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description= 15 posts published by Lars Syll during March 2025;
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the (206), and (128), that (87), not (46), share (45), opens (45), new (45), window (45), och (44), are (44), for (36), mar (32), att (32), som (30), bluesky (30), facebook (30), with (29), 2025 (28), may (24), #econometrics (23), but (23), this (22), models (22), they (20), die (20), der (20), comments (19), jan (19), det (19), have (18), inte (17), den (17), from (17), med (17), apr (16), jun (16), posted (16), und (16), jul (15), aug (15), sep (15), oct (15), nov (15), dec (15), feb (15), one (15), its (15), har (15), has (14), 2022 (13), economics (13), dass (13), ist (13), das (13), 2012 (12), 2013 (12), 2014 (12), 2015 (12), 2016 (12), 2017 (12), 2018 (12), 2019 (12), 2020 (12), 2021 (12), 2023 (12), 2024 (12), model (12), than (12), real (12), more (12), world (12), should (12), population (12), economic (12), reality (12), lönebildning (11), arbetslöshet (11), time (11), what (11), keynes (11), off (11), out (11), causal (11), data (11), there (11), haavelmo (11), för (11), lars (10), 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), arbetslöshetssiffran (5), styr (5), does (5), stuff (5), social (5), system (5), possible (5), trump (5), uncertainty (5), wie (5), 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att föreläsa på min kurs finanskriser orsaker förlopp och konsekvenser enligt alf fanns det ingen anledning att vara speciellt förvånad över ekonomernas usla prognoser vad är värst en dålig prognos eller ingen prognos svaret är enkelt så fort du exponeras för en prognos är du i en sämre position än du var innan expertprognoser gör med all sannolikhet mer skada än nytta det är därför det lönar sig att snabbt bläddra förbi tidningsartiklar med rubriker som så kommer börsen gå i år tänk dig att du har som jobb att sköta ditt företags valutaväxlingar du måste bestämma att antingen säkra växelkursen redan nu eller vänta tills beloppet anländer och växla till den kurs som gäller då som tur är har du analytikernas dollarprognoser till hjälp de gör det inte ett dugg lättare att förutspå dollarkursen men de kan hjälpa dig ändå om du lyckas göra rätt spelar inte analyserna någon större roll men om dollarn faller som en sten och du har valt att inte säkra växelkursen kommer företagsledningen att vilja veta varför du har sumpat bort företagets pengar du kan dra en lång historia om historiska valutatrender ekonomisk tillväxt betalningbalans och ränteskillnader till slut kommer alla att hålla med om att du agerade rätt mot bakgrund av den information du hade på förhand analyserna gör att du kommer undan särskilt de som hade mest fel prognoserna har inget ekonomiskt värde vare sig för företaget eller samhället värdet är att de räddar ditt skinn med andra ord nationalekonomerna med sina rigorösa och precisa matematisk statistiska ekonometriska modeller har genomgående fel och det är alla vi andra som får betala för det ska vi kunna återskapa förtroendet för den nationalekonomiska vetenskapen måste vi sluta låtsas som om vi har exakta och riktiga svar på allting för det har vi inte vi bygger modeller och teorier där vi på hundradelars procent kan tala om vad värdet på räntan kommer att vara om tio år vi kan kalkylera investeringsrisker och göra exakta framtidsprognoser men detta bygger vi på matematiska och statistiska antaganden som ofta har väldigt lite med verkligheten att göra genom att inte låtsas om den avgörande skillnaden mellan modell och verklighet invaggar vi människor i en falsk trygghet av att vi har koll på läget det har vi inte det är denna falska trygghet som aktivt medverkar till återkommande finanskriser vi måste våga erkänna att vi ibland inte vet allt och att det finns saker som vi inte ens vet att vi inte vet all osäkerhet går inte att reducera till kalkylerbar risk på det ekonomisk politiska området måste vi sluta upp med att bygga modeller och prognoser på fullständigt verklighetsfrånvända modeller med robotliknande optimerande människor utrustade med rationella förväntningar detta är ren och skär nonsens vi måste bygga våra modeller på antaganden som inte står i uppenbar kontrast till verkligheten och i det arbetet måste vi vara ödmjuka nog att också inse att psykologi och andra beteendevetenskaper kan bidra med viktiga pusselbitar it is my view that most individuals underestimate the uncertainty of the world this is almost as true of economists and other specialists as it is of the lay public to me our knowledge of the way things work in society or in nature comes trailing clouds of vagueness experience during world war ii as a weather forecaster added the news that the natural world is also unpredictable an incident illustrates both uncertainty and the unwillingness to entertain it some of my colleagues had the responsibility of preparing long range weather forecasts i e for the following month the statisticians among us subjected these forecasts to verification and found they differed in no way from chance the forecasters themselves were convinced and requested that the forecasts be discontinued the reply read approximately like this the commanding general is well aware that the forecasts are no good however he needs them for planning purposes kenneth arrow 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 critical realist critique 28 mar 2025 at 22 30 posted in statistics econometrics 2 comments mainstream economists often hold the view that criticisms of econometrics are the conclusions of sadly misinformed and misguided people who dislike and do not understand much of it this is really a gross misapprehension to be careful and cautious is not the same as to dislike any perusal of the mathematical statistical and philosophical works