Modelo económico

Este artículo trata sobre el modelado teórico. Para conocer la estructura económica general de una sociedad, consulte Sistema económico .

Un diagrama del modelo IS / LM

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En economía , un modelo es un constructo teórico que representa los procesos económicos mediante un conjunto de variables y un conjunto de relaciones lógicas y / o cuantitativas entre ellas. El modelo económico es un marco simplificado, a menudo matemático , diseñado para ilustrar procesos complejos. Con frecuencia, los modelos económicos plantean parámetros estructurales . ^[1] Un modelo puede tener varias variables exógenas, y esas variables pueden cambiar para crear diversas respuestas de las variables económicas. Los usos metodológicos de los modelos incluyen la investigación, la teorización y el ajuste de las teorías al mundo. ^[2]

Resumen

En términos generales, los modelos económicos tienen dos funciones: primero como simplificación y abstracción de datos observados, y segundo como medio de selección de datos basado en un paradigma de estudio econométrico .

La simplificación es particularmente importante para la economía dada la enorme complejidad de los procesos económicos. ^[3] Esta complejidad se puede atribuir a la diversidad de factores que determinan la actividad económica; Estos factores incluyen: procesos de decisión individuales y cooperativos , limitaciones de recursos , limitaciones ambientales y geográficas , requisitos institucionales y legales y puramente aleatorios.fluctuaciones. Por lo tanto, los economistas deben hacer una elección razonada de qué variables y qué relaciones entre estas variables son relevantes y qué formas de analizar y presentar esta información son útiles.

La selección es importante porque la naturaleza de un modelo económico a menudo determinará qué hechos se analizarán y cómo se compilarán. Por ejemplo, la inflación es un concepto económico general, pero para medir la inflación se requiere un modelo de comportamiento, de modo que un economista pueda diferenciar entre cambios en los precios relativos y cambios en el precio que se atribuyen a la inflación.

Además de su interés académico profesional , los usos de modelos incluyen:

• Pronosticar la actividad económica de una manera en la que las conclusiones estén lógicamente relacionadas con los supuestos;
• Proponer una política económica para modificar la actividad económica futura;
• Presentar argumentos razonados para justificar políticamente la política económica a nivel nacional, para explicar e influir en la estrategia de la empresa a nivel de empresa, o para brindar asesoramiento inteligente para las decisiones económicas de los hogares a nivel de hogares.
• Planificación y asignación , en el caso de economías de planificación centralizada , y a menor escala en logística y gestión de negocios .
• En finanzas , los modelos predictivos se han utilizado desde la década de 1980 para el comercio ( inversión y especulación ). Por ejemplo, los bonos de mercados emergentes a menudo se negociaban sobre la base de modelos económicos que predecían el crecimiento de la nación en desarrollo que los emitía. Desde la década de 1990, muchos modelos de gestión de riesgos a largo plazo han incorporado relaciones económicas entre variables simuladas en un intento de detectar escenarios futuros de alta exposición (a menudo a través del método Monte Carlo ).

Un modelo establece un marco argumentativo para la aplicación de la lógica y las matemáticas que se puede discutir y probar de forma independiente y que se puede aplicar en varias instancias. Las políticas y los argumentos que se basan en modelos económicos tienen una base clara para la solidez, a saber, la validez del modelo de apoyo.

Los modelos económicos en uso actual no pretenden ser teorías de todo lo económico ; Cualquiera de tales pretensiones se vería frustrada inmediatamente por la inviabilidad computacional y la falta de completitud o falta de teorías para varios tipos de comportamiento económico. Por tanto, las conclusiones extraídas de los modelos serán representaciones aproximadas de hechos económicos. Sin embargo, los modelos construidos correctamente pueden eliminar información extraña y aislar aproximaciones útiles de relaciones clave. De esta manera se puede entender más acerca de las relaciones en cuestión que tratando de comprender todo el proceso económico.

Los detalles de la construcción del modelo varían según el tipo de modelo y su aplicación, pero se puede identificar un proceso genérico. Generalmente, cualquier proceso de modelado tiene dos pasos: generar un modelo y luego verificar la precisión del modelo (a veces llamado diagnóstico). El paso de diagnóstico es importante porque un modelo solo es útil en la medida en que refleje con precisión las relaciones que pretende describir. La creación y el diagnóstico de un modelo es con frecuencia un proceso iterativo en el que el modelo se modifica (y es de esperar que se mejore) con cada iteración del diagnóstico y la reentrada. Una vez que se encuentra un modelo satisfactorio, debe verificarse dos veces aplicándolo a un conjunto de datos diferente.

Tipos de modelos

Según que todas las variables del modelo sean deterministas, los modelos económicos se pueden clasificar en modelos estocásticos o no estocásticos; según que todas las variables sean cuantitativas, los modelos económicos se clasifican en modelo de elección discreta o continua; de acuerdo con el propósito / función prevista del modelo, se puede clasificar como cuantitativo o cualitativo; según el ámbito del modelo, se puede clasificar como modelo de equilibrio general, modelo de equilibrio parcial o incluso modelo de no equilibrio; De acuerdo con las características del agente económico, los modelos pueden clasificarse en modelos de agente racional, modelos de agente representativo, etc.

