거시경제모형

Macroeconomic model
다음에 대한 시리즈 일부
거시경제학
Bank of England
기본개념
정책들
모델
관련분야
학교
주류

헤테로독스

사람
참고 항목

거시경제 모델은 국가나 지역의 경제 문제의 운영을 설명하기 위해 고안된 분석 도구다. 이러한 모델은 생산되는 재화와 용역의 총량, 총소득, 생산자원의 고용 수준, 가격 수준 등 총 수량의 비교 통계학 및 역학을 조사하기 위해 설계된다.

Macroeconomic models may be logical, mathematical, and/or computational; the different types of macroeconomic models serve different purposes and have different advantages and disadvantages.[1] Macroeconomic models may be used to clarify and illustrate basic theoretical principles; they may be used to test, compare, and quantify different macroeconomic theories; they may be used to produce "what if" scenarios (usually to predict the effects of changes in monetary, fiscal, or other macroeconomic policies); and they may be used to generate economic forecasts. Thus, macroeconomic models are widely used in 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 IS-LM model and Mundell–Fleming model of Keynesian macroeconomics, and the Solow model of neoclassical growth theory. These models share several features. They are based on a few equations involving a few variables, which can often be explained with simple diagrams.[2] Many of these models are static, but some are dynamic, describing the economy over many time periods. The variables that appear in these models often represent macroeconomic aggregates (such as 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 national income and product accounting data, economists set out to construct quantitative models to describe the dynamics observed in the data.[3] These models estimated the relations between different macroeconomic variables using (mostly linear) 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]

Dutch economist Jan Tinbergen developed the first comprehensive national model, which he built for the Netherlands in 1936. He later applied the same modeling structure to the economies of the United States and the United Kingdom.[3] The first global macroeconomic model, Wharton Econometric Forecasting Associates' LINK project, was initiated by Lawrence Klein. The model was cited in 1980 when Klein, like Tinbergen before him, won the 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 Phillips curve.[8] Empirical macroeconomic forecasting models, being based on roughly the same data, had similar implications: they suggested that unemployment could be permanently lowered by permanently increasing inflation. However, in 1968, Milton Friedman[9] and Edmund Phelps[10] argued that this apparent tradeoff was illusory. They claimed that the historical relation between inflation and unemployment was due to the fact that past inflationary episodes had been largely unexpected. They argued that if monetary authorities permanently raised the inflation rate, workers and firms would eventually come to understand this, at which point the economy would return to its previous, higher level of unemployment, but now with higher inflation too. The stagflation of the 1970s appeared to bear out their prediction.[11]

1976년 로버트 루카스 주니어는 1970년대 필립스 곡선의 실패는 경험적 예측 모델의 일반적인 문제의 한 예에 불과하다고 주장하는 영향력 있는 논문을 발표했다.[12][13] 그는 이러한 모델은 시간의 경과에 따른 다양한 거시경제적 수량의 관찰된 관계에서 파생되며, 이러한 관계는 어떤 거시경제적 정책체제가 갖춰져 있느냐에 따라 다르다고 지적했다. 필립스 곡선의 맥락에서, 이것은 인플레이션과 실업 사이의 관계가 과거에 인플레이션이 낮았던 경제에서 관측된 것과 인플레이션이 높았던 경제에서 관측된 관계가 다를 것이라는 것을 의미한다.[14] 더욱이 이는 정책체제가 마련되지 않은 이전 기간의 데이터를 바탕으로 한 경험적 예측 모델을 사용하여 새로운 정책체제의 효과를 예측할 수 없다는 것을 의미한다. 루카스는 경제학자들이 정책변화에 영향을 받지 않아야 할 경제 펀더멘털(선호, 기술, 예산 제약 등)을 바탕으로 모델을 구축하지 않는 한 새로운 정책의 효과를 예측할 수 없을 것이라고 주장했다.

동적 확률적 일반 평형 모델

주요 기사: 동적 확률형 일반 평형

부분적으로 루카스 비평에 대한 대응으로서 1980년대와 1990년대의 경제학자들은 합리적 선택에 기초하여 미시적으로[15] 근거한 거시경제 모델을 구축하기 시작했는데, 이것을 동적 확률론적 일반 균형(DSGE) 모델이라고 불렀다. 이러한 모델은 하나 이상의 국가에서 가계, 기업, 정부와 같이 경제에서 활동 중인 에이전트들의 집합과 각 국가의 선호도, 기술, 예산 제약 등을 명시하는 것으로 시작한다. 각 대리점은 현재와 향후의 가격과 다른 대리점의 전략을 고려하여 최적의 선택을 하는 것으로 가정한다. 다양한 유형의 대리점의 결정을 종합하면, 모든 시장에서 공급과 수요의 동일 가격을 찾을 수 있다. 따라서 이러한 모델은 평형 자기 일관성의 한 유형을 구현한다. 즉, 대리점은 최적의 가격을 선택하는 반면, 가격은 대리인의 공급과 수요에 부합해야 한다.

