The large scale econometric models

1
The large scale econometric models
The first large-scale econometric model was built
by Professor Lawrence Klein in the 1950s. The
equations which formed the model represented a
synthetic or artificial economy.The modelwent
through various iterations and evolved into the
MIT-FR-Wharton model
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Uses of the model

• Using this model, it was possible to simulate the
  effects of proposed fiscal policy measures such
  as increased military spending and tax cuts on a
  wide array of aggregate (Y, I, C, S, ...) and
  disaggregate level variables (truck sales,
  employment in construction trades, cement
  prices).
• For example, The people who ran the model were
  asked to simulate the impact of the proposed
  Kennedy-Johnson tax cuts in the early 60s (took
  effect in 1964) on a broad array of economic
  variables.

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The Suits modela
The article is noteworthybecause is
educatedeconomists on the newapplications of
econometrics made possible by advances in
computer technology
Y C I G (1) C 20
0.7(Y - T) (2) I 2 .01Yt - 1
(3) T 0.2Y (4)

• The unkown variables are Y, C, I, and T
• The known variables are G and Yt - 1.

aDaniel Suits. Forecasting and Analysis with an
Econometric Model, American Economic Review,
March 1962 104-132.
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A simple national econometric model a
Consider a closed economy with government
GDP C I G
GDP is the dependent variable.Hence, to get
solution for GDP, we mustfirst specify and
estimate models for C, I, and G
a The following is based on A. Migliario. The
National Econometric Model A Laymans Guide,
Graceway Publishing, 1987.
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The aggregate level specifications
GDP t 1 C t 1 I t 1 G t 1
(2) C t 1 ?1 ?2DYt et
(3) I t 1 ?3 ?4it et
(4) G t 1 ?5 ?6Gt b
(5)

• Migliaro used OLS to estimated ?1, ?2, ?3, ?4,
  ?5, and ?6
• Having accomplished that, he substituted
  estimated equations (3), (4), and (5) back into
  (2) to get a forecasted value of G t 1.
• An example I t 1 11.567 - 0.419it

b Migliaro used the trend component to forecast
G.
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Extending (disaggregating) the model
Let C t 1 DUR t 1 NONDUR t 1
SERVICES t 1
Now let DUR t 1 AUTOS t 1 FURNITURE t
1 APPLIANCES t 1 . . .
Now letAUTOS t 1 Passenger Cars t 1
Vans t 1 Trucks t 1 . . .
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A trucks specification
Trucks t 1 ?1 ?2DYt ?3AGEt ?4PRICEt et

• As we increase the level of disaggregation, we
  increase the number of equations.That is, we
  could have equations for different classes of
  trucks--midsize, etc.
• It is the disaggregate level forecasts which are
  most valuable tobusiness decision-makers.
• Entities such as DRI-McGraw Hill and Chase
  econometrics sell disaggregate-level forecasts to
  a high-powered client base.

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A lot of equations

• The DRI-McGraw Hill Model has approximately 450
  equations.
• The FRB-MIT-Wharton model has 669 equations.
• The Chase Econometrics modle has 350 equations
• The Kent model has 44,400 equations.