it has limited scope for certainty and perfect knowledge its intrinsic and almost unlimited complexity and the interrelatedness of its parts prevent the possibility of treating it as constituted by atoms with discretely distinct separable and stable causal relations our knowledge accordingly has to be of a rather fallible kind to search for deductive precision and rigour in such a world is self defeating the only way to defend such an endeavour is to restrict oneself to prove things in closed model worlds why we should care about these and not ask questions of relevance is hard to see as scientists we have to get our priorities right ontological under labouring has to precede epistemology the value of getting at precise and rigorous conclusions about causality based on tractability conditions that are seldom met in real life is difficult to assess testing and constructing models is one thing but we do also need guidelines for how to evaluate in which situations and contexts they are applicable formalism may help us a bit down the road but we have to make sure it somehow also fits the world if it is going to be really helpful in navigating that world in all of science conclusions are never more certain than the assumptions on which they are founded b...