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description=15 posts published by Lars Syll during March 2025;

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för (11), 2011 (10), theory (10), could (10), between (10), can (10), statistical (10), true (10), some (10), society (9), their (9), till (9), really (9), our (9), variables (9), will (8), only (8), also (8), problem (8), eine (8), most (8), these (8), econometric (8), rather (8), hope (8), probabilistic (8), view (7), already (7), other (7), politics (7), who (7), modern (7), cannot (7), which (7), mmt (7), was (7), sich (7), man (7), regression (7), give (7), just (7), approach (7), level (7), econometricians (7), modeller (7), prognoser (7), kan (7), eller (7), 2026 (6), science (6), statistics (6), like (6), being (6), student (6), ekonomiska (6), want (6), all (6), knowledge (6), those (6), wenn (6), study (6), when (6), relations (6), mechanisms (6), them (6), assumptions (6), stable (6), were (6), truth (6), make (6), kind (6), måste (6), vad (6), does (5), stuff (5), social (5), system (5), possible (5), trump (5), uncertainty (5), wie (5), mainstream (5), investitionen (5), 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y can only be found at the level of the individual since individual agents are seen as the ultimate determinants of the variables in the economic system but why the logically conceivable really should turn out to be the case is difficult to see at least if we are not satisfied with sheer hope as we have already noted keynes reacted against using unargued for and unjustified assumptions of complex structures in an open system being reducible to those of individuals in real economies it is unlikely that we find many autonomous relations and events and one could of course with keynes and from a critical realist point of view also raise the objection that to invoke a probabilistic approach to econometrics presupposes e g that we have to be able to describe the world in terms of risk rather than genuine uncertainty and that is exactly what haavelmo 1944 48 does to make this a rational problem of statistical inference we have to start out by an axiom postulating that every set of observable variables has associated with it one particular true but unknown probability law but to use this trick of our own and just assign a certain probability law to a system of observable variables however cannot just as little as hoping build a firm bridge between model and reality treating phenomena as if they essentially were stochastic processes is not the same as showing that they essentially are stochastic processes as hicks 1979 120 21 so neatly puts it things become more difficult when we turn to time series the econometrist who works in that field may claim that he is not treading on very shaky ground but if one asks him what he is really doing he will not find it easy even here to give a convincing answer h e must be treating the observations known to him as a sample of a larger population but what population i am bold enough to conclude from these considerations that the usefulness of statistical or stochastic methods in economics is a good deal less than is now conventionally supposed we have no business to turn to them automatically we should always ask ourselves before we apply them whether they are appropriate to the problem in hand and as if this wasn t enough one could also seriously wonder what kind of populations these statistical and econometric models ultimately are based on why should we as social scientists and not as pure mathematicians working with formal axiomatic systems without the urge to confront our models with real target systems unquestioningly accept haavelmo s infinite population fisher s hypothetical infinite population von mises s collective or gibbs s ensemble of course one could treat our observational or experimental data as random samples from real populations i have no problem with that but modern probabilistic econometrics does not content itself with that kind of population instead it creates imaginary populations of parallel universes and assumes that our data are random samples from that kind of population but this is actually nothing else but handwaving and it is inadequate for real science as david freedman writes in statistical models and causal inference with this approach the investigator does not explicitly define a population that could in principle be studied with unlimited resources of time and money the investigator merely assumes that such a population exists in some ill defined sense and there is a further assumption that the data set being analyzed can be treated as if it were based on a random sample from the assumed population these are convenient fictions nevertheless reliance on imaginary populations is widespread indeed regression models are commonly used to analyze convenience samples the rhetoric of imaginary populations is seductive because it seems to free the investigator from the necessity of understanding how data were generated econometricians should know better than to treat random variables probabilities and expected values as anything else than things that are strictly seen only pertain to statistical models if they want us to take the leap of faith from mathematics into the empirical world in applying the theory they have to really argue and justify this leap by showing that those neat mathematical assumptions that to make things worse often are left implicit as e g independence and additivity do not collide with the ugly reality the set of mathematical assumptions is no validation in itself of the adequacy of the application rigour and elegance in the analysis do not make up for the gap between reality and model it is the distribution of the phenomena in itself and not its estimation that ought to be at the centre of the stage a crucial ingredient to any economic theory that wants to use probabilistic models should be a convincing argument for the view that there can be no harm in considering economic variables as stochastic variables haavelmo 1943 13 in most cases no such arguments are given of course you are entitled like haavelmo and his modern probabilistic followers to express a hope at a metaphysical level that there are invariant features of reality to uncover and that also show up at the empirical level of observations as some kind of regularities but is it a justifiable hope i have serious doubts the kind of regularities you may hope to find in society is not to be found in the domain of surface phenomena but rather at the level of causal mechanisms powers and capacities persistence and generality have to be looked out for at an underlying deep level most econometricians do not want to visit that playground they are content with setting up theoretical models that give us correlations and eventually mimic existing causal properties we have to accept that reality has no correct representation in an economic or econometric model there is no such thing as a true model that can capture an open complex and