Special Topics in Simultaneous Equations. Back Matter Pages About this book Introduction This book had its conception in in a friendly tavern near the School of Businessand PublicAdministration at the UniversityofMissouri-Columbia. Two of the authors Fomby and Hill were graduate students of the third Johnson , and were and are concerned about teaching econometrics effectively at the graduate level. We decided then to write a book to serve as a comprehensive text for graduate econometrics. Generally, the material included in the bookand itsorganization have been governed by the question, " Howcould the subject be best presented in a graduate class?

The intended purpose has also affected the levelofmathematical rigor. Proofs that would demand inordinant amounts of class time have simply been referenced. The book is intended for a two-semester course and paced to admit more extensive treatment of areas of specific interest to the instructor and students. We have great confidence in the ability, industry, and persistence of graduate students in ferreting out and understanding the omitted proofs and results.

In the end, this is how one gains maturity and a fuller appreciation for the subject in any case. It is assumed that the readers of the book will have had an econometric methods course, using texts like J. Johnston's Econometric Methods, 2nd ed. The fact that the tool can be misused and often has been misused does not change the fact that it is the most important tool we have for advancing knowledge.

- Learning Outcomes?
- Simple Regression.
- Social Emotions in Nature and Artifact?
- Application of Econometric Methods to Various Economic Problems.
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Econometrics does not have to assume linearity, exogeneity of explanatory variables, normal distribution of errors or any of the other assumptions of convenience that may be adopted. Sometimes those assumptions are pernicious, sometimes harmless in context. More to the point all can be tested in any particular case. It is hard to avoid the suspicion that Mr Syll simply does not like confronting numerical data and would rather live in a world of undisciplined speculation.

What Lars Syll points out, we all know. There is nothing new in it. The real issue is to find out the alternative methodology. Perhaps, the contemporary mathematics and statistics are not rich enough to analyze the real phenomena with limited data or if there are any, they are unknown to the economists. We are aware of the problems of covariance among the explanatory variables, but we do not have a dependable method to deal with the problem. We are aware of misspecification problems, but we do not have enough information to specify the model correctly.

We acknowledge nonlinearity and perhaps multiple solutions multiple optima and nonconvexity but we do not have a foolproof method to handle it. We understand the issues of risk and uncertainty, but we do not have methods to deal with them appropriately.

## Studying at York

We understand the problem of non-quantifiability, but we do not have appropriate methods to deal with it. Then, the answer is not to criticise and destroy. The answer is to develop new methods, learn from other disciplines, invent, and do a constructive work. It is much more difficult than simply criticising. I know well what it means, and others too know it. Those of us in the economics community who are impolite enough to dare to question the preferred methods and models applied in mainstream economics and econometrics are as a rule met with disapproval.

Nothing is perfect … The assumptions are reasonable. The biases will cancel. We can model the biases. Now we use more sophisticated techniques. What would you do? You have to do the best you can with the data. You have to make assumptions in order to make progress. You have to give the models the benefit of the doubt.

Lars is absolutely right in pointing out that those who dare question the status quo are met with disapproval, even intimidation and contempt. Well done, Lars—please keep it up. I loved your comment! Richard E.

Planck, M. Firstly, the use of formal and logical modeling of our macroeconomics system has taken us far, unlike the negative claim given above. It has taken us so far that it has strangely managed to show by the use of quite a simple numerical method, that an increment in the taxation of personal incomes has a positive effect on national prosperity, when taken at large, but that when the same sum is collected from the taxation of land values, the benefit is roughly 3 times as big. These facts were first derived in my recent book, which I suggest be taken a bit more seriously.

I can send you an e-copy, so you can check the arithmetic chesterdh hotmail. Secondly any model cannot simply be wrong since it does represent something or some concept that by its nature deals with our subject, and it does have some limited resemblance to it. Thirdly, probability has no place here. We do not model a situation with the belief that it has some probability of being accurate. That is the nature of taking this imaginative methodology.

I support Lars fully. The paper has actually an eye opener in it before the econometricians. Successful economic thoughts have least bothered about the metrics so far. Those who have influenced the transformations in civilizations have seen the past with full devotion, taken insights from there but not got driven by then. Historicity gives an easy pathway towards estimating forward but limits it to the lame contexts of the past and attempts to impose on the future. Thanks Professor R P Banerjee rpbanerjee10 hotmail. I found Mr. I think he could have gone much further. Plutonometrics without compassionate wisdom and vast knowledge of the situation is rational justification of rape, pillage, plunder, normative psychopathology, systemic cultural corruption and the anti-ethical Piracy Paradigm that perpetuates it all.

