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description= 15 posts published by Lars Syll during December 2014;
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Text of the page (random words):
are actually very similar to theoretical models in many ways they e g have the same basic problem that they are built on rather artificial conditions and have difficulties with the trade off between internal and external validity the more artificial conditions the more internal validity but also less external validity the more we rig experiments models to avoid the confounding factors the less the conditions are reminicent of the real target system the nodal issue is how economists using different isolation strategies in different nomological machines attempt to learn about causal relationships i doubt the generalizability of both research strategies because the probability is high that causal mechanisms are different in different contexts and that lack of homogeneity stability invariance doesn t give us warranted export licenses to the real societies or economies if we see experiments as theory tests or models that ultimately aspire to say something about the real target system then the problem of external validity is central assume that you have examined how the work performance of swedish workers a is affected by b treatment how can we extrapolate generalize to new samples outside the original population e g to the uk how do we know that any replication attempt succeeds how do we know when these replicated experimental results can be said to justify inferences made in samples from the original population if for example p a b is the conditional density function for the original sample and we are interested in doing a extrapolative prediction of e p a b how can we know that the new sample s density function is identical with the original unless we can give some really good argument for this being the case inferences built on p a b is not really saying anything on that of the target system s p a b as i see it is this heart of the matter external validity extrapolation generalization is founded on the assumption that we can make inferences based on p a b that is exportable to other populations for which p a b applies sure if one can convincingly show that p and p are similar enough the problems are perhaps not insurmountable but arbitrarily just introducing functional specification restrictions of the type invariance stability homogeneity is at least for an epistemological realist far from satisfactory and often it is unfortunately exactly this that we see when we take part of neoclassical economists models experiments by this i do not mean to say that empirical methods per se are so problematic that they can never be used on the contrary i am basically though not without reservations in favour of the increased use of experiments within economics as an alternative to completely barren bridge less axiomatic deductive theory models my criticism is more about aspiration levels and what we believe we can achieve with our mediational epistemological tools and methods in social sciences many experimentalists claim that it is easy to replicate experiments under different conditions and therefore a fortiori easy to test the robustness of experimental results but is it really that easy if in the example given above we run a test and find that our predictions were not correct what can we conclude the b works in sweden but not in the uk or that b works in a backward agrarian society but not in a post modern service society that b worked in the field study conducted in year 2005 but not in year 2014 population selection is almost never simple had the problem of external validity only been about inference from sample to population this would be no critical problem but the really interesting inferences are those we try to make from specific labs experiments to specific real world situations institutions structures that we are interested in understanding or causally to explain and then the population problem is more difficult to tackle just as traditional neoclassical modelling randomized experiments is basically a deductive method given the assumptions such as manipulability transitivity separability additivity linearity etc these methods deliver deductive inferences the problem of course is that we will never completely know when the assumptions are right real target systems are seldom epistemically isomorphic to our axiomatic deductive models systems and even if they were we still have to argue for the external validity of the conclusions reached from within these epistemically convenient models systems causal evidence generated by randomization procedures may be valid in closed models but what we usually are interested in is causal evidence in the real target system we happen to live in ideally controlled experiments still the benchmark even for natural and quasi experiments tell us with certainty what causes what effects but only given the right closures making appropriate extrapolations from ideal accidental natural or quasi experiments to different settings populations or target systems is not easy it works there is no evidence for it will work here causes deduced in an experimental setting still have to show that they come with an export warrant to the target population system the causal background assumptions made have to be justified and without licenses to export the value of rigorous and precise methods is despairingly small many advocates of randomization and experiments want to have deductively automated answers to fundamental causal questions but to apply thin methods we have to have thick background knowledge of what s going on in the real world and not in ideally controlled experiments conclusions can only be as certain as their premises and that also goes for methods based on randomized experiments the claimed strength of a social experiment relatively to non experimental methods is that few assumptions are required to establish its internal validity in identifying a project s impact the identification is not assumption free people are typically and thankfully free agents who make purposive choices about whether or not they should take up an assigned intervention as is well understood by the randomistas one needs to correct for such selective compliance the randomized assignment is assumed to only affect outcomes through treatment status the exclusion restriction there is another more troubling assumption just under the surface inferences are muddied by the presence of some latent factor unobserved by the evaluator but known to the participant that influences the individual specific impact of the program in question then the standard instrumental variable method for identifying the average treatment effect on the treated is no longer valid even when the instrumental variable is a randomized assignment most social experiments in practice make the implicit and implausible assumption that the program has the same impact for everyone while internal validity is the claimed strength of an experiment its acknowledged weakness is external validity the ability to learn from an evaluation about how the specific intervention will work in other settings and at larger scales the randomistas see themselves as the guys with the lab coats the scientists while other types the policy analysts worry about things like external validity yet it is hard to argue that external validity is less important than internal validity when