contextual system in a set of equations with parameters stable over space and time and exhibiting invariant regularities to just believe hope or assume that such a model possibly could exist is not enough it has to be justified in relation to the ontological conditions of social reality in contrast to those who want to give up on fallible transient and transformable truth as a relation between theory and reality and content themselves with truth as a relation between a model and a probability distribution i think it is better to really scrutinize if this latter attitude is feasible to abandon the quest for truth and replace it with sheer positivism would indeed be a sad fate of econometrics it is more rewarding to stick to the truth as a regulatory ideal and keep on searching for theories and models that in relevant and adequate ways express those parts of reality we want to describe and explain econometrics may be an informative tool for research but if its practitioners 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 i remain a sceptic of the pretences and aspirations of econometrics so far i cannot really see that it has yielded very much in terms of relevant interesting economic knowledge the marginal return on its ever higher technical sophistication in no way makes up for the lack of serious under labouring of its deeper philosophical and methodological foundations that already keynes complained about the rather one sided emphasis of usefulness and its concomitant instrumentalist justification cannot hide that neither haavelmo nor the legions of probabilistic econometricians following in his footsteps give supportive evidence for their considering it fruitful to believe in the possibility of treating unique economic data as the observable results of random drawings from an imaginary sampling of an imaginary population after having analyzed some of its ontological and epistemological foundations i cannot but conclude that econometrics on the whole has not delivered truth and i doubt if it has ever been the intention of its main protagonists our admiration for technical virtuosity should not blind us to the fact that we have to have a more cautious attitude towards probabilistic inference of causality in economic contexts science should help us penetrate the true process of causation lying behind current events and disclose the causal forces behind the apparent facts keynes 1971 89 vol xvii 427 we should look out for causal relations but econometrics can never be more than a starting point in that endeavour since econometric statistical explanations are not explanations in terms of mechanisms powers capacities or causes firmly stuck in an empiricist tradition econometrics is only concerned with the measurable aspects of reality but there is always the possibility that there are other variables of vital importance and although perhaps unobservable and non additive not necessarily epistemologically inaccessible that were not considered for the model those who were can hence never be guaranteed to be more than potential causes and not real causes a rigorous application of econometric methods in economics really presupposes that the phenomena of our real world economies are ruled by stable causal relations between variables a perusal of the leading econom etr ic journals shows that most econometricians still concentrate on fixed parameter models and that parameter values estimated in specific spatio temporal contexts are presupposed to be exportable to totally different contexts to warrant this assumption one however has to convincingly establish that the targeted acting causes are stable and invariant so that they maintain their parametric status after the bridging the endemic lack of predictive success of the econometric project indicates that this hope of finding fixed parameters is a hope for which there really is no other ground than hope itself this is a more fundamental and radical problem than the celebrated lucas critique has suggested this is not the question if deep parameters absent on the macro level exist in tastes and technology on the micro level it goes deeper real world social systems are not governed by stable causal mechanisms or capacities it is the criticism that keynes 1951 1926 232 33 first launched against econometrics and inferential statistics already in the 1920s the atomic hypothesis which has worked so splendidly in physics breaks down in psychics we are faced at every turn with the problems of organic unity of discreteness of discontinuity the whole is not equal to the sum of the parts comparisons of quantity fails us small changes produce large effects the assumptions of a uniform and homogeneous continuum are not satisfied thus the results of mathematical psychics turn out to be derivative not fundamental indexes not measurements first approximations at the best and fallible indexes dubious approximations at that with much doubt added as to what if anything they are indexes or approximations of the kinds of laws and relations that econom etr ics 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 economic theoretical modelling rather useless references freedman david 2010 statistical models and causal inference cambridge cambridge university press haavelmo trygve 1943 statistical testing of business cycle theories the review of economics and statistics 25 13 18 1944 the probability approach in econometrics supplement to econometrica 12 1 115 hicks john 1979 causality in economics london basil blackwell keynes john maynard 1951 1926 essays in biography london rupert hart davis 1971 89 the collected writings of john maynard keynes vol i xxx d e moggridge e a g robinson eds london macmillan share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky riksbanken tänker till 27 mar 2025 at 18 26 posted in economics 1 comment wooh vem hade trott det verkligen imponerande årets nobelpristagare i ekonomi share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window 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on dynamic and static interpretations of regression coefficients when econometric and statistical textbooks present simple and multiple regression analysis for cross sectional data they often do it with regressions like regress test score y on study hours x and get the result y constant slope coefficient x error term when speaking of increases or decreases in x in these interpretations we have to remember that it is a question of cross sectional data and increases which means that we are referring to increases in the value of a variable from one unit in the population to another unit in the same population strictly it is only admissible to give slope coefficients a dynamic interpretation when dealing with time series regression for cross sectional data we should stick to static interpretations and look upon slope coefficients as giving information about what we can expect to happen to the value of the dependent variable when there is a change in the independent variable from one unit to another although it is tempt...