sumatera barat Model basis Ekonomi
This article is about theoretical simulations. For the overall economic structure of a society, see
Economic system.
In economics, a model is a theoretical construct representing 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.
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.
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.
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.
Economic system.
In economics, a model is a theoretical construct representing 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.
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.
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.
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.