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description= 15 posts published by Lars Syll during July 2022;
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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 but most epistemically convenient methods and models that work in well behaved systems do not come with warrants that they will work in other real world contexts share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky postmodernism explained 16 jul 2022 at 10 30 posted in politics society comments off on postmodernism explained 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 nothing but a second best explanatory practice 13 jul 2022 at 10 50 posted in statistics econometrics comments off on econometrics nothing but a second best explanatory practice consider two elections a and b for each of them identify the events that cause a given percentage of voters to turn out once we have thus explained the turnout in election a and the turnout in election b the explanation of the difference if any follows automatically as a by product as a bonus we might be able to explain whether identical turnouts in a and b are accidental that is due to differences that exactly offset each other or not in practice this procedure might be too demanding the data or he available theories might not allow us to explain the phenomena in and of themselves we should be aware however that if we do resort to explanation of variation we are engaging in a second best explanatory practice modern econometrics is fundamentally based on assuming usually without any explicit justification that we can gain causal knowledge by considering independent variables that may have an impact on the variation of a dependent variable this is however far from self evident often the fundamental causes are constant forces that are not amenable to the kind of analysis econometrics supplies us with as stanley lieberson has it in making it count one can always say whether in a given empirical context a given variable or theory accounts for more variation than another but it is almost certain that the variation observed is not universal over time and place hence the use of such a criterion first requires a conclusion about the variation over time and place in the dependent variable if such an analysis is not forthcoming the theoretical conclusion is undermined by the absence of information moreover it is questionable whether one can draw much of a conclusion about causal forces from simple analysis of the observed variation to wit it is vital that one have an understanding or at least a working hypothesis about what is causing the event per se variation in the magnitude of the event will not provide the answer to that question trygve haavelmo was making a somewhat similar point back in 1941 when criticizing the treatment of the interest variable in tinbergen s regression analyses the regression coefficient of the interest rate variable being zero was according to haavelmo not sufficient for inferring that variations in the rate of interest play only a minor role or no role at all in the changes in investment activity interest rates may very well play a decisive indirect role by influencing other causally effective variables and the rate of interest may not have varied much during the statistical testing period and for this reason the rate of interest would not explain very much of the variation in net profit and thereby the variation in investment which has actually taken place during this period but one cannot conclude that the rate of influence would be inefficient as an autonomous regulator which is after all the important point this problem of nonexcitation when there is too little variation in a variable to say anything about its potential importance and we can t identify the reason for the factual influence of the variable being negligible strongly confirms that causality in economics and other social sciences can never solely be a question of statistical inference causality entails more than predictability and to really in depth explain social phenomena requires theory analysis of variation the foundation of all econometrics can never in itself reveal how these variations are brought about first when we are able to tie actions processes or structures to the statistical relations detected can we say that we are getting at relevant explanations of causation too much in love with axiomatic deductive modeling neoclassical economists especially tend to forget that accounting for causation how causes bring about their effects demands deep subject matter knowledge and acquaintance with the intricate fabrics and contexts as already keynes argued in his a treatise on probability statistics and econometrics should primarily be seen as means to describe patterns of associations and correlations means that we may use as suggestions of possible causal relations forgetting that economists will continue to be stuck with a second best explanatory practice share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky uber the ugly truth 12 jul 2022 at 18 45 posted in politics society comments off on uber the ugly truth added july 13 and macron now tells us he s extremely proud of his uber maneuvering share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky mainstream economics the triumph of ideology over science 12 jul 2022 at 11 13 posted in economics 2 comments research shows not only that individuals sometimes act differently than standard economic theories predict but that they do so regularly systematically and in ways that can be understood and interpreted through alternative hypotheses competing with those utilised by orthodox economists to most market participants and indeed ordinary observers this does not seem like big news in fact this irrationality is no news to the economics profession either john maynard keynes long ago described the stock market as based not on rational individuals struggling to uncover market fundamentals but as a beauty contest in which the winner is the one who guesses best what the judges will say adam smith s invisible hand the idea that free markets lead to efficiency as if guided by unseen forces is invisible at least in part because it is not there for more than 20 years economists were enthralled by so called rational expectations models which assumed that all participants have the same if not perfect information and act perfectly rationally that markets are perfectly efficient that unemployment never exists except when caused by greedy unions or government minimum wages and where there is never any credit rationing that such models prevailed especially in america s graduate schools despite evidence to the contrary bears testimony to a triumph of ideology over science unfortunately students of these graduate programmes now act as policymakers in many countries and are trying to implement programmes based on the ideas that have come to be called market fundamentalism good science recognises its limitations but the prophets of rational expectations have usually shown no such modesty joseph stiglitz the rational expectations hypothesis one of the cornerstones of mainstream economics presupposes basically for reasons of consistency that agents have complete knowledge of all relevant probability distribution functions and when trying to incorporate learning in these models trying to take the heat of some of the criticism launched against it up to date it is always a very restricted kind of learning that is considered learning where truly unanticipated surprising new things never take place but only rather mechanical updatings increasing the precision of already existing information sets of existing probability functions nothing really new happens in these ergodic models where the statistical representation of learning and information is nothing more than a caricature of what takes place in the real world target system this follows from taking for granted that people s decisions can be portrayed as based on an existing probability distribution which by definition implies the knowledge of every possible event otherwise it is in a strict mathematical statistical sense not really a probability distribution that can be thought of taking place but in the real world it is as shown again and again by behavioral and experimental economics common to mistake a conditional distribution for a probability distribution mistakes that are impossible to make in the kinds of economic analysis built on the rational expectations hypothesis that mainstream economists are such adamant propagators for on average rational expectations agents are always correct but truly new information will not only reduce the estimation error but actually change the entire estimation and hence possibly the decisions made to be truly new information has to be unexpected if not it would simply be inferred from the already existing information set in rational expectations models new information is typically presented as only reducing the estimated parameter variance but if new information means truly new information it actually could increase our uncertainty and variance information set a b a b c truly new information gives birth to new probabilities revised plans and decisions something the rational expectations hypothesis cannot account for with its finite sampling representation of incomplete information in the world of rational expectations learning is like being better and better at reciting the complete works of shakespeare by heart or at hitting the bull s eye when playing dart it presupposes that we have a complete list of the possible states of the world and that by definition mistakes are non systematic which strictly seen follows from the assumption of subjective probability distributions being equal to the objective probability distribution this is a rather uninteresting and trivial kind of learning it is a closed world learning synonymous with improving one s adaptation to a world that is fundamentally unchanging but in real open world situations learning is more often about adapting and trying to cope with genuinely new phenomena the rational expectations hypothesis presumes consistent behavior where expectations do not display any persistent errors in the world of rational expectations we are always on average hitting the bull s eye in the more realistic open systems view there is always the possibility danger of making mistakes that may turn out to be systematic it is because of this presumably...
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