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description= 15 posts published by Lars Syll during September 2017;
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chosen to evaluate the model is largely arbitrary second for a given set of moments there is no well defined statistic to measure the goodness of fit of a dsge model or to establish what constitutes an improvement in such a framework third the evaluation is complicated by the fact that at some level all economic models are rejected by the data in addition dsge models frequently impose a number of restrictions that are in direct conflict with micro evidence if a model has been rejected along some dimensions then a statistic that measures the goodness of fit along other dimensions is meaningless focusing on the quantitative fit of models also creates powerful incentives for researchers i to introduce elements that bear little resemblance to reality for the sake of achieving a better fit ii to introduce opaque elements that provide the researcher with free or almost free parameters and iii to introduce elements that improve the fit for the reported moments but deteriorate the fit along other unreported dimensions albert einstein observed that not everything that counts can be counted and not everything that can be counted counts dsge models make it easy to offer a wealth of numerical results by following a well defined set of methods that requires one or two years of investment in graduate school but is relatively straightforward to apply thereafter there is a risk for researchers to focus too much on numerical predictions of questionable reliability and relevance that absorb a lot of time and effort rather than focusing on deeper conceptual questions that are of higher relevance for society anton korinek great essay showing that rigorous and precise dsge models cannot be considered anything else than unsubstantiated conjectures as long as they aren t supported by evidence from outside the theory or model to my knowledge no in any way decisive empirical evidence has been presented no matter how precise and rigorous the analysis and no matter how hard one tries to cast the argument in modern mathematical form they do not push economic science forwards one single millimetre if they do not stand the acid test of relevance to the target no matter how clear precise rigorous or certain the inferences delivered inside these models are they do not say anything about real world economies proving things rigorously in dsge models is at most a starting point for doing an interesting and relevant economic analysis forgetting to supply export warrants to the real world makes the analysis an empty exercise in formalism without real scientific value mainstream economists think there is a gain from the dsge style of modelling in its capacity to offer some kind of structure around which to organise discussions to me that sounds more like a religious theoretical methodological dogma where one paradigm rules in divine hegemony that s not progress that s the death of economics as a science as kornik argues using dsge models creates a bias towards models that have a well behaved ergodic steady state since we know that most real world processes do not follow an ergodic distribution this is to say the least problematic to understand real world non routine decisions and unforeseeable changes in behaviour stationary probability distributions are of no avail in a world full of genuine uncertainty where real historical time rules the roost the probabilities that ruled the past are not those that will rule the future imposing invalid probabilistic assumptions on the data make all dsge models statistically misspecified advocates of dsge modelling 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 idealized models conclusions can only be as certain as their premises and that also applies to the quest for causality and forecasting predictability in dsge models if substantive questions about the real world are being posed it is the formalistic mathematical representations utilized that have to match reality not the other way around the modelling convention used when constructing dsge models makes it impossible to fully incorporate things that we know are of paramount importance for understanding modern economies such as income and wealth inequality asymmetrical power relations and information liquidity preference just to mention a few given all these fundamental problems for the use of these models and their underlying methodology it is beyond understanding how the dsge approach has come to be the standard approach in modern macroeconomics dsge models are based on assumptions profoundly at odds with what we know about real world economies that also makes them little more than overconfident story telling devoid of real scientific value macroeconomics would do much better with more substantive diversity and plurality dangers of running with the mainstream pack 18 sep 2017 at 14 43 posted in economics 3 comments an absolutely fabulous speech and soskice and carlin s textbook macroeconomics institutions instability and the financial system that dullien mentions at the beginning of his speech is really a very good example of the problems you run into if you want to be pluralist within the mainstream pack carlin and soskice explicitly adapts a new keynesian framework including price rigidities and adding a financial system to the usual neoclassical macroeconomic set up but although i find things like the latter amendment an improvement it s definitely more difficult to swallow their methodological stance and especially their non problematized acceptance of the need for macroeconomic microfoundations some months ago another sorta kinda new keynesian paul krugman argued on his blog that the problem with the academic profession is that some macroeconomists aren t bothered to actually figure out how the new keynesian model with its euler conditions based on the assumption that people have perfect access to capital markets so that they can borrow and lend at the same rate really works according to krugman this shouldn t be hard at all at least it shouldn t be for anyone with a graduate training in economics carlin soskice seem to share krugman s attitude from the first page of the book they start to elaborate their preferred 3 equations new keynesian macromodel and after twenty two pages they have already come to specifying the demand side with the help of the permanent income hypothesis and its euler equations but if people not the representative agent at least sometimes can t help being off their labour supply curve as in the real world then what are these hordes of euler equations that you find ad nauseam in these new keynesian macromodels gonna help us yours truly s doubts regarding the new keynesian modellers obsession with euler equations is basically that as with so many other assumptions in modern macroeconomics the euler equations don t fit reality all empirical sciences use simplifying or unrealistic assumptions in their modelling activities that is not the issue as long as the assumptions made are not unrealistic in the wrong way or for the wrong reasons if we cannot show that the mechanisms or causes we isolate and handle in our models are stable in the sense that what when we export them from our models to our target systems they do not change from one situation to another then they only hold under ceteris paribus conditions and a fortiori are of limited value for