trying to enhance development effectiveness against poverty nor is external validity any less legitimate as a topic for scientific inquiry martin ravaillon 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 private 25 dec 2014 at 14 38 posted in varia comments off on perfect day private after almost forty years in lund yours truly has returned to the town where he was born and bred malmö taking a stroll in a sunny and snow glittering pildammsparken today further convinced me returning was a good decision credence 25 dec 2014 at 10 13 posted in varia 1 comment statistics in the service of science 24 dec 2014 at 09 50 posted in statistics econometrics comments off on statistics in the service of science a chicken is an egg s way of constructing another egg and empirical research is a scientific theory s way of uncovering the theory s flaws it s harder to make such clear statements about statistics or engineering or computer science as these are essentially tools in the service of science rather than being the object of study themselves and there are fields where new paradigms don t seem so apparent consider three areas with which i m somewhat familiar political science psychology and economics in political science i see persistent difficulties in integrating different perspectives coming from the studies of public opinion institutions and political maneuvering it really feels like we re not seeing the whole elephant at once and i include my own research as an example of this incomplete perspective psychology seems to be undergoing a reforming process in which various unsuccessful paradigms such as embodied cognition are being rejected with no clear unification of the cognitive and behavioral approaches similarly in economics although there it seems worse in that various incomplete perspectives are taken by their proponents as being all encompassing how does statistics fit into all this statistics can potentially do a lot guidance in data collection and the assessment of measurements and recall that data collection is not just about how to collect a random sample or assign treatments in an experiment it also includes considerations of what to measure and how to measure it methods for calibrating variation by comparing to models of randomness this is where i think that statistical significance and p values fit in not as a way to make scientific discoveries p less than 05 so we get published in the tabloids but as a measuring stick when interpreting observed comparisons and variation tools for combining information that to me is the most general way to think of inference and it encompasses all sorts of things from classical iid models to more complicated approaches methods for checking fit for revealing the aspects of data that are not well explained by our models to me this includes all of exploratory data analysis which is about learning the unexpected again statistics is in the service of science and i see statistics as a way of organizing science rather than as a way of making scientific discovery i think most of the real scientific heavy lifting is coming from existing substantive theories the statistics is more of a way of rearranging the data or of adjudicating between competing hypotheses or underlying models of reality andrew gelman share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky boom bust boom 23 dec 2014 at 08 59 posted in economics comments off on boom bust boom h t jan milch invariance and inferring causation from regressions wonkish 20 dec 2014 at 12 20 posted in statistics econometrics 10 comments in order to make causal inferences from simple regression it is now conventional to assume something like the setting in equation 1 the equation makes very strong invariance assumptions which cannot be tested from data on x and y 1 y a bx δ what happens without invariance the answer will be obvious if intervention changes the intercept a the slope b or the mean of the error distribution the impact of the intervention becomes difficult to determine if the variance of the error term is changed the usual confidence intervals lose their meaning how would any of this be possible suppose for instance that unbeknownst to the statistician x and y are both the effects of a common cause operating through linear statistical laws like 1 suppose errors are independent and normal while nature randomizes the common cause to have a normal distribution the scatter diagram will look lovely a regression line is easily fitted and the straightforward causal interpretation will be wrong share on facebook opens in new window facebook share on x opens in new window x share on bluesky opens in new window bluesky new keynesian dsge models irrelevant gadgets 19 dec 2014 at 16 01 posted in economics comments off on new keynesian dsge models irrelevant gadgets there is something about the way macroeconomists construct their models nowadays that obviously doesn t sit right empirical evidence only plays a minor role in neoclassical mainstream economic theory where models largely function as a substitute for empirical evidence one might have hoped that humbled by the manifest failure of its theoretical pretences during the latest economic financial crisis the one sided almost religious insistence on axiomatic deductivist modeling as the only scientific activity worthy of pursuing in economics would give way to methodological pluralism based on ontological considerations rather than formalistic tractability that has so far not happened fortunately when you ve got tired of the kind of macroeconomic apologetics produced by new keynesian macroeconomists and other dsge modellers there still are some real keynesian macroeconomists to read one of them axel leijonhufvud writes for many years now the main alternative to real business cycle theory has been a somewhat loose cluster of models given the label of new keynesian theory new keynesians adhere on the whole to the same dsge modeling technology as rbc macroeconomists but differ in the extent to which they emphasise inflexibilities of prices or other contract terms as sources of shortterm adjustment problems in the economy the new keynesian label refers back to the rigid wages brand of keynesian theory of 40 or 50 years ago except for this stress on inflexibilities this brand of contemporary macroeconomic theory has basically nothing keynesian about it i conclude that dynamic stochastic general equilibrium theory has shown itself an intellectually bankrupt enterprise but this does not mean that we should revert to the old keynesian theory that preceded it or adopt the new keynesian theory that has tried to compete with it what we need to learn from keynes are about how to view our responsibilities and how to approach our subject if macroeconomic models no matter of what ilk build on microfoundational assumptions of representative actors rational expectations market clearing and equilibrium and we know that real people and markets cannot be expected to obey these assumptions the warrants for supposing that conclusions or hypothesis of causally relevant mechanisms or regularities can be bridged are obviously non justifiable incompatibility between actual behaviour and the behaviour in macroeconomic models building on representative actors and rational expectations microfoundations is not a symptom of irrationality it rather shows the futility of trying to represent real world target systems with models flagrantly at odds with real...
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