Clearly, as Syll and others see and understand, any model that fails to closely approximate the actuality of human culture and its activities, despite being officially accepted as a technically valid and useful tool, serves as diversionary, subversive camouflage, obfuscating the nature and purpose of The Plutonomy Game. I am confident that model predicts the outcome of every game, matching the current financial state of the world with exact accuracy.

The only way to make the model more accurate would be to include religious factions, governments, armaments, armies, terrorists, police, multi-national corporations and every kind of psychopathic player available.

### Econometric Methods

It includes new equations and formulas enabling integration of quantitative and qualitative data. It needs corrections and expansion, but you may find it helpful. I agree with several of the assertions of Lars Syll, but it is true that his arguments are a bit of a mixture. Analysis based on econometrics seems to assume s the following, regarding each point: a Impacts are linear or tractable as linear e.

Assumptions a and b are strictly econometric, and are at its core. Assumptions c and d instead are more related to the use of econometric outcomes, both by theoreticians and practitioners. It is not Econometrics, as the Syll rightly points out. Econometrics per se is not able to solve this question, besides the usual calculus of correlations, as causality arguments are embedded in theory, and it is theory that have to decide whether there is or not relationship among two variables.

Theoretically we must allow that the influence of one variable over another may change in its intensity; e. In this point, linearity may be too restrictive. But this is a rather technical issue, that perhaps more sophisticated econometrics will solve in the future: indeed, some non linear specifications are quite usual nowadays. Non linear forms are preferred when a constant elasticity is the desired outcome. It is a deep if solvable epistemological problem to decide whether a process features randomness.

Rolling a dice or throwing a coin — two noteworthy examples of randomness — are not random at all; are mechanical processes, just too complex to be predictable in analytical terms. It seems that there has been a confusion between the random tables o generators used for sampling and the very idea of random variable. There remain c and d ; as told before, these are economic questions, and therefore the answer to them must arise from Economics, not from Econometrics. The clue question is the following: Is Economics able or entitled to find empirical relationships that are transferable through time and context?

The lab in the future or in other place is able to replicate the same processes and outcomes. Syll clearly asserts that this kind of universal statements are precluded for Economics.

## Econometric Techniques

But if this question — to be or not to be a Science — is rather definitional, it is not that important. We all agree that economic analysis is able to deliver useful assertions for the problems it has decided to face at least for the problems that are really economic, i. For example, when Dani Rodrik argues that the Washington Consensus meant wrong policies for Latin America in the Nineties, this assertion is based both on empirical and theoretical grounds, and can be rationalized as such. On the same token, the assertion that car A performs better than car B is not scientific, but anyway the scientist when choosing a car surely pays attention to the opinion of experts and to test drives.

Perhaps, it would be better to keep it in the backstage. We suspect that Economics cannot go much beyond this point.

## Econometrics: Methods and Applications

No more, but not less. Sorry for the length of this comment. This is because Economics, correctly understood, is a science of complexity, with chaotic features, evolving in both time and space in ways that are unpredictable according to conventional statistics and simplistic linear models.

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This just says that economists have been applying the wrong mathematics and have had the wrong expectations. Nonlinear models can tell us a great deal if developed at an appropriate level of abstraction and scale in time and space, but they generally do so via numerical simulations of a variety of scenarios.

Consider the famous limits-to-growth models from the Club of Rome work in the s. These were dismissed by clueless economists, like Nordhaus, but did in fact forewarn us of the dangers of ecological overshoot and collapse that may be starting now business-as-usual scenario or within a few generations more optimistic scenarios. But it is wrong to expect these models to predict a certain probability of collapse at a certain date.

### Econometric Techniques

But each scenario is based on a certain choice of parameters, so assumptions would have to be made on the probability distributions of those parameters. This is guess work at best, so one would have to try a variety of guesses as to the form and parameters of these probability distributions, and then how to weight the different guesses? The whole enterprise begins to get very doubtful. Yet much can be learned about the risks even from a few well chosen scenarios, like the dozen or so computed by the limits-to-growth studies.

Not registered? Sign up. Publications Pages Publications Pages. Search my Subject Specializations: Select Users without a subscription are not able to see the full content. Econometric Methods for Labour Economics Stephen Bazen Abstract This book provides a presentation of the standard statistical techniques used by labour economists. More This book provides a presentation of the standard statistical techniques used by labour economists.

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