The Keynes-Tinbergen Debate on the Relevance of Estimating Econometric Models for Policy Analysis11/9/2013
Tinbergen’s motivation and basic approach to econometrics The desire to combat the socio-economic consequences of the Great Depression of the 1930s was Tinbergen's motivation for using econometric modeling. His approach towards studying periodic economic upswings and downswings contrasted with previous approaches to business cycle research. After a 19th-century undertaking by Juglar (1862) ascribing the recurrent business crises in Europe and North America to credit crises, and Jevons's (1884) study pointing to agricultural production cycles connected with sunspot numbers, several research projects in the early 20th century were devoted to the construction of so-called business cycle barometers. The purpose was to measure economic fluctuations through a particular index (or set of indices) with the aim of giving warning signals for turning points that would lead to a depression. An example was the Harvard Index of Business Conditions, known as the Harvard Barometer, constructed by a team led by Persons (1919). Another well-known descriptive approach to the business cycle during this period was initiated by Mitchell (1913). Mitchell’s work was followed by that of Yule (1927) and Slutzky (1927), who suggested that the cumulative effect of random shocks could be the cause of cyclical patterns in economic variables. Frisch (1933) applied these ideas, introducing econometric models in which impulse propagation mechanisms led to business cycles. However useful it could be as a starting point, Tinbergen criticized descriptive analysis as being too vague for use in policy preparation, and started a quantitatively oriented research program to explore the possible economic causes of the periodic upswings and downswings in economic activity. In an earlier theoretical study, Aftalion (1927) had argued that lags in an economic model could generate cyclical variation in economic activity. Following this argument, Tinbergen specified a first simple case using a system of difference equations to express lagged responses of supply to price changes in a market for a single good. He noted that the systematic fluctuations that could arise in such a system had been observed in an empirical study of the pork market by the German economist Hanau (1928), a phenomenon that became known as the ‘cobweb model’. Tinbergen subsequently generalized the specification of dynamic equations with lagged adjustment processes to macroeconomic settings, arguing that fluctuations in components of national product, such as investment and consumption expenditures, would lead to business cycle fluctuations in general economic activity. In 1936 he published the first applied macroeconometric model for the Netherlands. It was a dynamic model consisting of 22 equations in 31 variables. Employing what we now see as basic statistical techniques like correlation and regression analysis, it was to be used for the analysis of the particularly pressing unemployment problem. The specification of consumption and employment in this model anticipated elements of Keynes's theory (1936). This modeling exercise resulted in a strong policy recommendation in favour of a devaluation of the Dutch guilder to tackle unemployment. But its importance for the economics profession was far more profound: for the first time, the economic-policy debate had been based on empirically tested, quantitative economic analysis and not on rather informally stated economic theory, the so-called verbal approach. Thus, according to Solow (2004, p. 159), Tinbergen's work during this period ‘was a major force in the transformation of economics from a discursive discipline into a model-building discipline’. The Keynes-Tinbergen Debate The formulation of certain relations in Tinbergen's 1936 model showed some resemblance to Keynes's theory. Nevertheless, in an article in the Economic Journal of 1939, Keynes was remarkably skeptical of Tinbergen's work. Keynes labeled Tinbergen's method of estimating the parameters of an econometric model and computing quantitative policy scenarios as ‘statistical alchemy’, arguing that this approach ‘… is a means of giving quantitative precision to what, in qualitative terms, we know already as the result of a complete theoretical analysis’ (Keynes, 1939, p. 560). Their widely diverging views on the relevance of quantitative economic analysis were also illustrated by Keynes's reaction to Tinbergen's estimate of the price elasticity of demand for