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led technical assumptions that actually make the testing results and the inferences we build on them unwarranted haavelmo s probabilistic revolution gave econometricians their basic framework for testing economic hypotheses it still builds on the assumption that the hypotheses can be treated as hypotheses about joint probability distributions and that economic variables can be treated as if pulled out of an urn as a random sample but as far as i can see economic variables are nothing of that kind to be able to draw causal inferences from data econometricians have to use mathematical statistical models but to be useful one also has to go beyond econometric analysis and show that the inferences made in the models also apply to and are compatible with the economic reality the targeted economic phenomena of interest behind the data the data generating process simply performing an empirical test of an economic model is like playing tennis with the net down all economic data are models the ultimate purpose of econometric analysis should therefore not be to test a model as such but to help us assess and evaluate the extent to which the model is compatible with the economic phenomena we are interested in trying to explain running a standard econometric test without verifying whether the model s assumptions match the actual economic process makes it far too easy to claim success the test may give statistically significant results but those results risk being little more than an artefact of the model s own structure not a genuine confrontation with reality data are never raw unfiltered facts they are already shaped by definitions measurement procedures sampling methods imputations and classification choices all of which embody theoretical assumptions gdp figures and unemployment rates are never simply observed they are constructed according to specific conventions when testing an economic model using such data economists are in effect testing one set of modelling assumptions against another set of assumptions we need to ask given what we know about how the data were produced how much confidence can we place in the model s ability to capture the target economic phenomena to answer that we have to use institutional knowledge and historical context the standard econometric practice taking a given model running it on available data and reporting whether the coefficients are statistically significant or whether the model fits according to some goodness of fit measure treats the model as if it were the object of interest in itself but a model is never the final object of interest the real economy is moving beyond testing models as such means treating econometrics as a tool for learning about real world economic mechanisms the net must be raised the purpose of econometric work should shift towards using data to learn how economic processes actually behave including their institutional specificities and historical contingencies otherwise we are merely playing tennis with the net down share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky how to do econometrics properly 14 apr 2026 at 15 49 posted in statistics econometrics comments off on how to do econometrics properly always but always plot your data remember that data quality is at least as important as data quantity always ask yourself do these results make economic common sense check whether your statistically significant results are also numerically economically significant be sure that you know exactly what assumptions are used needed to obtain the results relating to the properties of any estimator or test that you use just because someone else has used a particular approach to analyse a problem that looks like yours that doesn t mean they were right test test test david hendry but don t forget that pre testing raises some important issues of its own don t assume that the computer code that someone gives to you is relevant for your application or that it even produces correct results keep in mind that published results will represent only a fraction of the results that the author obtained but is not publishing don t forget that peer reviewed does not mean correct results or even best practices were followed dave giles nowadays it has almost become a self evident truism among economists that you cannot expect people to take your arguments seriously unless they are based on or backed up by advanced econometric modelling so legions of mathematical statistical theorems are proved and heaps of fiction are produced masquerading as science the rigour of the econometric modelling and the far reaching assumptions on which they are built are frequently not supported by data modelling assumptions made in statistics and econometrics are more often than not made for reasons of mathematical tractability rather than verisimilitude that is unfortunately also a reason why the methodological rigour encountered when taking part in statistical and econometric research is to a large degree nothing but a deceptive appearance the models constructed may seem technically advanced and very sophisticated but that is usually only because the problems discussed here have been swept under the carpet assuming that our data are generated by coin flips in an imaginary superpopulation only means that we get answers to questions that we are not asking share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky limits of econometric testing 2 apr 2026 at 00 13 posted in statistics econometrics 1 comment econometricians are of course well aware of the importance of the relationship between the data and the underlying phenomena of interest in the literature this relationship is generally couched in terms of a data generating process dgp if we were to be able to perceive the true dgp in its entirety we would essentially know the complete underlying structure whose observable precipitates are the data our only evidence of the dgp however is the data it is important to note however that characterizing pieces of the data generating process is an intra model activity it reveals the possible mathematical structure underlying a matrix of numbers and it is properly judged according to and only according to the relevant rules of mathematics in contrast the requirement that a relation of homomorphism exist between the data and the underlying phenomena is concerned with the relationship between model and target entities the extent to which data satisfy this requirement in any given case cannot be determined through econometric analysis nor does econometric analysis obviate the need to establish that the requirement is met on the contrary the results of an econometric analysis of a given data set i e the characterization of a piece of its dgp can be validly interpreted as providing epistemic access to the target only if it is plausible that a relation of homomorphism holds between the data and the aspects of the target they ostensibly represent econometrics is supposed to be able to test economic theories but to serve as a testing device one has to make several assumptions many of which cannot be tested or verified to make things worse there are also rarely strong and reliable ways of telling us which set of assumptions is preferred trying to test and infer causality from data we have to rely on