What is Cliometrics ?

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Part I - What is Cliometrics ?

The aim of this note is to arrive at precise notions concerning the subject-matter of cliometrics.

Cliometric analysis is foremost a theoretical approach. Great emphasis is placed on developing a coherent and consistent theoretical model that will provide the basis for interpreting historical economic and social phenomena. Cliometric models enable a better understanding of the real world. Because economic and social processes are complex, a thorough understanding of the underlying forces and interrelationships is generally impossible. Models break up phenomena into more manageable portions by abstracting those variables that are believed to be a significant influence on choice and subjecting them to deductive reasoning based on a set of accepted axioms. Logical conclusions are then derived which must be translated into propositions about the real world. These propositions or predictions must then be compared to actual behavior and experience, either by observation or statistical methods.

In cliometrics there are two modes of discourse : positive (what is) and normative (what ought to be). To this I would add a third : descriptive analysis. The success of positive, normative and descriptive theory are to be judged by different criteria and hence they are not susceptible to the same criticism. Critics of the cliometric approach often confuse these three modes of analysis resulting in much ill conceived criticism. Here a very simplified account of cliometric methodology is given.

Positive cliometrics

Positive cliometrics is the empirical branch of the discipline. I seeks to generate a set of testable, that is potentially refutable, predictions than can be verified by the empirical evidence. A positive cliometric model is a meaningful model if it is both correct and useful. It is correct if it is internally consistent; it is useful if it focuses on a significant influence on choice. A meaningful model is thus one that generates predictions to which behavior conforms more frequently that those generated by some alternative competing theory. If the model is successful in predicting, then the negative judgment can be made that the model has not been falsified.

Positive cliometric analysis is used to make qualitative predictions and to organize data for the testing of these predictions. It is predictive and empirical. The predictions of positive cliometric models must be interpreted with some care. First, such models only establish partial relationships. For example, one of the most common predictions in cliometrics is the inverse relationship between the price of a good and the quantity demanded. However, this statement must be read with an important caveat the caveat of ceteris paribus. The prediction states that in practice the quantity demanded will increase as price increase only if all other factors affecting demand such as income, tastes, and the relative prices of other goods remain constant. Thus the predictions of positive cliometric models are in the nature of conditional statements if A then B, given C, but may B never be observed to occur because other influences on A (C) have also changed. Secondly, since positive cliometric models only deal with partial relationships they do not imply that other factors, economic and non economic, are of no, or less, importance in explaining behavior. A cliometrician may argue, for example, that people will respond to cost-pressures (such as liability for damages) in the care they take in an activity which places safety of others at risk, and he may empirically establish this proposition. But this finding does not naturally lead to the conclusion that pecuniary incentives are the only, or even the best, means of achieving an increase in the level of safety.

Normative cliometrics

Normative cliometrics is the ethical branch of cliometrics concerned with efficiency, distributive and social justice and prescribing corrective measures to improve social welfare. Normative propositions cannot, as a matter of fact, be verified, nor are they in principle verifiable. Value judgments cannot be tested to see whether they are true or false; they are acceptable or unacceptable.

Descriptive cliometrics

Much cliometric analysis does not fall neatly into either positive or normative categories. Indeed the bulk of cliometrics is not designed to generate testable predictions nor to determine the social desirability of a set of policies. It is abstract theory of economic and social problems whose function is to generate logical deductions and to describe economic and social phenomena in a historical perspective. In this perspective, it is useful to distinguish a third (though not exhaustive) category of cliometric analysis descriptive cliometrics. Descriptive cliometrics attempts to model/analyse historical processes and to describe the economic and social influences that affect them. It is thus based on assumptions that are more or less realistic and therefore subject to empirical verification. Much of the literature by cliometricians falls into this category. It seeks to provide a comprehensive model of history based on economics and econometrics.

Although there are a number of historical economic schools the dominant approach used in cliometrics is neoclassical economics. Here the main ingredients of this approach are outlined.

Methodological Individualism

Neoclassical economics builds on the postulate of methodological individualism the view that social theories must be based on the attitudes and behavior of individuals. The basic unit of analysis is thus the rational individual and the behavior of groups is assumed to be the outcome of the decisions taken by the individuals who compose them. Neoclassical economics is, in other words, individualistic economics based on the behavioral model of rational choice.

Maximization Principle

Economic man or woman is assumed to be a self-interested egotist who maximizes his utility. The assumptions of utility (and profit) maximization, or economic rationality as it is sometimes referred to, have given rise to much criticism and confusion. When an economist says that an individual is acting rationally or maximizing his utility he is saying no more than that the individual is making consistent choices, and that he chooses the preferred alternative(s) from those available to him. That is, it is assumed the individual is the best judge of his own welfare the notion of consumer sovereignty. The economic approach does not content that all individuals are rational nor that these assumptions are necessarily realistic. Rather economic man or woman is some weighted average of the individuals under study in which the extremes in behavior are evened out. The theory allows for irrationality but argues that groups of individuals behave as if their members are rational. Also the utility maximizing postulate does not asset that individuals consciously calculate the cost and benefits of all actions, only that their behavior can be explained as if they did so.

Market and Prices

The final ingredient of the cliometric approach concerns the concepts of a market and price. Even in areas where there is not an explicit market, the cliometric approach will often study the subject by analogy with the market concepts of supply, demand and price.

Claude DIEBOLT, for AFC.
In Autumn 2002.

 

Part II - What is retrospective growth accounting ?

Checking theoretical hypotheses on economic growth requires always new statistical sets. The method of quantitative history launched in the 1960s by S. Kuznets, J. Marczewski and F. Perroux is essential for reaching this aim. It consists of assembling historical facts in homogenous, comparable time units in order to measure changes in intervals of time (generally annually). The advantage of this method is that the moment of operation of the observer's choice is shifted. Instead of acting while observing the reality to be described, he operates during the construction of the reference system serving in the recording of facts rendered conceptually homogeneous. This methodology should allow for an empirical verification or the rejection of initial hypotheses hinged on the pattern of theoretical interpretation.