of nancy cartwright chris chatfield hugo keuzenkamp john maynard keynes tony lawson asad zaman aris spanos duo qin and david freedman would show that the critique is put forward by respected authorities i would argue against common knowledge that they do not misunderstand the crucial issues at stake in the development of econometrics quite the contrary they just as yours truly know them all too well and are not satisfied with the validity and philosophical underpinning of the assumptions made for applying its methods let me try to do justice to the critical arguments on the logic of probabilistic induction and shortly elaborate mostly from a philosophy of science vantage point on some insights critical realism gives us on econometrics and its methodological foundations the methodological difference between an empiricist and a deductivist approach can also clearly be seen in econometrics the ordinary deductivist textbook approach views the modelling process as foremost an estimation problem since one at least implicitly assumes that the model provided by economic theory is a well specified and true model the more empiricist general to specific methodology often identified as the lse approach on the other hand views models as theoretically and empirically adequate representations approximations of a data generating process dgp diagnostics tests mostly some variant of the f test are used to ensure that the models are true or at least congruent representations of the dgp the modelling process is here more seen as a specification problem where poor diagnostics results may indicate possible misspecification requiring re specification of the model the objective is standardly to identify models that are structurally stable and valid across a large time space horizon the dgp is not seen as something we already know but rather something we discover in the process of modelling it considerable effort is put into testing to what extent the models are structurally stable and generalizable over space and time although i have sympathy for this approach in general there are still some unsolved problematics with its epistemological and ontological presuppositions there is e g an implicit assumption that the dgp fundamentally has an invariant property and that models that are structurally unstable just have not been able to get hold of that invariance but as already keynes maintained one cannot just presuppose or take for granted that kind of invariance it has to be argued and justified grounds have to be given for viewing reality as satisfying conditions of model closure it is as if the lack of closure that shows up in the form of structurally unstable models somehow could be solved by searching for more autonomous and invariable atomic uniformity but if reality is congruent to this analytical prerequisite has to be argued for and not simply taken for granted even granted that closures come in degrees we should not compromise on ontology some methods simply introduce improper closures closures that make the disjuncture between models and real world target systems inappropriately large garbage in garbage out underlying the search for these immutable fundamentals lays the implicit view of the world as consisting of material entities with their own separate and invariable effects these entities are thought of as being able to be treated as separate and addible causes thereby making it possible to infer complex interaction from knowledge of individual constituents with limited independent variety but again if this is a justified analytical procedure cannot be answered without confronting it with the nature of the objects the models are supposed to describe explain or predict keynes himself thought it generally inappropriate to apply the atomic hypothesis to such an open and organic entity as the real world as far as i can see these are still appropriate strictures all econometric approaches have to face grounds for believing otherwise have to be provided by the econometricians trygve haavelmo the father of modern probabilistic econometrics wrote that he and other econometricians could not build a complete bridge between our models and reality by logical operations alone but finally had to make a non logical jump 1943 15 a part of that jump consisted in that econometricians like to believe that the various a priori possible sequences would somehow cluster around some typical time shapes which if we knew them could be used for prediction 1943 16 but since we do not know the true distribution one has to look for the mechanisms processes that might rule the data and that hopefully persist so that predictions may be made of possible hypotheses on different time sequences samples in haavelmo s somewhat idiosyncratic vocabulary most had to be ruled out a priori by economic theory although one shall always remain in doubt as to the possibility of some outside hypothesis being the true one 1943 18 to haavelmo and his modern followers econometrics is not really in the truth business the explanations we can give of economic relations and structures based on econometric models are not hidden truths to be discovered but rather our own artificial inventions models are consequently perceived not as true representations of dgp but rather instrumentally conceived as if constructs their intrinsic closure is realized by searching for parameters showing a great degree of invariance or relative autonomy and the extrinsic closure by hoping that the practically decisive explanatory variables are relatively few so that one may proceed as if natural limitations of the number of relevant factors exist haavelmo 1944 29 haavelmo seems to believe that persistence and autonomy 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 shou...
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