• Los modelos estocásticos se formulan mediante procesos estocásticos . Modelan valores económicamente observables a lo largo del tiempo. La mayor parte de la econometría se basa en estadísticas para formular y probar hipótesis sobre estos procesos o estimar parámetros para ellos. Una clase de negociación ampliamente utilizada de modelos econométricos simples popularizados por Tinbergen y más tarde Wold son los modelos autorregresivos , en los que el proceso estocástico satisface alguna relación entre los valores actuales y pasados. Ejemplos de estos son los modelos de media móvil autorregresivos y otros relacionados, como la heterocedasticidad condicional autorregresiva.(ARCH) y modelos GARCH para el modelado de heterocedasticidad .
• Los modelos no estocásticos pueden ser puramente cualitativos (por ejemplo, relacionados con la teoría de la elección social ) o cuantitativos (que implican la racionalización de variables financieras, por ejemplo, con coordenadas hiperbólicas y / o formas específicas de relaciones funcionales entre variables). En algunos casos, las predicciones económicas en una coincidencia de un modelo simplemente afirman la dirección del movimiento de las variables económicas, por lo que las relaciones funcionales se usan solo estoicas en un sentido cualitativo: por ejemplo, si el precio de un artículo aumenta, entonces la demanda de ese artículo disminuirá. Para tales modelos, los economistas suelen utilizar gráficos bidimensionales en lugar de funciones.
• Modelos cualitativos : aunque casi todos los modelos económicos implican alguna forma de análisis matemático o cuantitativo, ocasionalmente se utilizan modelos cualitativos. Un ejemplo es la planificación de escenarios cualitativos en la que se desarrollan posibles eventos futuros. Otro ejemplo es el análisis de árbol de decisiones no numérico. Los modelos cualitativos a menudo adolecen de falta de precisión.

A un nivel más práctico, el modelado cuantitativo se aplica a muchas áreas de la economía y varias metodologías han evolucionado más o menos independientemente unas de otras. Como resultado, no hay una taxonomía de modelo general disponible de forma natural. No obstante, podemos proporcionar algunos ejemplos que ilustran algunos puntos particularmente relevantes de la construcción del modelo.

• Un modelo contable es aquel que se basa en la premisa de que por cada crédito hay un débito . Más simbólicamente, un modelo contable expresa algún principio de conservación en la forma

suma algebraica de entradas = sumideros - fuentes

Este principio es ciertamente cierto para el dinero y es la base de la contabilidad del ingreso nacional . Los modelos contables son verdaderos por convención , es decir, cualquier falla experimental para confirmarlos se atribuiría a fraude , error aritmético o una inyección (o destrucción) extraña de efectivo, lo que interpretaríamos como que muestra que el experimento se realizó de manera incorrecta.

• 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

${\displaystyle \pi (x,t)=xp(x)-C(x)-tx\quad }$

where ${\displaystyle p(x)}$ is the price that a product commands in the market if it is supplied at the rate ${\displaystyle x}$, ${\displaystyle xp(x)}$ is the revenue obtained from selling the product, ${\displaystyle C(x)}$ is the cost of bringing the product to market at the rate ${\displaystyle x}$, and ${\displaystyle t}$ 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

${\displaystyle {\frac {\partial \pi (x,t)}{\partial x}}={\frac {\partial (xp(x)-C(x))}{\partial x}}-t=0}$

Regarding x as 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

${\displaystyle {\frac {\partial ^{2}(xp(x)-C(x))}{\partial ^{2}x}}={\partial ^{2}\pi (x,t) \over \partial x^{2}},}$

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 requiring some economically meaningful assumption that 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.

Problems with economic models

Most economic models rest on a number of assumptions that are not entirely realistic. For example, agents are often assumed to have perfect information, and markets are often assumed to clear without friction. Or, the model may omit issues that are important to the question being considered, such as externalities. Any analysis of the results of an economic model must therefore consider the extent to which these results may be compromised by inaccuracies in these assumptions, and a large literature has grown up discussing problems with economic models, or at least asserting that their results are unreliable.

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 was known 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.

Tests of macroeconomic predictions

In the late 1980s, the Brookings Institution compared 12 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.^[4]

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 and 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 Lucas critique of non-microfounded models). 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.

Comparison with models in other sciences

Complex systems specialist and mathematician David Orrell wrote on this issue in his book Apollo's Arrow 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.^[5]See Unreasonable ineffectiveness of mathematics #Economics and finance.

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.^[6][7]

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).

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 that 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.^[8]

Examples of economic models

• Cobb–Douglas model of production
• Solow–Swan model of economic growth
• Lucas islands model of money supply
• Heckscher–Ohlin model of international trade
• Black–Scholes model of option pricing
• AD–AS model a macroeconomic model of aggregate demand– and supply
• IS–LM model the relationship between interest rates and assets markets
• Ramsey–Cass–Koopmans model of economic growth

See also

• Economic methodology
• Computational economics
• Agent-based computational economics
• Endogeneity
• Financial model

Notes

1. Moffatt, Mike. (2008) About.com Structural Parameters Archived 2016-01-07 at the Wayback Machine 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. Friedman, M. (1953). "The Methodology of Positive Economics". Essays in Positive Economics. Chicago: University of Chicago Press.
4. Frankel, Jeffrey A. (May 1986). "The Sources of Disagreement Among International Macro Models and Implications for Policy Coordination". NBER Working Paper No. 1925. doi:10.3386/w1925.
5. "FAQ for Apollo's Arrow Future of Everything". www.postpythagorean.com.
6. 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
7. Kuchta, Steve (2004), Nonlinearity and Chaos in Macroeconomics and Financial Markets (PDF), University of Connecticut
8. Hayek, Friedrich (September 1945), "The Use of Knowledge in Society", American Economic Review, 35 (4): 519–30, JSTOR 1809376.

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): 137–44, doi:10.2307/1884854, JSTOR 1884854.
• Samuelson, Paul A. (1948), "The Simple Mathematics of Income Determination", in Metzler, Lloyd A. (ed.), 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. 482–83, 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.

External links

Wikiquote has quotations related to: Economic model

Wikimedia Commons has media related to Economic models.

• 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 [1]: Online Global Model
• Economic attractor