assumptions such as disturbance terms being independent and identically distributed functions being additive linear and with constant coefficients parameters being invariant under intervention variables being exogenous structural and so on unfortunately we are seldom or never informed where that kind of knowledge comes from beyond referring to the economic theory that one is supposed to test that leaves us in the awkward position of admitting that if the assumptions made do not hold the inferences conclusions and testing outcomes econometricians come up with simply do not follow from the data and statistics they use the central question is how do we learn from empirical data but we have to remember that the value of testing hinges on our ability to validate the often unarticulated assumptions on which the testing models build if the model is wrong the test apparatus simply gives us fictional values there is always a risk that one turns a blind eye to some of those unfulfilled technical assumptions that actually make the testing results and the inferences we build on them unwarranted econometric testing standardly builds on the assumption that hypotheses can be treated as hypotheses about joint probability distributions and that economic variables can be treated as if pulled out of an urn as a random sample most economic phenomena are nothing of the kind most users of the econometric toolbox seem to have a built in blindness to the fact that mathematical statistical modelling in the social sciences is inherently incomplete since it builds on the presupposition that the model properties are without serious argumentation or warrant assumed to apply also to the intended real world target systems studied many of the processes and structures that we know play essential roles in the target systems do not show up in the models often for reasons of mathematical statistical tractability the bridge between model and reality is failing valid and relevant information is unrecognised and lost making the models harmfully misleading and largely irrelevant if our goal is to learn explain or understand anything about actual economies and societies without strong evidence for an essential compatibility between model and reality the analysis becomes nothing but a fictitious storytelling of questionable scientific value it is difficult to find any hard evidence that econometric testing has been able to exclude any economic theory if we are to judge econometrics on its capacity to eliminate invalid theories it has not been a very successful business share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky how to make progress in quantitative analysis 22 mar 2026 at 22 04 posted in statistics econometrics comments off on how to make progress in quantitative analysis important questions often require a leap from the empirical evidence to the theoretical claim sociology stands out from other social sciences for its willingness to tackle hard questions even when they require such a leap however burying the estimand obscures those decisions and confuses the link between theory and evidence at best this creates an uncomfortable ambiguity about the author s intentions at worst it can mislead rather than a call for sociologists to narrow their ambitions our framework is a call for sociologists to be explicit about the goals that motivate their projects and transparent about the assumptions needed to believe them a paper that develops a compelling theoretical estimand but relies on less than perfect identification assumptions should be recognized for making an important contribution it sets the stage for future work to explore that theoretical estimand under different identification assumptions while it may be simpler obfuscation of the true goal does not make an argument more compelling if we want to make progress on big theoretical questions we should begin every quantitative analysis with a question that makes its purpose precise what is the estimand ian lundberg rebecca johnson brandon stewart as forcefully argued by the authors quantitative research often suffers from a fundamental oversight researchers devote excessive attention to how to estimate while neglecting to specify what is being estimated every serious study has to begin with defining the precise theoretical quantity of interest the estimand without a clearly stated estimand one cannot judge whether the chosen methods are fit for purpose leaving the study s conclusions utterly ambiguous share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky why your dates are attractive jerks 14 mar 2026 at 19 18 posted in statistics econometrics 1 comment a helpful intuition for understanding collider bias the spurious correlation induced when one controls for a common effect rather than a common cause can be found in modern dating behaviour in a population at large it is reasonable to assume that attractiveness and personality are independent mean people are not necessarily attractive nor are nice people however once we shift our focus from the general population to the specific subset of people who are currently dating we are conditioning on a collider the choice to date someone is caused by both attractiveness and personality within the dating group a false trade off emerges if an individual has a mean personality the reason they might still be dating is probably their attractiveness conversely if someone is unattractive they must have a nice personality to be successful daters looking only at the dating pool one would conclude that there is a negative correlation between the two traits guessing that mean people are attractive and that unattractive people are nice this simple analogy should serve as a powerful warning for economists in recent decades the discipline has developed a tendency to address the problem of omitted variable bias by adding as many controls as possible to a regression the logic seems sound if leaving a variable out biases your estimate putting it in should fix it this mechanical approach however crucially depends on the type of variable being added controlling for a confounder a variable that causes both the treatment and the outcome removes bias but controlling for a collider a variable that is caused by both the treatment and the outcome creates it when economists add variables that may themselves be outcomes of the processes they are studying they risk opening up back door paths and creating correlations that have no basis in reality reducing complex research questions to manageable statistical hypotheses is a difficult art and the assumption that one can simply treat real world processes as if they were random statistical experiments is a phantasmagoric fantasy if our goal is a relevant and realistic science we must remember that more controls are not always better sometimes they are the very source of the illusion share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky how not to make valid causal inferences 14 mar 2026 at 14 08 posted in statistics econometrics comments off on how not to make valid causal inferences traditionally analysts use data on stopped individuals to study bias by computing the difference in violence rates between stopped minority and white civilians while controlling for observable differences between these two 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