However, a map is not the area it plots, and care must be taken not to confuse reality and its description. The approach is not applicable to isolated historical events. It is used to describe the history of the masses, but is not sensitive to the history of the heroes. It is only an image of reality and does not draw all of reality's contours. Quantitative history does not aim at replacing traditional descriptive history. On the contrary, the two forms of historical investigation are strictly complementary and hence indispensable for a better knowledge of the past. The application of quantitative methods can nevertheless profoundly renew the terms that have progressively become legitimate. The great merit of statistical formalism is that it allows for the examination of the logical and quantitative consequences of historical proposals, which could not be obtained by a discussion based on literary documents and the like.

The proposed method has two advantages. Firstly, it is of immediate practical interest as it is an original reconstruction of the process of growth. It is also of theoretical interest as it provides better knowledge of the mechanisms governing the long development of the economic system. We are nevertheless aware that the statistical work provides only the quantitative aspect of changes in structure. Although it is important, it is not sufficient to provide the complete picture of the sequence of facts.

The decisive role of deductive theories in empirical research must therefore be stressed. Starting with a general idea, they attempt at identifying symptoms in reality by means of a chronological series of statistics. Like the Frankfurter Gesellschaft für Konjunkturforschung (founded in 1926 by E. Altschul), we recommend an economic semiology that would implement the results of statistical research and deductive reasoning of the theory. There is no conflict or compartmentalisation between empirical research and deductive theory; on the contrary, these disciplines are only truly valuable individually if they draw on each other's results. The validity of the theoretical hypotheses thus depends both on their external coherence, i.e. on their conformity with real facts, and their internal coherence, i.e. their ability to provide one or more solutions to the raised questions. The general equilibrium theory is extremely significant in this respect. It has given rise to much work concerning the validity of its bases and of the teachings drawn from it. In contrast, research on the existence of a solution has been carried out by only a handful of specialists. Here, it is still possible to discuss the coherence of a theoretical hypothesis, but it is more important to submit it to empirical verification.

1. The scope of quantitative history

Quantitative history aims at drawing up a general macroscopic synthesis by constructing models integrated at the national level with the prospect of possible links between several national models. This requires a succession of research operations, the most important of which is the creation of chronological data series covering the whole of the period under consideration.

The initial work consists of sorting dozens or even hundreds of volumes of old statistics. The figures are transcribed and reclassified using a pre-established model (e.g. using national budget items). This meticulous research is a question of doing rather than learning and is carried out in the silence of archives and libraries. The classifications used in statistical documents change according to the year and the recording administration. It is necessary at all times to exert some judgement to decide on the matching of the original item and the item under which the figure is to be transcribed.

2. The quantitative history method

The quantitative history method applies when a large number of causes and their complexity and intermingling make the use of experimental methods impossible. The moment at which the observer's choice intervenes is shifted. Instead of being set during observation of the reality to be described, it applies during the elaboration of the system of reference used to count facts thus rendered conceptually homogeneous.

The definition just given essentially consists of three main phases:

• collection of documents;
• analysis of the data collected;
• interpretation of the results.

The first phase is mainly descriptive in order to prepare the real work of the researcher by co-ordinating the collected information. It is a preliminary task required for a serious analysis. All the documents related to the field of study are assembled. The data from archived documents must not be merely recorded, but have to be subjected to intelligent, perspicacious criticism. Some are discarded when considered unreliable; others must be corrected when their interpretation reveals sources of error. Finally, estimates may be required to fill gaps. The final results are assembled in a statistical table.

Having arrived at this mass of numerical information, the researcher must then put the figures in a logical order and classify them according to a previously established nomenclature. The data are reduced, substituted by a small number of representative series drawn by calculation from the ordered mass of numerical data.

The third and final interpretation phase consists of drawing conclusions from the analysis. It is a decisive stage as here an attempt is made to explain the observations. There is a vast scope of interpretative possibilities, ranging from merely checking hypotheses to forecasting future developments.

One has to be aware that the conclusions drawn from the analysis of the statistical sets contain a degree of uncertainty. Also the degree of the validity of the general observations has to be considered. Quantitative history is a long, slow process that is never free from difficulties. There are hesitations to overcome and problems to solve at every stage, as each new research operation is a new beginning and there are decisions that can only be taken alone.

Claude DIEBOLT, for AFC.
In Autumn 2002.

 

 

Part III - What is Convergence ?

The notions of rate of convergence (ß) and evolution of dispersion (a) have been widely used in recent studies of economic growth. The notion of ß-convergence refers to the rate at which the income or production per capita of a poor region tends to catch up that of a rich region. In other words, ß-convergence is observed if the initially poor economic units in a set of cross-sectional data tend to grow more rapidly than the rich units. If the ß coefficient is calculated without taking into account the characteristics that determine the levels of long-term equilibrium of economies (such as the savings ratio, technologies and institutions), it is absolute convergence. The conditional convergence hypothesis is then applied when differences in long-term equilibrium values are taken into account. Two economies may thus have different savings ratios, reflecting differences in the time preference rates. In this case, the traditional neoclassical framework forecasts that the two economies should display the same rate of equilibrium growth but that the economy with the highest savings ratio would display a higher income per capita in a stationary state. In this respect, the conditional convergence notion refers to the hypothesis according to which the economy that is initially the farthest from its own equilibrium trajectory will experience more rapid growth.

Estimation of the absolute convergence coefficient is performed using a non-linear regression based on cross-sectional data in the following form:

 

in which t and T are respectively the first and last years of the observation period and i is an economic unit. Y is the per capita economic unit and u a remainder. The left-hand side of the equation is therefore an approximation of the annual growth of the economic unit i between years t and T. In the context of an analysis of absolute convergence, B is a constant for all the economic units. Its value is determined by the growth of the economic indicators at equilibrium. Furthermore, the coefficient (1-e-ß(T-t))/(T-t) makes it possible to allow for the share of growth that can be accounted for by the initial level of the economic indicator. The gap between the various economic units decreases exponentially at rate ß from t=0 to T. A situation of convergence (a positive ß coefficient) implies a negative relation between the average growth rate during the observation period and the logarithm of the initial level of the economic indicator per capita. The greater the value of ß, the more the economic indicator per capita in the poor region will rapidly approach the level observed in the rich region.

