Economic model

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 changes in relative prices and changes in price that are to be attributed to inflation.

In addition to their professional

• 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.
• allocation, in the case of centrally planned economies, and on a smaller scale in logistics and management of businesses
  .
• In
  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
  ).

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 incompleteness or lack of theories for various 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.

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
  heteroskedasticity
  .
• Non-stochastic models may be purely qualitative (for example, relating to
  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

• An
  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

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
  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 ${\displaystyle p(x)}$ 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

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.
  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.

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

• 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
  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
  credibility
  ) must be included in the model then it becomes much harder to influence some of the variables simulated.

Effects of deterministic chaos on economic models

Economic and meteorological simulations may share a fundamental limit to their predictive powers:

"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

• In 2004
  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

Critique of hubris in planning

A key strand of

• 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
• Gordon–Loeb model for cyber security investments

See also

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

Notes

References

• 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,
  Austrian School
  , in particular in relation to falsifiability.
• S2CID 4140287
  . 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.
• ISBN 0-521-21088-7
  . A thorough discussion of many quantitative models used in modern economic theory. Also a careful discussion of aggregation.
• ISBN 978-0-00-200740-5
  .
• 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),
  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",
  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.
• S2CID 1500788
  .

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