exports. When, in 1919, Keynes had strongly criticized the excessive war indemnity payments enforced upon Germany after the First World War, his argument had depended critically on the value of this elasticity. Tinbergen empirically found this value to be minus 2, precisely the value that Keynes had assumed a priori in his study. When informed about this Keynes replied: ‘How nice that you found the correct figure’. Keynes' critical attitude towards macroeconometric modeling and analysis originated from his view that the underlying economic theory should be complete in the sense that it should include all relevant variables and set out in detail its causal and dynamic structure. Econometrics could be used only for measuring relations (‘curve fitting’ was the term used); it could not refute economic hypotheses or evaluate economic models. Tinbergen, on the other hand, argued that economic theories cannot be complete. Econometric research could be useful for scrutinizing elements of economic theories and for examining whether one theory describes reality better than another. Further, it could provide the numerical values of the coefficients in dynamic models that determine the cyclical and stability properties of the model, and, by applying a testing procedure of trial and error, it could yield suggestions for an improved specification of dynamic lags. The debate still goes on In this controversy Tinbergen's approach soon gained the upper hand as increasing numbers of economists, especially in the United States, noted its practical results in terms of model construction and verification, including forecasting and policy recommendation in particular for monetary policy. However, Keynes's comments on the role of expectations and uncertainty in macro-econometrics and on specification and simultaneous equation biases remained relevant. Haavelmo (1943) advocated the use of probability theory in bridging the gap between theory and data in business cycle analysis. Later these issues would become the subject of intensive debate and research. The pioneering work by Thomas Sargent and Christopher Sims, Nobel Laureates in Economics in 2011, to construct models that both fit the data and can be used for forecasting and policy is a clear example of the continuing debate. Discussing their contributions is beyond the scope of the present note. I only mention that forecast and policy implications based on their modeling and inference techniques are studied and used by almost all econometricians at the US Federal Reserve System and at European Central Banks. Personal note I met Jan Tinbergen late in his life when I was director of the Tinbergen Institute, www.tinbergen.nl , from 1992-1998 and after his death from 2008-2010. A brief story of my personal experience with Tinbergen shows his interest in that empirical econometric work which may have enormous potential policy implications. In 1994, just a few months before he died, he read my paper on the bimodal distribution of the World’s Income and the very low estimates of the catch-up probability of poor countries with the rich ones, see Paap and Van Dijk (EER, 1998). He saw it as a testimony to his efforts to make development programming of poor countries an important area of theoretical and practical research and he invited me (with my wife) to discuss in more detail my paper on a Saturday afternoon in the Spring of 1994. Regrettably he passed away on the Monday before our meeting, but it shows how actively involved he was in following empirical econometric research that has substantial policy implications until the very last days of his life. This note is an adjusted excerpt from the paper: “Tinbergen, Jan (1903–1994)." By Cornelisse, Peter A. and Herman K. van Dijk, in The New Palgrave Dictionary of Economics. Second Edition. Eds. Steven N. Durlauf and Lawrence E. Blume. Palgrave Macmillan, 2008. The New Palgrave Dictionary of Economics Online. Palgrave Macmillan. 25 March 2008 <http://www.dictionaryofeconomics.com/article?id=pde2008_T000065> doi:10.1057/9780230226203.1710. References can be found in that paper. Selected papers Keynes, J.M. 1939. Professor Tinbergen's method. Economic Journal 49, 558–68. Keynes, J.M. 1940. Comment. Economic Journal 50, 154–6. Tinbergen J., 1939. Statistical Testing of Business Cycle Theories. I: A Method and Its Application to Investment Activity. II: Business Cycles in the United States of America,1919–1932. Geneva: League of Nations. Tinbergen J., 1940. On a method of statistical business cycle research. A reply. Economic Journal 50, 141–54.