The second convergence notion, referred to as a convergence, is based on the analysis of time series. It is hinged on the movement of a dispersion index of an economic indicator per capita. The dispersion index can be measured in several ways. The standard deviation of the log of income per capita is often used. If the dispersion of income per capita between a set of economies tends to decrease in time, a convergence is observed. In practice, a convergence is observed if the dispersion index time series is integrated of order one and displays a negative drift (a stochastic trend) or evolves with a decreasing trend (a deterministic trend).

In conclusion, it can be said that the notions of ß and a convergence are interdependent. The catching up process (ß convergence) contributes to reducing the cross-sectional dispersion of income (a convergence), but exogenous disturbances to relative growth rates tend to accentuate dispersion.

Claude DIEBOLT, for AFC.
In Autumn 2002.

 

 

Part IV - A New View of Evolutionary Economics, Game Theory and Cliometrics

Since the publication in 1982 of Nelson and Winter's work An Evolutionary Theory of Economic Change, the evolutionary approach to economic and social phenomena is claimed in an increasing number of works and by an increasing number of authors. The early 1990s thus marked the establishment of a scientific community centred on evolutionary principles and a journal (the Journal of Evolutionary Economics was founded in 1991). This reveals deep dissatisfaction with neoclassical thinking which, with the conjunctural change in the early 1970s, displayed its inability to make a pertinent appraisal of the structural crisis affecting the economic and social system.

In fact, economic analyses couched in evolutionary terms result from a fundamental criticism of the general equilibrium theory. The criticism was itself inspected by a set of previous questions mainly concerning aspects of the coherence of the dominant thinking and its methodological and epistemological foundations. Thus, for the partisans of the approach, the general equilibrium theory is unable to describe the economic movement. Both structural change phenomena and crisis situations in the system are denied or eliminated. In consequence, the present slump is handling in an indirect manner because, in a pure economic model, all markets balance out and disturbances can only be a violation of the basic hypotheses. The view proposed is that of a stable, timeless economic system, that is to say in which the essential of any possibility of dynamics and historical change is rejected. Furthermore, the economic system is perceived as an enclosed domain isolated from the social field. The general equilibrium theory thus gives a poor description of the complex social issues that build up the conflict processes that occur during economic development. In fact, in the dominant reasoning, the relations established by the market between supposedly autonomous units is sufficient to ensure the overall coherence of individual actions. The latter is made possible by competitive reasoning reduced to commercial relations that are in turn regulated by a price system.

The ambition of the evolutionary project is therefore to develop a new interpretation of the dynamics of the socio-economic system, that is to say to implement a real change in the way in which the economy is viewed. The evolutionary theory is essentially multiform. It is a theory of change. It is in the Schumpeterian tradition and aimed at interpreting the long movements that affect economic and social activity.

The foundation of the evolutionary approach lies in the analogy with the principles of the biological theories of evolution. In economics, one could summarise the point of departure as follows: a varied set of individuals compete for a vital, rare resource. The nature of the environment determines the qualities required in individuals in order to succeed in this competition. The individuals that have most developed these qualities increase their viability and they reproduction is enhanced, whereas, in contrast, the individuals who have least developed them are threatened. The aggregate characteristics of the population evolve endogenously through this selection mechanism. The process is maintained continuously by endogenous and exogenous changes in the selection criterion and by the mutations of certain individuals, recreating microscopic heterogeneity and possibly forming the vectors of new characteristics with an advantage for selection.

These principles also represent the fundamentals of evolutionary game theory. Following on from the survey performed by A. GREIF in 1996, their application to cliometrics seem most stimulating, on the one hand for renewing analysis of strategic interactions and on the other for the comparative study of growth paths in the nineteenth and twentieth centuries. This being said, before going on to this doubtless important but above all joint stage, we first seek to review the literature, that is to say present the genesis and development of evolutionary game research.

Claude DIEBOLT, for AFC. 
In Autumn 2003.

Economic Model

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A diagram of the IS/LM model

In economics, a model is a theoretical construct that represents economic processes by a set of variables and a set of logical and/or quantitative relationships between them. The economic model is a simplified framework designed to illustrate complex processes, often but not always using mathematical techniques. Frequently, economic models posit structural parameters. Structural parameters are underlying parameters in a model or class of models.^[1] A model may have various parameters and those parameters may change to create various properties. Methodological uses of models include investigation, theorizing, fitting theories to the world.^[2]

Contents  [hide]  1 Overview 2 Types of models 2.1 Quantitative vs. Qualitative models 3 Pitfalls 3.1 Restrictive, unrealistic assumptions 3.2 Omitted details 3.3 Are economic models falsifiable? 4 History 5 Tests of macroeconomic predictions 5.1 Comparison with models in other sciences 5.2 The effects of deterministic chaos on economic models 5.3 The critique of hubris in planning 6 Examples of economic models 7 See also 8 Notes 9 References 10 External links

[edit] Overview

In general terms, economic models have two functions: first as a simplification of and abstraction from observed data, and second as a means of selection of data based on a paradigm of econometric study.
Simplification is particularly important for economics given the enormous complexity of economic processes. This complexity can be attributed to the diversity of factors that determine economic activity; these factors include: individual and cooperative decision processes, resource limitations, environmental and geographical constraints, institutional and legal requirements and purely random fluctuations. Economists therefore must make a reasoned choice of which variables and which relationships between these variables are relevant and which ways of analyzing and presenting this information are useful.
Selection is important because the nature of an economic model will often determine what facts will be looked at, and how they will be compiled. For example inflation is a general economic concept, but to measure inflation requires a model of behavior, so that an economist can differentiate between real changes in price, and changes in price which are to be attributed to inflation.
In addition to their professional academic interest, the use of models include:

• Forecasting economic activity in a way in which conclusions are logically related to assumptions;
• Proposing economic policy to modify future economic activity;
• Presenting reasoned arguments to politically justify economic policy at the national level, to explain and influence company strategy at the level of the firm, or to provide intelligent advice for household economic decisions at the level of households.
• Planning and allocation, in the case of centrally planned economies, and on a smaller scale in logistics and management of businesses.
• In finance predictive models have been used since the 1980s for trading (investment, and speculation), for example emerging market bonds were often traded based on economic models predicting the growth of the developing nation issuing them. Since the 1990s many long-term risk management models have incorporated economic relationships between simulated variables in an attempt to detect high-exposure future scenarios (often through a Monte Carlo method).

