Macroeconomic model

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A macroeconomic model is an analytical tool designed to describe the operation of the problems of economy of a country or a region. These models are usually designed to examine the

goods and services produced, total income earned, the level of employment of productive resources, and the level of prices
.

Macroeconomic models may be logical, mathematical, and/or computational; the different types of macroeconomic models serve different purposes and have different advantages and disadvantages.

academia in teaching and research, and are also widely used by international organizations, national governments and larger corporations, as well as by economic consultants and think tanks
.

Types

Simple theoretical models

Simple textbook descriptions of the macroeconomy involving a small number of equations or diagrams are often called ‘models’. Examples include the

GDP or total employment
) rather than individual choice variables, and while the equations relating these variables are intended to describe economic decisions, they are not usually derived directly by aggregating models of individual choices. They are simple enough to be used as illustrations of theoretical points in introductory explanations of macroeconomic ideas; but therefore quantitative application to forecasting, testing, or policy evaluation is usually impossible without substantially augmenting the structure of the model.

Empirical forecasting models

Main article: Large-scale macroeconometric model

In the 1940s and 1950s, as governments began accumulating

time series analysis. Like the simpler theoretical models, these empirical models described relations between aggregate quantities, but many addressed a much finer level of detail (for example, studying the relations between output, employment, investment, and other variables in many different industries). Thus, these models grew to include hundreds or thousands of equations describing the evolution of hundreds or thousands of prices and quantities over time, making computers essential for their solution. While the choice of which variables to include in each equation was partly guided by economic theory (for example, including past income as a determinant of consumption, as suggested by the theory of adaptive expectations), variable inclusion was mostly determined on purely empirical grounds.[4]

Nobel Prize. Large-scale empirical models of this type, including the Wharton model, are still in use today, especially for forecasting purposes.[5][6][7]

The Lucas critique of empirical forecasting models

Main article: Lucas critique

Econometric studies in the first part of the 20th century showed a negative correlation between inflation and unemployment called the

stagflation of the 1970s appeared to bear out their prediction.[11]

In 1976,

preferences, technology, and budget constraints
) that should be unaffected by policy changes.

Dynamic stochastic general equilibrium models

Main article: Dynamic stochastic general equilibrium

Partly as a response to the

optimal choice, taking into account prices and the strategies of other agents, both in the current period and in the future. Summing up the decisions of the different types of agents, it is possible to find the prices that equate supply with demand in every market. Thus these models embody a type of equilibrium
self-consistency: agents choose optimally given the prices, while prices must be consistent with agents’ supplies and demands.

DSGE models often assume that all agents of a given type are identical (i.e. there is a ‘

real business cycle model[19][20][21] and the New Keynesian DSGE model.[22][23] More elaborate DSGE models are used to predict the effects of changes in economic policy and evaluate their impact on social welfare. However, economic forecasting
is still largely based on more traditional empirical models, which are still widely believed to achieve greater accuracy in predicting the impact of economic disturbances over time.

DSGE versus CGE models

Main article: Computable general equilibrium

A methodology that pre-dates DSGE modeling is computable general equilibrium (CGE) modeling. Like DSGE models, CGE models are often microfounded on assumptions about preferences, technology, and budget constraints. However, CGE models focus mostly on long-run relationships, making them most suited to studying the long-run impact of permanent policies like the tax system or the openness of the economy to international trade.[24][25] DSGE models instead emphasize the dynamics of the economy over time (often at a quarterly frequency), making them suited for studying business cycles and the cyclical effects of monetary and fiscal policy.

Agent-based computational macroeconomic models

Main article: Agent-based computational economics

Another modeling methodology is Agent-based computational economics (ACE), which is a variety of

preferences of those agents, ACE models often jump directly to specifying their strategies. Or sometimes, preferences are specified, together with an initial strategy and a learning rule whereby the strategy is adjusted according to its past success.[27]
Given these strategies, the interaction of large numbers of individual agents (who may be very heterogeneous) can be simulated on a computer, and then the aggregate, macroeconomic relationships that arise from those individual actions can be studied.

Strengths and weaknesses of DSGE and ACE models

DSGE and ACE models have different advantages and disadvantages due to their different underlying structures. DSGE models may exaggerate individual rationality and foresight, and understate the importance of heterogeneity, since the

preferences may remain vulnerable to the Lucas critique
: a changed policy regime should generally give rise to changed strategies.

See also

References

  1. ^ Blanchard, Olivier (January 12, 2017). "The need for different classes of macroeconomic models". Peterson Institute for International Economics. Retrieved February 22, 2022.
  2. ISBN 0-13-013306-X
    .
  3. ^
    S2CID 154689887
    .
  4. JSTOR 1928627
    .
  5. ISBN 0-19-505772-4
    .
  6. ISBN 0-07-018972-2
    .
  7. ^ Bodkin, Ronald; Klein, Lawrence; Marwah, Kanta (1991). A History of Macroeconometric Model Building. Edward Elgar.
  8. JSTOR 2550759
  9. JSTOR 1831652
  10. S2CID 154427979
  11. ^ Blanchard, Olivier (2000), op. cit., Ch. 28, p. 540.
  12. doi:10.1016/S0167-2231(76)80003-6
  13. ISBN 0-631-14605-9
    .
  14. ^ Blanchard, Olivier (2000), op. cit., Ch. 28, p. 542.
  15. ISBN 0-393-09326-3
    .
  16. S2CID 17606592
    .
  17. ISBN 0-691-04921-1
    .
  18. ISBN 0-691-12648-8
    .
  19. JSTOR 1913386
    .
  20. ^ Thomas F. Cooley (1995), Frontiers of Business Cycle Research. Princeton University Press.
  21. ISBN 0-201-54392-3
    .
  22. S2CID 154438345
    .
  23. ISBN 0-691-01049-8
    .
  24. doi:10.1016/0047-2727(72)90009-6. Archived from the original
    (PDF) on 2022-02-26. Retrieved 2019-07-12.
  25. ^ Kehoe, Patrick J.; Kehoe, Timothy J. (1994). "A primer on static applied general equilibrium models" (PDF). Federal Reserve Bank of Minneapolis Quarterly Review. 18 (1): 2–16.
  26. ^ Tesfatsion, Leigh (2003). "Agent-Based Computational Economics" (PDF). Iowa State University Economics Working Paper #1.
  27. JSTOR 2171879
    .

External links
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