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Introduction to the interview with Eric Maskin by Jean Tirole I’m very pleased that The TSEconomist chose to interview Eric Maskin, who in turn kindly accepted to participate. Eric Maskin is a role model for scholarship. There is no point reviewing here his breakthrough contributions to the economics of incentives; they are well-known and have been deservedly recognized by the Economics Nobel Prize Committee. Let me just say a few words about a slightly less well-known side of Eric. As all those who have met him will confirm, he is a very modest person; he is also a very good citizen and generous with his time. A superb advisor, he has mentored generations of students at MIT, Harvard and Princeton and has changed the path of their careers. I had the great opportunity to be one of his first students- along with my MIT classmate Drew Fudenberg. Extrapolating on our own experience, I can conjecture that his extraordinary track record with students is due to three factors. First, he never insists on his students embarking on his own research topics. He is intellectually curious and will get interested even in subjects lying clearly outside his area of expertise (at least when the student starts working on them- quickly Eric becomes an expert...). If a research path will in his opinion lead to a dead-end or to an unambitious outcome, he will say so. But otherwise he will be strongly supportive, regardless of the topic. As he rightly argues in his TSEconomist interview: “I think the most important element in producing a successful Ph.D. dissertation is being really interested in the questions you are trying to answer. If you truly care about the subject, you have the incentive to immerse yourself in it – and that’s what leads to a good dissertation. In fact, this is probably the most important element in producing good research more generally.” By being supportive of the student’s own interests, Eric makes it more likely that the student succeeds in his/her dissertation and start of career. I should mention two other factors, both related to the personal traits mentioned above. First, he is very generous with his time. I still fondly remember the tutorials Drew Fudenberg and I had with Eric Maskin in his MIT office. He would discuss with us the yet unpublished papers of now classic work by for instance Myerson, Myerson-Satterthwaite, Green-Laffont, Milgrom-Roberts, Kreps- Wilson and (as we asked him) his own work by himself or with Jean-Jacques Laffont, John Riley, Peter Diamond or Partha Dasgupta. This was just extraordinary; we just saw the field of economics change in real time and were getting insights as to what the shortages of the new approaches (game theory, information economics) and areas for future research were. This is what my TSE colleagues try to emulate today by covering recent developments in an enthusiastic manner. Eric’s second trait that makes the PhD process under his supervision both enjoyable and productive is his graciousness. Working on a PhD thesis generates anxiety; regardless of previously demonstrated talent, performing research is for most PhD students a jump into the unknown and there are always many moments of doubt, when one wonders if one is able to fulfill expectations. Self-confidence is with passion a key to delivering good research; but unlike passion, self-confidence is fragile and does not come by easily. Eric has always been a gentle supervisor. While he clearly indicates to the student unpromising research paths or wrong reasoning, he always does so in a soft, constructive manner. Let me give you an anecdote, which probably Eric does not recall (and that I almost successfully repressed myself). In my second year of the PhD program, I had the great opportunity to work on a joint paper with him on Keynesian equilibria – this was the beginning of a long collaboration (indeed in the following year, while I was still an MIT student, we started working on the various papers on Markov perfect equilibria – the pure game – theory piece as well as the three IO applications). One day, while he was away at a conference, I added a proposition to the paper. Alas, this proof of the proposition contained a fateful mistake. When returning, Eric quickly figured out the mistake, but very graciously and in a soft-spoken way tried to find redeeming features (honestly, there were none, the mistake was rather stupid), and went on to provide an alternative proof. While embarrassing for me, this episode certainly taught me a lesson. Research is a complex alchemy, in which sheer analytical power is just one of many ingredients. Another ingredient is the ability to alternate between two opposite advocacy roles: wishful thinking- hoping that a proof or idea will work, sometimes against all odds, and not hesitating to take shortcuts before writing things more carefully – and critical examination – submitting the outcome to the toughest refereeing process possible. Yet another absolutely key factor is passion, as described in the interview. And finally there is a large payoff to being in the right place with the right people. Eric Maskin is certainly one of these scholars who always make the investigation intellectually challenging and at the same time much fun, as it should! Interview with Eric S. Maskin: Questions by TSE students Eric Maskin is Adams University Professor at Harvard. He received the 2007 Nobel Memorial Prize in Economics (with L. Hurwicz and R. Myerson) for laying the foundations of mechanism design theory. He also has made contributions to game theory, contract theory, social choice theory, political economy, and other areas of economics. He received his B.A. and Ph.D from Harvard and was a postdoctoral fellow at Jesus College, Cambridge University. He was a faculty member at MIT from 1977-1984, Harvard from 1985-2000, and the Institute for Advanced Study from 2000-2011. He rejoined the Harvard faculty in 2012. • Life and the Nobel Prize Why did you decide to become an economist given that you were trained as a mathematician? How much investment in math would you recommend to Ph.D. students interested in economic theory in general but without any math background? It’s true that my initial training was in mathematics. However, almost by accident, I happened to take a course from Kenneth Arrow on “Information Economics,” which was so inspiring that I decided to change direction. It seemed to me that economics