A model establishes an argumentative framework for applying logic and mathematics that can be independently discussed and tested and that can be applied in various instances. Policies and arguments that rely on economic models have a clear basis for soundness, namely the validity of the supporting model.
Economic models in current use do not pretend to be theories of everything economic; any such pretensions would immediately be thwarted by computational infeasibility and the paucity of theories for most types of economic behavior. Therefore conclusions drawn from models will be approximate representations of economic facts. However, properly constructed models can remove extraneous information and isolate useful approximations of key relationships. In this way more can be understood about the relationships in question than by trying to understand the entire economic process.
The details of model construction vary with type of model and its application, but a generic process can be identified. Generally any modelling process has two steps: generating a model, then checking the model for accuracy (sometimes called diagnostics). The diagnostic step is important because a model is only useful to the extent that it accurately mirrors the relationships that it purports to describe. Creating and diagnosing a model is frequently an iterative process in which the model is modified (and hopefully improved) with each iteration of diagnosis and respecification. Once a satisfactory model is found, it should be double checked by applying it to a different data set.

[edit] Types of models

According to whether all the model variables are deterministic, economic models can be classified as stochastic or non-stochastic models; according to whether all the variables are quantitative, economic models are classified as discrete or continuous choice model; according to the model's intended purpose/function, it can be classified as quantitative or qualitative; according to the model's ambit, it can be classified as a general equilibrium model, a partial equilibrium model, or even a non-equilibrium model; according to the economic agent's characteristics, models can be classified as rational agent models, representative agent models etc.

• Stochastic models are formulated using stochastic processes. They model economically observable values over time. Most of econometrics is based on statistics to formulate and test hypotheses about these processes or estimate parameters for them. A widely used class of econometric models popularized by Tinbergen and later Wold are autoregressive models, in which the stochastic process satisfies some relation between current and past values. Examples of these are autoregressive moving average models and related ones such as autoregressive conditional heteroskedasticity (ARCH) and GARCH models for the modelling of heteroskedasticity.

• Non-stochastic mathematical models may be purely qualitative (for example, models involved in some aspect of social choice theory) or quantitative (involving rationalization of financial variables, for example with hyperbolic coordinates, and/or specific forms of functional relationships between variables). In some cases economic predictions of a model merely assert the direction of movement of economic variables, and so the functional relationships are used only in a qualitative sense: for example, if the price of an item increases, then the demand for that item will decrease. For such models, economists often use two-dimensional graphs instead of functions.

• Qualitative models – Although almost all economic models involve some form of mathematical or quantitative analysis, qualitative models are occasionally used. One example is qualitative scenario planning in which possible future events are played out. Another example is non-numerical decision tree analysis. Qualitative models often suffer from lack of precision.

At a more practical level, quantitative modelling is applied to many areas of economics and several methodologies have evolved more or less independently of each other. As a result, no overall model taxonomy is naturally available. We can nonetheless provide a few examples which illustrate some particularly relevant points of model construction.

• An accounting model is one based on the premise that for every credit there is a debit. More symbolically, an accounting model expresses some principle of conservation in the form

algebraic sum of inflows = sinks − sources

This principle is certainly true for money and it is the basis for national income accounting. Accounting models are true by convention, that is any experimental failure to confirm them, would be attributed to fraud, arithmetic error or an extraneous injection (or destruction) of cash which we would interpret as showing the experiment was conducted improperly.

• Optimality and constrained optimization models – Other examples of quantitative models are based on principles such as profit or utility maximization. An example of such a model is given by the comparative statics of taxation on the profit-maximizing firm. The profit of a firm is given by

where is the price that a product commands in the market if it is supplied at the rate , is the revenue obtained from selling the product, is the cost of bringing the product to market at the rate , and is the tax that the firm must pay per unit of the product sold.

The profit maximization assumption states that a firm will produce at the output rate x if that rate maximizes the firm's profit. Using differential calculus we can obtain conditions on x under which this holds. The first order maximization condition for x is

Regarding x is an implicitly defined function of t by this equation (see implicit function theorem), one concludes that the derivative of x with respect to t has the same sign as

which is negative if the second order conditions for a local maximum are satisfied.

Thus the profit maximization model predicts something about the effect of taxation on output, namely that output decreases with increased taxation. If the predictions of the model fail, we conclude that the profit maximization hypothesis was false; this should lead to alternate theories of the firm, for example based on bounded rationality.

Borrowing a notion apparently first used in economics by Paul Samuelson, this model of taxation and the predicted dependency of output on the tax rate, illustrates an operationally meaningful theorem; that is one which requires some economically meaningful assumption which is falsifiable under certain conditions.