DSGE models often assume that all agents of a given type are identical (i.e. there is a ‘representative household’ and a ‘representative firm’) and can perform perfect calculations that forecast the future correctly on average (which is called rational expectations). However, these are only simplifying assumptions, and are not essential for the DSGE methodology; many DSGE studies aim for greater realism by considering heterogeneous agents[16] or various types of adaptive expectations.[17] Compared with empirical forecasting models, DSGE models typically have fewer variables and equations, mainly because DSGE models are harder to solve, even with the help of computers.[18] Simple theoretical DSGE models, involving only a few variables, have been used to analyze the forces that drive business cycles; this empirical work has given rise to two main competing frameworks called the 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 closely related 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 that has developed at the same time as DSGE models is Agent-based computational economics (ACE), which is a variety of Agent-based modeling.[26] Like the DSGE methodology, ACE seeks to break down aggregate macroeconomic relationships into microeconomic decisions of individual agents. ACE models also begin by defining the set of agents that make up the economy, and specify the types of interactions individual agents can have with each other or with the market as a whole. Instead of defining the 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 rational expectations, representative agent case remains the simplest and thus the most common type of DSGE model to solve. Also, unlike ACE models, it may be difficult to study local interactions between individual agents in DSGE models, which instead focus mostly on the way agents interact through aggregate prices. On the other hand, ACE models may exaggerate errors in individual decision-making, since the strategies assumed in ACE models may be very far from optimal choices unless the modeler is very careful. A related issue is that ACE models which start from strategies instead of 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 (2017), “The need for different classes of macroeconomic models”, blog post, Jan. 12, 2017, Peterson Institute for International Economics.
  2. ^ Blanchard, Olivier (2000), Macroeconomics, 2nd ed., Chap. 3.3, p. 47. Prentice Hall, ISBN0-13-013306-X.
  3. ^ a b Klein, Lawrence (2004). "The contribution of Jan Tinbergen to economic science". De Economist. 152 (2): 155–157. doi:10.1023/B:ECOT.0000023251.14849.4f.
  4. ^ Koopmans, Tjalling C. (1947). "Measurement Without Theory". Review of Economics and Statistics. 29 (3): 161–172. doi:10.2307/1928627. JSTOR 1928627.
  5. ^ Klein, Lawrence R., ed. (1991). Comparative Performance of US Econometric Models. Oxford University Press. ISBN 0-19-505772-4.
  6. ^ Eckstein, Otto (1983). The DRI Model of the US Economy. McGraw-Hill. ISBN 0-07-018972-2.
  7. ^ Bodkin, Ronald; Klein, Lawrence; Marwah, Kanta (1991). A History of Macroeconometric Model Building. Edward Elgar.
  8. ^ Phillips, A. W. (1958), "The relationship between unemployment and the rate of change of money wages in the United Kingdom 1861-1957", Economica, 25 (100): 283–299, doi:10.2307/2550759, JSTOR 2550759
  9. ^ Friedman, Milton (1968), "The role of monetary policy", American Economic Review, American Economic Association, 58 (1): 1–17, JSTOR 1831652
  10. ^ Phelps, Edmund S. (1968), "Money wage dynamics and labor market equilibrium", Journal of Political Economy, 76 (4): 678–711, doi:10.1086/259438
  11. ^ Blanchard, Olivier (2000), op. cit., Ch. 28, p. 540.
  12. ^ Lucas, Robert E., Jr. (1976), "Econometric Policy Evaluation: A Critique" (PDF), Carnegie-Rochester Conference Series on Public Policy, 1: 19–46, doi:10.1016/S0167-2231(76)80003-6
  13. ^ Hoover, Kevin D. (1988). "Econometrics and the Analysis of Policy". The New Classical Macroeconomics. Oxford: Basil Blackwell. pp. 167–209. ISBN 0-631-14605-9.
  14. ^ Blanchard, Olivier (2000), op. cit., Ch. 28, p. 542.
  15. ^ Edmund S. Phelps, ed., (1970), Microeconomic Foundations of Employment and Inflation Theory. New York, Norton and Co. ISBN 0-393-09326-3.
  16. ^ Krusell, Per; Smith, Anthony A., Jr. (1998). "Income and wealth heterogeneity in the macroeconomy". Journal of Political Economy. 106 (5): 243–277. doi:10.1086/250034.
  17. ^ George W. Evans and Seppo Honkapohja (2001), Learning and Expectations in Macroeconomics. Princeton University Press, ISBN 0-691-04921-1.
  18. ^ DeJong, D. N. with C. Dave (2007), Structural Macroeconometrics. Princeton University Press, ISBN 0-691-12648-8.
  19. ^ Kydland, Finn E.; Prescott, Edward C. (1982). "Time to Build and Aggregate Fluctuations". Econometrica. 50 (6): 1345–70. doi:10.2307/1913386. JSTOR 1913386.
  20. ^ Thomas F. Cooley (1995), Frontiers of Business Cycle Research. Princeton University Press.
  21. ^ Andrew Abel and Ben Bernanke (1995), Macroeconomics, 2nd ed., Ch. 11.1, pp. 355-362. Addison-Wesley, ISBN 0-201-54392-3.
  22. ^ Rotemberg, Julio J.; Woodford, Michael (1997). "An optimization-based econometric framework for the evaluation of monetary policy" (PDF). NBER Macroeconomics Annual. 12: 297–346. doi:10.1086/654340. JSTOR 3585236.
  23. ^ Woodford, Michael (2003). Interest and Prices: Foundations of a Theory of Monetary Policy. Princeton University Press. ISBN 0-691-01049-8.
  24. ^ Shoven, John B.; Whalley, John (1972). "A general equilibrium calculation of the effects of differential taxation of income from capital in the US" (PDF). Journal of Public Economics. 1 (3–4): 281–321. doi:10.1016/0047-2727(72)90009-6.
  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. ^ Brock, William; Hommes, Cars (1997). "A rational route to randomness". Econometrica. 65 (5): 1059–1095. doi:10.2307/2171879. JSTOR 2171879.

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