combined the best of both worlds: the rigor of mathematics with the immediate relevance of a social science. As for how much math I would recommend, I’d say that basic analysis, including measure theory, is certainly very useful. Also, linear algebra and stochastic processes always helps. But beyond that, I don’t think a huge mathematical investment is necessary to do economic theory unless you are planning to work in an extremely technical area. What were your immediate reactions and thoughts when you were informed that you won the Nobel Prize? How did being a Nobel laureate affect your life? My first reaction was great surprise and a sense of unreality – but also pleasure, especially from the fact that Leo Hurwicz and Roger Myerson were being recognized too. For the most part, the prize hasn’t changed my life a great deal, but it has given me the opportunity to visit some places I wouldn’t have otherwise seen and meet some people I wouldn’t have otherwise come across. It has also given me the chance to speak to a much broader audience. What was the feeling of living in the Princeton house in which Albert Einstein spent the last 19 years of his life? What do you think is the most remarkable element of his work and his legacy? It was certainly a thrill to live in Einstein’s old house; he has always been one of my heroes. For me, his most remarkable accomplishment is to show how far we can go in understanding the world through pure thought alone. You come from a musical family: your mother taught at Juilliard and your brother is a professional oboist. As a matter of fact, you turn out to be a first-rate musician yourself. You play the piano as well as the clarinet, and perform regularly in concerts, mainly classical music, but also jazz. Did your involvement with music affect your research life? Did you ever regret that you did not follow the family tradition in the choice of your profession? Music has helped provide some balance to my life. I love doing research, but must acknowledge that writing an economics paper does not allow me to express my emotions very much. Playing music, by contrast, gives me a rich emotional outlet, which is very satisfying. I don’t regret not becoming a professional musician. As it is, I have the best of both worlds – I can do economics and play music on the side (perhaps not as often as I’d like, but still quite a bit). It would be very hard for a professional musician to do economics on the side. • About Crises and Economic Theory The recent financial crisis revealed that markets do not work so perfectly as some policy makers had thought. What were the main reasons for which policy makers and politicians were so confident about the performance of financial markets and rejected the call for more stringent regulation? Can such behavior be justified by realistic economic theoretical models? What do you think are the main lessons that we should keep in mind for the future? I don’t really understand why politicians and policy makers had such faith in the self-regulation of financial markets – perhaps ideology had something to do with it. Certainly, such faith was not based on a good understanding of economic theory, which shows very clearly how financial markets can fail because of serious externalities. I hope that we remember going forward that financial stability depends on correcting these externalities, and that a good way of doing so is regulation – especially, regulation of leverage. How do you comment on the recent policy decisions of the EU, like the outright monetary transactions of the ECB, the austerity packages implemented in many European countries and the intensive discussions around the issue of Eurobonds? Do you believe that the currently applied rescue plans in these countries are incentive compatible or they leave room for misbehavior? I think the ECB’s accommodating monetary policy has been helpful, but I’m afraid that austerity programs have largely proved to be counterproductive; they were implemented at time when economies were still fragile, and have tended to make things worse. I applaud the idea of Eurobonds and other moves to integrate Europe’s fiscal side. Of course, rescue plans create a moral hazard problem, which is why the fiscal consolidation of Europe (including the power of a central authority to constrain member countries’ domestic spending) is so important. Do you think that the Euro as currency can survive the exit of one of the small and peripheral countries from the Eurozone (say Greece for example)? Could it survive the exit of Germany? Can the consequences of such events be really predicted? I’m no expert on European politics, but I’d guess that the Euro might survive the exit of Greece, but not the exit of France or Germany. The honest answer, though, is that these are just wild guesses. I suspect that even experts on the subject can’t do much better than guess. • Patent Protection and Innovation Your opinion that patents can inhibit innovation in particular in industries like the software industry seems at first sight to contradict the conventional wisdom that patent protection leads to more innovations. Do you think that we should weaken the patent protection in particular industries in order to improve social welfare? There are indeed industries in which relaxing patent protection might be good for innovation and society. This is true especially of industries – like software – in which invention is highly sequential; where instead of there being one big discovery, there are lots of little discoveries, each building on what has been done before. In such industries, patents can block critical follow-on innovation. You may want to build on a discovery I’ve made. But if I hold a patent on that discovery, I am apt – as a monopolist – to set a high license fee, which may well deter you from innovating. • Our final question: You have contented yourself with your research, but have been a fantastic advisor as well. What do you think are the main ingredients of a successful Ph.D. dissertation? I think the most important element in producing a successful Ph.D. dissertation is being really interested in the questions you are trying to answer. If you truly care about the subject, you have the incentive to immerse yourself in it – and that’s what leads to a good dissertation. In fact, this is probably the most important element in producing good research more generally. |
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