• Aggregate models. Macroeconomics needs to deal with aggregate quantities such as output, the price level, the interest rate and so on. Now real output is actually a vector of goods and services, such as cars, passenger airplanes, computers, food items, secretarial services, home repair services etc. Similarly price is the vector of individual prices of goods and services. Models in which the vector nature of the quantities is maintained are used in practice, for example Leontief input-output models are of this kind. However, for the most part, these models are computationally much harder to deal with and harder to use as tools for qualitative analysis. For this reason, macroeconomic models usually lump together different variables into a single quantity such as output or price. Moreover, quantitative relationships between these aggregate variables are often parts of important macroeconomic theories. This process of aggregation and functional dependency between various aggregates usually is interpreted statistically and validated by econometrics. For instance, one ingredient of the Keynesian model is a functional relationship between consumption and national income: C = C(Y). This relationship plays an important role in Keynesian analysis.elow

[edit] Quantitative vs. Qualitative models

A quantitative model is designed to produce accurate predictions, without elucidating the underlying dynamics. On the other hand, a qualitative model aims to explain these dynamics without necessarily fitting empirical data or informing accurate predictions. Interest rate parity can be deemed a qualitative model in this sense: though it generally fails to fit exchange rate data as well as higher-powered statistical forecasting models, it offers an intuitive interpretation of the exchange rate and its relation to foreign and domestic interest and inflation rates. Views on the relative merits of qualitative and quantitative models vary across the profession: Milton Friedman can be viewed as having advocated a qualitative approach, while Ronald Coase worried that "if you torture the data long enough, it will confess;" Prospect theory as proposed by Daniel Kahneman(a Nobel prize winner) is more quantiative, while rational agent models are more qualitative.

[edit] Pitfalls

[edit] Restrictive, unrealistic assumptions

Provably unrealistic assumptions are pervasive in neoclassical economic theory (also called the "standard theory" or "neoclassical paradigm"), and those assumptions are inherited by simplified models for that theory. (Any model based on a flawed theory, cannot transcend the limitations of that theory.) Joseph Stiglitz' 2001 Nobel Prize lecture ^[3] reviews his work on Information Asymmetries, which contrasts with the assumption, in standard models, of "Perfect Information". Stiglitz surveys many aspects of these faulty standard models, and the faulty policy implications and recommendations that arise from their unrealistic assumptions. Stiglitz writes: (p. 519–520)

"I only varied one assumption – the assumption concerning perfect information – and in ways which seemed highly plausible. ... We succeeded in showing not only that the standard theory was not robust – changing only one assumption in ways which were totally plausible had drastic consequences, but also that an alternative robust paradigm with great explanatory power could be constructed. There were other deficiencies in the theory, some of which were closely connected. The standard theory assumed that technology and preferences were fixed. But changes in technology, R & D, are at the heart of capitalism. ... I similarly became increasingly convinced of the inappropriateness of the assumption of fixed preferences. (Footnote: In addition, much of recent economic theory has assumed that beliefs are, in some sense, rational. As noted earlier, there are many aspects of economic behavior that seem hard to reconcile with this hypothesis.)"

Economic models can be such powerful tools in understanding some economic relationships, that it is easy to ignore their limitations. One tangible example where the limits of Economic Models collided with reality, but were nevertheless accepted as "evidence" in public policy debates, involved models to simulate the effects of NAFTA, the North American Free Trade Agreement. James Stanford published his examination of 10 of these models. ^{[4] [5]}
The fundamental issue is circularity: embedding one's assumptions as foundational "input" axioms in a model, then proceeding to "prove" that, indeed, the model's "output" supports the validity of those assumptions. Such a model is consistent with similar models that have adopted those same assumptions. But is it consistent with reality? As with any scientific theory, empirical validation is needed, if we are to have any confidence in its predictive ability.
If those assumptions are, in fact, fundamental aspects of empirical reality, then the model's output will correctly describe reality (if it is properly "tuned", and if it is not missing any crucial assumptions). But if those assumptions are not valid for the particular aspect of reality one attempts to simulate, then it becomes a case of "GIGO" – Garbage In, Garbage Out".
James Stanford outlines this issue for the specific Computable General Equilibrium ("CGE") models that were introduced as evidence into the public policy debate, by advocates for NAFTA: ^[6]

"..CGE models are circular: if trade theory holds that free trade is mutually beneficial, then a quantitative simulation model based on that theoretical structure will automatically show that free trade is mutually beneficial...if the economy actually behaves in the manner supposed by the modeler, and the model itself sheds no light on this question, then a properly calibrated model may provide a rough empirical estimate of the effects of a policy change. But the validity of the model hangs entirely on the prior, nontested specification of its structural relationships ... [Hence, the apparent consensus of pro-NAFTA modelers] reflects more a consensus of prior theoretical views than a consensus of quantitative evidence."

Commenting on Stanford's analysis, one computer scientist wrote,

"When simulating the impact of a trade agreement on labor, it seems absurd to assume a priori that capital is immobile, that full employment will prevail, that unit labor costs are identical in the U.S. and Mexico, that American consumers will prefer products made in America (even if they are more expensive), and that trade flows between the U.S. and Mexico will exactly balance. Yet a recent examination of ten prominent CGE models showed that nine of them include at least one of those unrealistic assumptions, and two of the CGE models included all the above assumptions.

This situation bears a disturbing resemblance to computer-assisted intellectual dishonesty. Human beings have always been masters of self-deception, and hiding the essential basis of one's deception by embedding it in a computer program surely helps reduce what might otherwise become an intolerable burden of cognitive dissonance." ^[7]

In commenting on the general phenomenon of embedding unrealistic "GIGO" assumptions in neoclassical economic models, Nobel prizewinner Joseph Stiglitz is only slightly more diplomatic: (p. 507-8)

"But the ... model, by construction, ruled out the information asymmetries which are at the heart of macro-economic problems. Only if an individual has a severe case of schizophrenia is it possible for such problems to arise. If one begins with a model that assumes that markets clear, it is hard to see how one can get much insight into unemployment (the failure of the labor market to clear)." ^[3]

Despite the prominence of Stiglitz' 2001 Nobel prize lecture, the use of misleading (perhaps intentionally) neoclassical models persisted in 2007, according to these authors: ^[8]

" ... projected welfare gains from trade liberalization are derived from global computable general equilibrium (CGE) models, which are based on highly unrealistic assumptions. CGE models have become the main tool for economic analysis of the benefits of multilateral trade liberalization; therefore, it is essential that these models be scrutinized for their realism and relevance. ... we analyze the foundation of CGE models and argue that their predictions are often misleading. ... We appeal for more honest simulation strategies that produce a variety of plausible outcomes."