our understanding explanation and prediction of our real world target system no matter how many convoluted refinements of concepts made in the model if the successive approximations do not result in models similar to reality in the appropriate respects such as structure isomorphism etc the surrogate system becomes a substitute system that does not bridge to the world but rather misses its target from this methodological perspective yours truly has to conclude that carlin s and soskice s microfounded macroeconomic model is a rather unimpressive attempt at legitimizing using fictitious idealizations such as euler equations for reasons more to do with model tractability than with a genuine interest in understanding and explaining features of real economies running with the mainstream pack is not a good idea if you want to develop realist and relevant economics putting predictions to the test 17 sep 2017 at 11 58 posted in economics 1 comment it is the somewhat gratifying lesson of philip tetlock s new book that people who make prediction their business people who appear as experts on television get quoted in newspaper articles advise governments and businesses and participate in punditry roundtables are no better than the rest of us when they re wrong they re rarely held accountable and they rarely admit it either they insist that they were just off on timing or blindsided by an improbable event or almost right or wrong for the right reasons they have the same repertoire of self justifications that everyone has and are no more inclined than anyone else to revise their beliefs about the way the world works or ought to work just because they made a mistake no one is paying you for your gratuitous opinions about other people but the experts are being paid and tetlock claims that the better known and more frequently quoted they are the less reliable their guesses about the future are likely to be the accuracy of an expert s predictions actually has an inverse relationship to his or her self confidence renown and beyond a certain point depth of knowledge people who follow current events by reading the papers and newsmagazines regularly can guess what is likely to happen about as accurately as the specialists whom the papers quote our system of expertise is completely inside out it rewards bad judgments over good ones the new yorker mainstream neoclassical economists often maintain usually referring to the methodological individualism of milton friedman that it doesn t matter if the assumptions of the models they use are realistic or not what matters is if the predictions are right or not but if so then the only conclusion we can make is throw away the garbage because oh dear oh dear how wrong they have been when simon potter a couple of years ago analyzed the predictions that the federal reserve bank of new york did on the development of real gdp and unemployment for the years 2007 2010 it turned out that the predictions were wrong with respectively 5 9 and 4 4 which is equivalent to 6 millions of unemployed economic forecasters never expect to predict precisely one way of measuring the accuracy of their forecasts is against previous forecast errors when judged by forecast error performance metrics from the macroeconomic quiescent period that many economists have labeled the great moderation the new york fed research staff forecasts as well as most private sector forecasts for real activity before the great recession look unusually far off the mark using a similar approach to reifschneider and tulip but including forecast errors for 2007 one would have expected that 70 percent of the time the unemployment rate in the fourth quarter of 2009 should have been within 0 7 percentage point of a forecast made in april 2008 the actual forecast error was 4 4 percentage points equivalent to an unexpected increase of over 6 million in the number of unemployed workers under the erroneous assumption that the 70 percent projection error band was based on a normal distribution this would have been a 6 standard deviation error a very unlikely occurrence indeed in other words the rigorous and precise macroeconomic mathematical statistical forecasting models were wrong and the rest of us have to pay potter is not the only one who lately has criticized the forecasting business john mingers comes to essentially the same conclusion when scrutinizing it from a somewhat more theoretical angle it is clearly the case that experienced modellers could easily come up with significantly different models based on the same set of data thus undermining claims to researcher independent objectivity this has been demonstrated empirically by magnus and morgan 1999 who conducted an experiment in which an apprentice had to try to replicate the analysis of a dataset that might have been carried out by three different experts leamer sims and hendry following their published guidance in all cases the results were different from each other and different from that which would have been produced by the expert thus demonstrating the importance of tacit knowledge in statistical analysis the empirical and theoretical evidence is clear predictions and forecasts are inherently difficult to make in a socio economic domain where genuine uncertainty and unknown unknowns often rule the roost the real processes that underly the time series that economists use to make their predictions and forecasts do not conform with the assumptions made in the applied statistical and econometric models much less is a fortiori predictable than standardly and uncritically assumed the forecasting models fail to a large extent because the kind of uncertainty that faces humans and societies actually makes the models strictly seen inapplicable the future is inherently unknowable and using statistics econometrics decision theory or game theory does not in the least overcome this ontological fact the economic future is not something that we normally can predict in advance better then to accept that as a rule we simply do not know in new york state section 899 of the code of criminal procedure provides that persons pretending to forecast the future shall be considered disorderly under subdivision 3 section 901 of the code and liable to a fine of 250 and or six months in prison although the law does not apply to ecclesiastical bodies acting in good faith and without fees i m not sure where that leaves macroeconomic model builders and other forecasters the accuracy of the predictions that experts make certainly seem to have an inverse relationship to their self confidence being cocky and wrong is a lethal combination and economists are often wrong and hardly known for being particularly modest people the growth of the internet will slow drastically as the flaw in metcalfe s law which states that the number of potential connections in a network is proportional to the square of the number of participants becomes apparent most people have nothing to say to each other by 2005 or so it will become clear that the internet s impact on the economy has been no greater than the fax machine s paul krugman sign your name 16 sep 2017 at 22 09 posted in varia comments off on sign your name nights in white satin 16 sep 2017 at 19 22 posted in varia comments off on nights in white satin economic growth and gender 16 sep 2017 at 17 59 posted in economics 2 comments the economic implications of gender discrimination are most serious to deny women is ...
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