The working paper, "Debunking the Myths of Computable General Equilibrium Models", ^[9] provides both a history, and a readable theoretical analysis of what CGE models are, and are not. In particular, despite their name, CGE models use neither the Walrass general equilibrium, nor the Arrow-Debreus General Equilibrium frameworks. Thus, CGE models are highly distorted simplifications of theoretical frameworks—collectively called "the neoclassical economic paradigm" – which—themselves—were largely discredited by Joseph Stiglitz.
In the "Concluding Remarks" (p. 524) of his 2001 Nobel Prize lecture, Stiglitz examined why the neoclassical paradigm—and models based on it—persists, despite his publication, over a decade earlier, of some of his seminal results showing that Information Asymmetries invalidated core Assumptions of that paradigm and its models:

"One might ask, how can we explain the persistence of the paradigm for so long? Partly, it must be because, in spite of its deficiencies, it did provide insights into many economic phenomena. ... But one cannot ignore the possibility that the survival of the [neoclassical] paradigm was partly because the belief in that paradigm, and the policy prescriptions, has served certain interests." ^[3]

In the aftermath of the 2007–2009 global economic meltdown, the profession's attachment to unrealistic models is increasingly being questioned and criticized. After a weeklong workshop, one group of economists released a paper highly critical of their own profession's unethical use of unrealistic models. Their Abstract offers an indictment of fundamental practices:

"The economics profession appears to have been unaware of the long build-up to the current worldwide financial crisis and to have significantly underestimated its dimensions once it started to unfold. In our view, this lack of understanding is due to a misallocation of research efforts in economics. We trace the deeper roots of this failure to the profession’s focus on models that, by design, disregard key elements driving outcomes in real-world markets. The economics profession has failed in communicating the limitations, weaknesses, and even dangers of its preferred models to the public. This state of affairs makes clear the need for a major reorientation of focus in the research economists undertake, as well as for the establishment of an ethical code that would ask economists to understand and communicate the limitations and potential misuses of their models." ^[10]

[edit] Omitted details

A great danger inherent in the simplification required to fit the entire economy into a model is omitting critical elements. Some economists believe that making the model as simple as possible is an art form, but the details left out are often contentious. For instance:

• Market models often exclude externalities such as unpunished pollution. Such models are the basis for many environmentalist attacks on mainstream economists. It is said that if the social costs of externalities were included in the models their conclusions would be very different, and models are often accused of leaving out these terms because of economist's pro-free market bias.
• In turn, environmental economics has been accused of omitting key financial considerations from its models. For example the returns to solar power investments are sometimes modelled without a discount factor, so that the present utility of solar energy delivered in a century's time is precisely equal to gas-power station energy today.
• Financial models can be oversimplified by relying on historically unprecedented arbitrage-free markets, probably underestimating the chance of crises, and under-pricing or under-planning for risk.
• Models of consumption either assume that humans are immortal or that teenagers plan their life around an optimal retirement supported by the next generation. (These conclusions are probably harmless, except possibly to the credibility of the modelling profession.)
• All Models share the same problem of the butterfly effect. Because they represent large complex nonlinear systems, it is possible that any missing variable as well as errors in value of included variables can lead to erroneous results.

[edit] Are economic models falsifiable?

The sharp distinction between falsifiable economic models and those that are not is by no means a universally accepted one. Indeed one can argue that the ceteris paribus (all else being equal) qualification that accompanies any claim in economics is nothing more than an all-purpose escape clause (See N. de Marchi and M. Blaug.) The all else being equal claim allows holding all variables constant except the few that the model is attempting to reason about. This allows the separation and clarification of the specific relationship. However, in reality all else is never equal, so economic models are guaranteed to not be perfect. The goal of the model is that the isolated and simplified relationship has some predictive power that can be tested, mainly that it is a theory capable of being applied to reality. To qualify as a theory, a model should arguably answer three questions: Theory of what?, Why should we care?, What merit is in your explanation? If the model fails to do so, it is probably too detached from reality and meaningful societal issues to qualify as theory. Research conducted according to this three-question test finds that in the 2004 edition of the Journal of Economic Theory, only 12% of the articles satisfy the three requirements.” ^[11] Ignoring the fact that the ceteris paribus assumption is being made is another big failure often made when a model is applied. At the minimum an attempt must be made to look at the various factors that may not be equal and take those into account.

[edit] History

One of the major problems addressed by economic models has been understanding economic growth. An early attempt to provide a technique to approach this came from the French physiocratic school in the Eighteenth century. Among these economists, François Quesnay should be noted, particularly for his development and use of tables he called Tableaux économiques. These tables have in fact been interpreted in more modern terminology as a Leontiev model, see the Phillips reference below.
All through the 18th century (that is, well before the founding of modern political economy, conventionally marked by Adam Smith's 1776 Wealth of Nations) simple probabilistic models were used to understand the economics of insurance. This was a natural extrapolation of the theory of gambling, and played an important role both in the development of probability theory itself and in the development of actuarial science. Many of the giants of 18th century mathematics contributed to this field. Around 1730, De Moivre addressed some of these problems in the 3rd edition of the Doctrine of Chances. Even earlier (1709), Nicolas Bernoulli studies problems related to savings and interest in the Ars Conjectandi. In 1730, Daniel Bernoulli studied "moral probability" in his book Mensura Sortis, where he introduced what would today be called "logarithmic utility of money" and applied it to gambling and insurance problems, including a solution of the paradoxical Saint Petersburg problem. All of these developments were summarized by Laplace in his Analytical Theory of Probabilities (1812). Clearly, by the time David Ricardo came along he had a lot of well-established math to draw from.

[edit] Tests of macroeconomic predictions

In the late 1980s a research institute compared twelve leading macroeconomic models available at the time. They compared the models' predictions for how the economy would respond to specific economic shocks (allowing the models to control for all the variability in the real world; this was a test of model vs. model, not a test against the actual outcome). Although the models simplified the world and started from a stable, known common parameters the various models gave significantly different answers. For instance, in calculating the impact of a monetary loosening on output some models estimated a 3% change in GDP after one year, and one gave almost no change, with the rest spread between.^[12]
Partly as a result of such experiments, modern central bankers no longer have as much confidence that it is possible to 'fine-tune' the economy as they had in the 1960s and early 1970s. Modern policy makers tend to use a less activist approach, explicitly because they lack confidence that their models will actually predict where the economy is going, or the effect of any shock upon it. The new, more humble, approach sees danger in dramatic policy changes based on model predictions, because of several practical and theoretical limitations in current macroeconomic models; in addition to the theoretical pitfalls, (listed above) some problems specific to aggregate modelling are:

• Limitations in model construction caused by difficulties in understanding the underlying mechanisms of the real economy. (Hence the profusion of separate models.)
• The law of Unintended consequences, on elements of the real economy not yet included in the model.
• The time lag in both receiving data and the reaction of economic variables to policy makers attempts to 'steer' them (mostly through monetary policy) in the direction that central bankers want them to move. Milton Friedman has vigorously argued that these lags are so long and unpredictably variable that effective management of the macroeconomy is impossible.
• The difficulty in correctly specifying all of the parameters (through econometric measurements) even if the structural model and data were perfect.
• The fact that all the model's relationships and coefficients are stochastic, so that the error term becomes very large quickly, and the available snapshot of the input parameters is already out of date.
• Modern economic models incorporate the reaction of the public & market to the policy maker's actions (through game theory), and this feedback is included in modern models (following the rational expectations revolution and Robert Lucas, Jr.'s critique of the optimal control concept of precise macroeconomic management). If the response to the decision maker's actions (and their credibility) must be included in the model then it becomes much harder to influence some of the variables simulated.

[edit] Comparison with models in other sciences

Complex systems specialist and mathematician David Orrell wrote on this issue and explained that the weather, human health and economics use similar methods of prediction (mathematical models). Their systems – the atmosphere, the human body and the economy – also have similar levels of complexity. He found that forecasts fail because the models suffer from two problems : i- they cannot capture the full detail of the underlying system, so rely on approximate equations; ii- they are sensitive to small changes in the exact form of these equations. This is because complex systems like the economy or the climate consist of a delicate balance of opposing forces, so a slight imbalance in their representation has big effects. Thus, predictions of things like economic recessions are still highly inaccurate, despite the use of enormous models running on fast computers. [2]

[edit] The effects of deterministic chaos on economic models

Economic and meteorological simulations may share a fundamental limit to their predictive powers: chaos. Although the modern mathematical work on chaotic systems began in the 1970s the danger of chaos had been identified and defined in Econometrica as early as 1958:

"Good theorising consists to a large extent in avoiding assumptions....(with the property that)....a small change in what is posited will seriously affect the conclusions."

(William Baumol, Econometrica, 26 see: Economics on the Edge of Chaos).

It is straightforward to design economic models susceptible to butterfly effects of initial-condition sensitivity.^[13][14]
However, the econometric research program to identify which variables are chaotic (if any) has largely concluded that aggregate macroeconomic variables probably do not behave chaotically. This would mean that refinements to the models could ultimately produce reliable long-term forecasts. However the validity of this conclusion has generated two challenges:

• In 2004 Philip Mirowski challenged this view and those who hold it, saying that chaos in economics is suffering from a biased "crusade" against it by neo-classical economics in order to preserve their mathematical models.
• The variables in finance may well be subject to chaos. Also in 2004, the University of Canterbury study Economics on the Edge of Chaos concludes that after noise is removed from S&P 500 returns, evidence of deterministic chaos is found.

More recently, chaos (or the butterfly effect) has been identified as less significant than previously thought to explain prediction errors. Rather, the predictive power of economics and meteorology would mostly be limited by the models themselves and the nature of their underlying systems (see Comparison with models in other sciences above).

[edit] The critique of hubris in planning

A key strand of free market economic thinking is that the market's "invisible hand" guides an economy to prosperity more efficiently than central planning using an economic model. One reason, emphasized by Friedrich Hayek, is the claim that many of the true forces shaping the economy can never be captured in a single plan. This is an argument which cannot be made through a conventional (mathematical) economic model, because it says that there are critical systemic-elements that will always be omitted from any top-down analysis of the economy.^[15]

[edit] Examples of economic models

• Black–Scholes option pricing model
• Heckscher-Ohlin model
• International Futures
• IS/LM model
• Participatory Economics
• Keynesian cross
• Leontief's input-output model
• World3
• Wonderland

[edit] See also

• Economic methodology
• Computational Economics
• Agent-based computational economics
• Macroeconomic model
• Financial modeling

[edit] Notes

1. ^ Moffatt, Mike. (2008) About.com Structural Parameters Economics Glossary; Terms Beginning with S. Accessed June 19, 2008.
2. ^ • Mary S. Morgan, 2008 "models," The New Palgrave Dictionary of Economics, 2nd Edition, Abstract.
      • Vivian Walsh 1987. "models and theory," The New Palgrave: A Dictionary of Economics, v. 3, pp. 482-83.
3. ^ ^{a b c} Joseph E. Stiglitz. 2001 Nobel Prize lecture: "INFORMATION AND THE CHANGE IN THE PARADIGM IN ECONOMICS". http://nobelprize.org/nobel_prizes/economics/laureates/2001/stiglitz-lecture.pdf.
4. ^ James Stanford. "Continental Economic Integration: Modeling the Impact on Labor," Annals of the American Academy of Political and Social Science, Mar 1993, V526 p. 92-110
5. ^ James Stanford. 1993. "FREE TRADE AND THE IMAGINARY WORLDS OF ECONOMIC MODELERS". http://www.pcdf.org/1993/45stanfo.htm.
6. ^ Robert Aponte. "NAFTA AND MEXICAN MIGRATION TO MICHIGAN AND THE U.S.". http://www.jsri.msu.edu/RandS/research/wps/wp25.pdf.
7. ^ Rick Crawford. 1996. Gerbner, George; Mowlana, Hamid; Schiller, Herbert I (1996), Computer-assisted Crises, ISBN 978-0-8133-2072-4, http://www.google.com/search?tbm=bks&tbo=1&q=CGE+models+included+all+the+above+assumptions.+This+situation+bears+a+disturbing+resemblance in "Invisible Crises: What Conglomerate Control of Media Means for America and the World". Ed. Herbert Schiller, Hamid Mowlana, George Gerbner. Westview. 1996.     Free, authorized version viewable at: Computer-assisted Crises, http://infowarethics.org/computer-assisted.crises.html
8. ^ Lance Taylor & Rudiger von Arnim. March 2007. "Projected Benefits of the Doha Round Hinge on Misleading Trade Models". http://www.newschool.edu/cepa/publications/policynotes/Doha%20Policy%20Note%20Final%2003_12_07.pdf.
9. ^ Mitra-Kahn, Benjamin H., 2008. "Debunking the Myths of Computable General Equilibrium Models". http://www.newschool.edu/cepa/publications/workingpapers/SCEPA%20Working%20Paper%202008-1%20Kahn.pdf. SCEPA Working Paper 01-2008.
10. ^ Colander, David; Follmer, Hans; Haas, Armin; Goldberg, Michael D.; Juselius, Katarina; Kirman, Alan; Lux, Thomas; and Sloth, Birgitte: The Financial Crisis and the Systemic Failure of Academic Economics. SSRN 1355882. (March 9, 2009). Univ. of Copenhagen Dept. of Economics Discussion Paper No. 09-03
11. ^ Klein, Daniel B. and Pedro P. Romero. "Model Building Versus Theorizing: The Paucity of Theory in the Journal of Economic Theory" (May 2007). [1]
12. ^ Frankel,, Jeffrey A. (May 1986). "The Sources of Disagreement Among International Macro Models and Implications for Policy Coordination". NBER Working Paper. http://www.nber.org/papers/w1925.pdf. Retrieved 23 January 2012.
13. ^ Paul Wilmott on his early research in finance: "I quickly dropped... chaos theory (as) it was too easy to construct ‘toy models’ that looked plausible but were useless in practice." Wilmott, Paul (2009), Frequently Asked Questions in Quantitative Finance, John Wiley and Sons, p. 227, http://books.google.com/books?id=n4swgjSoMyIC&lpg=PT227&pg=PT227#v=onepage
14. ^ Kuchta, Steve (2004) (pdf), Nonlinearity and Chaos in Macroeconomics and Financial Markets, University of Connecticut, http://www.sp.uconn.edu/~ages/files/NL_Chaos_and_%20Macro%20-%20429%20Essay.pdf
15. ^ Hayek, Friedrich (September, 1945), "The Use of Knowledge in Society", American Economic Review 35 (4): 519–530, JSTOR 1809376.

[edit] References

• Baumol, William & Blinder, Alan (1982), Economics: Principles and Policy (2nd ed.), New York: Harcourt Brace Jovanovich, ISBN 0-15-518839-9.
• Caldwell, Bruce (1994), Beyond Positivism: Economic Methodology in the Twentieth Century (Revised ed.), New York: Routledge, ISBN 0-415-10911-6.
• Holcombe, R. (1989), Economic Models and Methodology, New York: Greenwood Press, ISBN 0-313-26679-4. Defines model by analogy with maps, an idea borrowed from Baumol and Blinder. Discusses deduction within models, and logical derivation of one model from another. Chapter 9 compares the neoclassical school and the Austrian school, in particular in relation to falsifiability.
• Lange, Oskar (1945), "The Scope and Method of Economics", Review of Economic Studies (The Review of Economic Studies Ltd.) 13 (1): 19–32, doi:10.2307/2296113, JSTOR 2296113. One of the earliest studies on methodology of economics, analysing the postulate of rationality.
• de Marchi, N. B. & Blaug, M. (1991), Appraising Economic Theories: Studies in the Methodology of Research Programs, Brookfield, VT: Edward Elgar, ISBN 1-85278-515-2. A series of essays and papers analysing questions about how (and whether) models and theories in economics are empirically verified and the current status of positivism in economics.
• Morishima, Michio (1976), The Economic Theory of Modern Society, New York: Cambridge University Press, ISBN 0-521-21088-7. A thorough discussion of many quantitative models used in modern economic theory. Also a careful discussion of aggregation.
• Orrell, David (2007), Apollo's Arrow: The Science of Prediction and the Future of Everything, Toronto: Harper Collins Canada, ISBN 0-00-200740-1.
• Phillips, Almarin (1955), "The Tableau Économique as a Simple Leontief Model", Quarterly Journal of Economics (The MIT Press) 69 (1): 137–144, doi:10.2307/1884854, JSTOR 1884854.
• Samuelson, Paul A. (1948), "The Simple Mathematics of Income Determination", in Metzler, Lloyd A., Income, Employment and Public Policy; essays in honor of Alvin Hansen, New York: W. W. Norton.
• Samuelson, Paul A. (1983), Foundations of Economic Analysis (Enlarged ed.), Cambridge: Harvard University Press, ISBN 0-674-31301-1. This is a classic book carefully discussing comparative statics in microeconomics, though some dynamics is studied as well as some macroeconomic theory. This should not be confused with Samuelson's popular textbook.
• Tinbergen, Jan (1939), Statistical Testing of Business Cycle Theories, Geneva: League of Nations.
• Walsh, Vivian (1987), "Models and theory", The New Palgrave: A Dictionary of Economics, 3, New York: Stockton Press, pp. 482–483, ISBN 0-935859-10-1.
• Wold, H. (1938), A Study in the Analysis of Stationary Time Series, Stockholm: Almqvist and Wicksell.
• Wold, H. & Jureen, L. (1953), Demand Analysis: A Study in Econometrics, New York: Wiley.

[edit] External links

• R. Frigg and S. Hartmann, Models in Science. Entry in the Stanford Encyclopedia of Philosophy.
• H. Varian How to build a model in your spare time The author makes several unexpected suggestions: Look for a model in the real world, not in journals. Look at the literature later, not sooner.
• Elmer G. Wiens: Classical & Keynesian AD-AS Model – An on-line, interactive model of the Canadian Economy.
• IFs Economic Sub-Model [3]: Online Global Model